phd data science online

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work.

Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.

These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students will be exposed to a wider range of fields than in more standard PhD programs while working with our interdisciplinary faculty. In particular, we are proud to offer a medical track for students eager to explore data science as applied to healthcare or to develop novel theoretical models stemming from medical questions.

In short, the CDS PhD Data Science program prepares students to become leaders in data science research and prepares them for outstanding careers in academia or industry. Successful candidates are guaranteed financial support in the form of tuition and a competitive stipend in the fall and spring semesters for up to five years.* We invite you to learn more through our webpage or by contacting  [email protected] .

*The Ph.D. program also offers students the opportunity to pursue their study and research with Data Science faculty based at NYU Shanghai. With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

10 Best Online PhD in Data Science Programs [2024 Guide]

If you have a passion for mining information from large amounts of data, you should consider exploring PhD Data Science online programs.

Editorial Listing ShortCode:

Furthering your education in this field can help take your career to the next level. By earning your PhD, you may increase not only your knowledge but also your salary.

Universities Offering Online Data Science Doctorate Degree Programs

Methodology: The following school list is in alphabetical order. To be included, a college or university must be regionally accredited and offer degree programs online or in a hybrid format. In addition, the schools offer online data science programs .

1. Capella University

Founded in 1993, private Capella University offers online doctorate, master’s, and bachelor’s degrees. The Minneapolis-based school’s 38,000 enrolled students represent 50 states and 61 countries. Doctoral students account for more than 27 percent of Capella University’s student body.

• DBA in Business Intelligence – Data Analytics

Capella University is accredited by the Higher Learning Commission.

2. Capitol Technology University

Capitol Technology University is a private university located near the nation’s capital in South Laurel, Maryland. Established in 1927, the university now offers undergraduate and master’s programs in business, computer science, cybrsecurity, and engineering.

Capitol Technology University is a military-friendly school founded by a Navy veteran. It holds the prestigious SC Media Award for Best Cybersecurity Higher Education Program. The school’s annual enrollment is approximately 850 students.

• PhD in Business Analytics and Data Science

Capitol Technology University  is accredited by the Middle States Commission on Higher Education.

3. Colorado Technical University

Colorado Technical University was founded in 1965. This private university offers undergraduate, graduate, and doctoral degrees in business management and technology.

The school has earned the U.S. News & World Report “Best for Veterans” designation, the Council of College and Military Educators (CCME) Institution Award, and recognition as a center of Academic Excellence in Information Assurance and Cyber Defense from the NSA and Department of Homeland Security.

Annual enrollment stands at around 26,000 students.

• Doctor of Computer Science – Big Data Analytics

Colorado Technical University  is accredited by the Higher Learning Commission.

4. Columbia University

New York City’s Columbia University is a private Ivy League research university founded in 1754. It stands today as the oldest university in New York City. Columbia operates four undergraduate schools and 15 graduate/professional schools.

Bachelor’s, master’s, and PhD programs covering business, medicine, liberal arts, technology, and political science are available. Student enrollment at Columbia stands at roughly 33,413.

• PhD in Data Science

Columbia  is accredited by the Middle States Commission on Higher Education.

5. Grand Canyon University

Grand Canyon University is a private Christian college based in Phoenix, Arizona. With a student enrollment of 70,000 students, it is considered to be the world’s largest Christian university.

Grand Canyon University offers bachelor’s, master’s, and doctoral degrees in business, education, health sciences, liberal arts, and nursing. The school offers a total of 200 academic programs throughout its nine colleges.

• DBA in Data Analytics

Grand Canyon University is accredited by the Higher Learning Commission.

6. Harrisburg University of Science and Technology

Founded in 2001, Harrisburg University of Science and Technology is a STEM-focused institution with campuses in Harrisburg and Philadelphia.

This private university offers bachelor’s degrees, master’s degrees, doctoral degrees, and certificate programs. The nearly 6,000 students enrolled study degree paths related to applied science and technology.

• PhD in Data Sciences

Harrisburg University of Science and Technology is accredited by the Middle States Commission on Higher Education.

7. Indiana University-Purdue University Indianapolis

Indiana University-Purdue University Indianapolis is a public research university offering more than 225 options for bachelor’s, master’s, and doctoral degrees across 18 different schools. The university’s campus is based in Indianapolis, Indiana.

The more than 30,000 students enrolled pursue degrees in majors like art and design, business, education, engineering, law, liberal arts, medicine, nursing, and social work.

• PhD in Data Science (on-campus)

Indiana University – Purdue University Indianapolis  is accredited by the Higher Learning Commission.

8. National University

National University is a network of nonprofit educational institutions that is headquartered in San Diego, California. It offers a range of bachelor’s degrees, master’s degrees, doctoral degrees, and certificates in business, education, marriage and family therapy, psychology, and technology.

NU has over 30,000 students enrolled and more than 220,000 alumni from around the world.

National University is accredited by the Western Association of Schools and Colleges.

9. Stevens Institute of Technology

Located in Hoboken, New Jersey, Stevens Institute of Technology is a private research institution with an enrollment of approximately 6,125 students. Founded in 1870, the school has been named among the “Best Value Colleges” by the Princeton Review.

Additionally, the Princeton Review ranks Stevens Institute of Technology among its “Top 15 for Internships.” The school’s undergraduate and graduate students represent 47 states and 60 countries. Students can pursue bachelor’s, master’s, doctoral, and certificate programs.

Stevens Institute of Technology is accredited by the Middle States Commission on Higher Education.

10. University of Central Florida

Located along Orlando’s Space Coast, the University of Central Florida is a public research university with a student enrollment of approximately 69,525. It offers bachelor’s, master’s, and doctoral programs.

Students can pursue degrees in arts and humanities, business, engineering, computer science, health science, medicine, and nursing. The University of Central Florida has been ranked as a “Best Southeastern College” by the Princeton Review.

• PhD in Big Data Analytics

The  University of Central Florida  is accredited by the Southern Association of Colleges and Schools Commission on Colleges.

Online PhD in Data Science Programs

Data science is exactly what it sounds like – the study of data. Data scientists look at sets of data and notice patterns that emerge. They identify key information that data presents which may not seem readily apparent at first.

If you are someone that notices the small details while also keeping an eye on the bigger picture, a career in data science may be right for you. If you find trends and patterns in large amounts of data, you may be well-suited for this field.

What kind of job can you expect to have as a data scientist? In the last few years, Glassdoor has continuously ranked data scientist as one of the best jobs to have in the United States. The options for specific jobs are numerous and varied.

For example, one data scientist may work as a statistician and interpret statistical information for the U.S. Department of Agriculture. Another data scientist may be a business intelligence developer for Discover, creating strategies for businesses to make more informed decisions about their company.

Data Science Pros and Cons

As with any financial and length time investment, you should consider both the pros and the cons of earning your PhD in an online data science program.

All salary data in this table was provided by the Bureau of Labor Statistics.

Choosing to pursue an online PhD in a data science program is decision that must be taken into careful consideration, but there are many benefits to completing a program.

Data Science Curriculum & Courses

Curriculum for data science programs is heavily focused on analysis and research. Examples of courses offered by universities like Dakota State University and the University of North Texas are listed below.

• Information Systems – This course is designed to help students learn about the role information systems have for businesses and other organizations.
• Applied Statistics – This class teaches how to use statistical software to study data samples and make inferences based on the data presented.
• Project and Change Management – This class is designed to help students learn the underlying principles for managing information systems and how to utilize software for project management.
• Technology for Mobile Devices – Students in this course study the process of developing apps for mobile devices like smartphones and tablets.
• Advanced Network Technology and Management – This class helps students learn how to work with a model network environment, including how to find solutions for problems with the network.
• Seminar in Research and Research Methodology – Students in this seminar are asked to develop a research proposal and participate in a research study.
• Knowledge Management Tools and Technologies – This course introduces students to a variety of technologies including those associated with knowledge management and IT infrastructure.
• Seminar in Communication and Use of Information – This class explores the roles communication plays at various levels in society.
• Readings in Information Science – Students in this class study texts which emphasize methodological and theoretical issues.
• Medical Geography – In this course, students study the correlation between location and health care and work on their own projects.

Exploring the curriculum offered by different universities can help you determine which online PhD program is best suited for your interests and your needs.

Data Science PhD Admissions

Before applying for a PhD program, you will want to ensure that you have all the application materials on hand, including the commonly required materials listed below.

• Reference letters – You should request these documents well before your application deadline as mentors may not be able to honor a last-minute request due to time constraints.
• All transcripts – These grades will include both undergraduate and graduate level courses.
• Letter of intent – Be prepared to explain in writing why you want to enroll in the program and what you plan to do after its completion.
• Application fee – Fees to cover administrative costs of reviewing your application can add up, so make sure to budget for the costs of each one.
• Resume – Schools want to know your background in not just education but in the job market as well.
• Specific program application – Your prospective school will most likely have its own unique application on its official website.

Save yourself the stress of anxiously waiting to receive documents from an institution or mentor in time and compile them well ahead of the due date.

Data Science PhD Careers & Salaries

According to the U.S. Bureau of Labor Statistics , computer and information research scientists earned a median of $131,490 a year. Data scientists as a group earn increasingly high salaries in various industries including research laboratories, government departments, and a variety of companies focused on technology.

Some of the top companies that utilize data scientists are IBM, Amazon, Microsoft, Facebook, Oracle, Google, and Apple. These multi-billion dollar companies are consistently hiring data scientists to interpret the large amounts of data, or “big data,” that is collected via their services.

Data scientists can expect to work in roles where job duties include designing data models, organizing data from multiple sources, and identifying trends in data.

Data scientists use a comprehensive process for gathering and analyzing information including asking questions, acquiring data, storing data, using models to interpret it, and presenting their findings to stakeholders in the community.

According to the Bureau of Labor Statistics, some careers in the data science field include:

Whatever the job title, data scientists continually earn a significant amount more than employees in other fields.

Data Science Accreditation

Before clicking the “submit” button on your application to a PhD program, you will want to ensure that the university you are applying to is accredited, meaning it is recognized as a legitimate program that offers quality coursework and research opportunities.

If you decide to apply to a program related to computer technology or engineering, the Accreditation Board for Engineering and Technology (ABET) determine which schools offer suitable coursework and requirements for these fields. Also be sure that your prospective university is regionally accredited, the gold-standard for accreditation in the United States.

Search on your prospective schools’ website for information regarding their accreditation status. You will want to ensure that the schools you apply to are regionally accredited so you can get the most out of your PhD experience and your credits will be more likely to transfer should you switch schools while studying.

Data Science Professional Organizations

Joining a professional organization can help to advance your career by connecting you with other individuals who work in the same field.

Professional organizations offer a multitude of benefits, including networking opportunities (which may help to connect you with future employers), and they can also provide inspiration for completing your PhD program, decreasing feelings of isolation that can accompany students.

• Association for Information Science and Technology – This organization states its role “advances the information sciences and similar applications of information technology by helping members build their skills and [develop] their careers” via several different ways, including training and education.
• Association of Information Technology Professionals – This agency gives members advice on how to pursue certain career paths while also providing discounts on certifications and resources for professional development.
• International Association for Social Science Information Services and Technology – IASSIST has 300 members from countries around the world. They offer resources for professionals from sectors such as non-profits, academia, and government.

While some organizations may have a yearly membership fee, the potential gains for job opportunities and professional development through these groups can easily offset those costs.

Financial Aid

Across the nation, the average cost of obtaining a PhD online is between $4,000 and $20,000.  As a student in a PhD program, you can expect to have costs from tuition, books, personal supplies, transportation, etc. Without the time or energy for a full-time or often, even part-time job, you should explore all financial aid options available.

Financial aid for PhD students can come in the form of loans, scholarships, and grants. Grants and scholarships typically do not have to be paid back, but loans are borrowed money which may accrue interest and should be a last resort for students.

Some specific scholarships and grants are designed with scientists, including data scientists, in mind. For example, the National Science Foundation Graduate Research Fellowship is designed to support students who are pursuing research-based doctoral degrees.

Previous recipients include Nobel Prize winners, a U.S. Secretary of Energy, and the founder of Google.

Another common source of money comes from taking on teaching assistant positions within your university or becoming an assistant lecturer. Both positions are great for gaining experience teaching in your academic department while generating income to offset the costs incurred from your years of study.

How long does it take to get a PhD in data science?

It takes an average of 71 credits to complete a PhD in data science. On top of this, students may also have responsibilities to research and/or teach, which can make the process take even longer.

It is not unusual for some PhD programs to take anywhere from four to five years to complete.

Is a PhD in data science worth it?

Whether or not a PhD in data science is “worth it” depends on a number of factors. Do you have the time available for next few years (possibly longer) to invest in this opportunity? Are you motivated enough to complete coursework while also on a shoestring budget?

Search for employment positions you are interested in and take a look at the education requirements employers are requesting. These factors may effect your decision in potentially pursuing an online masters in data science instead.

Can I do a PhD in data science?

Whether or not you complete a PhD in data science depends on your ability to stay focused and motivated. PhD programs are notoriously intensive, and they are not for everyone.

You should have a better reason for applying to a program than simply not knowing what to do in today’s job market.

Getting Your PhD in Data Science Online

Obtaining your doctoral degree in data science is not an easy task, but it is also not an impossible one. If you are serious about pursuing your PhD, talk to experts in the field. The admissions departments at prospective universities can help put you in touch with recruiters who can give you more information about the program.

Joining a professional organization can help you connect with individuals who are working in the field, many of whom will have obtained their higher education degree. With careful planning and the right information to make informed career choices, you can further your education and your sense of accomplishment.

DiscoverDataScience.org

PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

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Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

• Related Programs

Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

Information Request Form

Ph.d. specialization committee.

• View All People
• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• The Fu Foundation School of Engineering and Applied Science Vice Dean of Research
• Tang Family Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• Data Science Institute Interim Director
• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Academic Affairs

PhD in Data Science

Students conduct research on cutting edge problems alongside preeminent faculty at UChicago and explore the emerging field of Data Science. As an emerging discipline, Data Science addresses foundational problems across the entire data life cycle. Tackling issues of inequity, climate change, and sustainability will require cutting edge research in artificial intelligence and data usage combined with innovative educational programs to train students in the concepts of information systems. Students of Data Science will not only immerse themselves in a rapidly evolving field; they will help redefine it altogether.

Research Excellence:

As a PhD student in Data Science, you will learn from faculty who have developed research programs that span a wide variety of data science and AI topics, from theory to applications, with a focus on making a societal impact.

Research Topics:

• Artificial Intelligence
• Data, AI, and Society
• Data Systems
• Human-Centered Data Science
• Machine Learning and Statistics
• Use-Inspired Data Science

For more information, including a link to the application, see the Committee on Data Science website .

Top 10 Best Data Science PhD Online Programs

According to IBM, poor data quality costs the US economy around $3 trillion annually. Globally, that figure could be as high as $14 trillion. As a result, the demand for data scientists has soared in recent years, making a PhD in data science a highly sought-after degree.

Data scientists are highly skilled professionals who use cutting-edge technology to make sense of large, often unstructured, datasets.  They are necessary for almost every industry to help provide analysis for a data-driven business environment. Data scientists use proven strategies such as clustering, behavioral analytics, and data mining to analyze and organize data to make informed decisions.

Becoming a data scientist requires a higher education to gain the necessary skills for this field. One of the best ways of doing so is earning a degree online. Better still, earning a   data science PhD online will give you the most comprehensive set of tools required to advance your career.

Related Articles:

• Duties of a Typical Data Scientist
• What is a Data Science Ph.D Program Like?
• Does a Data Science Ph.D Significantly Increase Earning Power?
• How is a Data Science Degree Different from a Cybersecurity Degree?
• Tips for Finding Success in a Data Science Ph.D. Program

Methodology for Ranking the Best Online Data Sciene PhDs

A PhD in data science online is a great choice for data scientists who may not want to relocate or take time away from work. We looked at over 20 different doctoral programs in data science.  We based our search on College Navigator and DataScience Community. This determined our top 10 for our data science PhD program ranking . We included data analytics doctoral programs in our search.  The best programs had:

• limited residency requirements
• a highly interactive curriculum
• a variety of courses in data science
• support from expert-level faculty

We then based our data science PhD programs ranking on the following considerations and assigned points accordingly:

Student-to-Faculty Ratio

• 15:1 or less = 5 Points
• 16:1 to 20:1 = 3 Points
• Greater than 20:1 = 1 Point

Average Graduate Tuition

• Less than $10,000 per year = 5 Points
• $10,000 to $15,000 per year = 4 Points
• $15,001 to $20,000 per year = 3 Points
• $20,001 to $25,000 per year = 2 Points
• Greater than $25,000 per year = 1 Point

Cost is a factor we considered when developing our list. However, many PhD-level data science programs provide students with assistance to cover the cost of their tuition. We reviewed each school’s average graduate tuition rates from College Navigator, a website run by the National Center for Education Statistics. It provides information on institutions of higher education around the country.

Tuition rates can vary from program to program. We looked specifically at tuition rates for graduate school. Always consult the university to find out specifics related to their online PhD data science program. Check if they offer any financial aid or scholarships to reduce your overall cost.

Below are the best online doctorate degrees in data science based on our criteria. Our list highlights the best of the best in quality  online doctorate in data science  programs.

Data Science Doctorate Online Degree Programs Ranking

#1. capitol technology university – laurel maryland, doctor of philosophy (phd) in business analytics and data science.

Program Website

• Student to Faculty Ratio: 9:1
• Average Graduate Tuition: $11,340/year
• Total Points: 9

Capitol Technology University offers our top data science online PhD. You will learn strategies and skills that prepare you for senior positions in the public or private sector. The low residency format allows you to balance your education with your established career. Most courses are offered online and cover areas like:

• analytics and decision analysis
• business intelligence
• machine learning
• data mining
• applied statistics and visualization for analytics
• Big Data warehousing and analytics systems

You must visit campus to complete your qualifying exam and work one-on-one and in groups to prepare for your dissertation. Core PhD in data analytics online courses are taught by working professionals who stay current on emerging developments in the field. You can feel confident you’re gaining professional skills that are in demand by top employers.

Program Pros:

• Most coursework is completed online
• Small class sizes
• Low average graduate tuition rate
• Tuition discounts for military

Program Cons:

• Abundance of program and institutional fees
• On-campus residency is required for some classes

#2. Dakota State University – Madison, South Dakota

Doctor of science in information systems.

• Student to Faculty Ratio: 18:1
• Average Graduate Tuition: $5,999/year in-state; $11,199/year out-of-state
• Total Points: 8 (points were determined using in-state tuition)

DSU features one of the most flexible data science Doctor of Philosophy programs in this ranking. You can take courses online or on-campus and attend full or part-time. You can choose from three different areas of specialization as well:

• data management
• health care information systems
• information systems cybersecurity
• network administration and security

You can also choose to complete a general specialization. This requires you to take courses in application development, network administration and security, and data management. Core courses for all specializations cover relevant areas such as information systems, applied statistics, and web application development. You’ll also take classes in research, enterprise modeling, and data management, to name a few.

As a full-time student with a master’s degree, you should be able to complete this program in just three years. However, it can take as much as seven years to finish. When you graduate, you will have the tools needed to solve complex data science problems in a variety of settings, including academics, business and finance, and government.

• Multiple options for completing the program, including online or on-campus
• Multiple specialization options
• Just three years needed to complete the degree as a full-time master’s-level student
• Very low average in-state tuition
• Long list of knowledge requirements for admission
• High out-of-state tuition compared to in-state tuition

#3. Indiana Wesleyan University – Marion, Indiana

Online doctor of business administration – data analytics specialization.

• Student to Faculty Ratio: 17:1
• Average Graduate Tuition: $9,731/year
• Total Points: 8

This online program follows a cohort format, meaning you progress through the curriculum with the same classmates in each class. This format encourages the development of collegiality between you and your classmates and also enhances your working relationship with your professors.

The curriculum is problem-based. You’ll learn how to collect, analyze, organize, and interpret data. Many opportunities exist to enhance your writing and communication skills as well. Some of the courses you’ll take include the following:

• Data Analytics and Research
• Academic Writing for Business
• Foundations of Doctoral Research
• Leadership and Global-Based Teams

You must also complete a comprehensive examination, a scholar-practitioner paper, and an applied doctoral project.

The comprehensive examination must be completed prior to the applied doctoral project, and involves a critique of relevant research and a demonstration of your subject matter expertise. The purpose of the paper is to build the required research and writing skills to produce a piece worthy of submitting to industry journals. The doctoral project takes the place of a traditional dissertation. The project focuses on developing an evidence-based consulting report that directly addresses a real-world problem in the business world.

• Extremely affordable
• The applied doctoral project gives you real-world experience prior to graduating
• Data analytics focus is highly sought-after in business
• An on-campus residency is required
• The program includes a religious focus, which might conflict with your personal beliefs

#4. National University – San Diego, California

Doctor of philosophy in data science online.

• Student to Faculty Ratio: 16:1
• Average Graduate Tuition: $15,912/year
• Total Points: 6

National University features a research-oriented online PhD in data science that is aligned with industry needs. The unique one-on-one learning model provides unmatched personal attention – you’re paired with a professor for each course to give you the guidance you need to succeed.

New courses begin weekly and last eight weeks (except dissertation courses, which are 12 weeks long). This allows you to fit your education into your busy personal and professional schedule. This program includes courses that cover areas like big data development, data preparation methods, and data curation. You’ll also take courses such as:

• multivariate analysis
• data visualization and communication
• exploratory data analysis
• databases and business intelligence
• inferential and predictive analytics

You can complete this program in as little as three-and-a-half years. Once you graduate, you can pursue any number of data science-related careers. This includes database architecture, database administration, and data warehousing, among many others.

• One-on-one learning model provides the utmost support
• Program applications are accepted year-round
• Monthly program start dates
• High average graduate tuition rates compared to other programs on this list

#5. The University of Rhode Island – Kingston, Rhode Island

Online phd in computer science.

• Average Graduate Tuition: $15,402/year in-state; $29,904/year out-of-state
• Total Points: 6 (points were determined using in-state tuition)

The computer science PhD program at the University of Rhode Island is a flexible program available to you as a full- or part-time student. Most, but not all, computer science courses are available online, making it a good choice if you’re a professional in the Kingston area looking for a low-residency program.

Course offerings include options like Programming for Data Science, Data Structures and Abstractions, and Database Management Systems. All told, you must complete 52 or more credits of coursework, 18 dissertation research credits, and at least two credits of seminars.

You can earn your degree in four years, provided you study full-time. Admission is for the fall semester or spring semester; bear that in mind when applying to ensure you meet application deadlines for the next academic term.

• Full-time and part-time options are available
• A statistics PhD option is available
• Some on-campus coursework is required
• Expensive compared to other options on this list

#6. University of North Texas – Denton, Texas

Phd in information science-data science.

• Student to Faculty Ratio: 23:1
• Average Graduate Tuition: $7,160/year in-state; $14,720/year out-of-state

UNT’s Department of Information Science offers a PhD with interdisciplinary training. You will gain experience in doctoral research in data science and related areas that enhance your critical thinking skills, disciplinary thinking, and research capabilities. The program offers numerous concentration areas, allowing you to specialize your degree according to your interests. The available specializations are as follows:

• Consumer Behavior and Experience Management
• Cybersecurity
• Data Science
• Geospatial Information Science
• Health Informatics
• Knowledge Management
• Linguistics

Though most coursework is online, you must come to campus for a residential experience that gives you a high level of interaction with your professors. The faculty in this program are all highly experienced and provide real-world guidance to support your theoretical learning.

UNT is committed to keeping costs affordable. The university offers various financial aid opportunities, including grants, competitive scholarships, fellowships, and assistantship opportunities.

• Interdisciplinary curriculum
• Low in-state tuition
• Extremely high student-to-faculty ratio

#7. Johns Hopkins University – Baltimore, Maryland

Drph in health policy and management-public health informatics.

Program Websi te

• Student to Faculty Ratio: 6:1
• Average Graduate Tuition: $62,840/year

The Bloomberg School of Public Health at Johns Hopkins University features an online Doctor of Public Health with a concentration in health policy and management. Within the concentration is a track in public health informatics. It is delivered in a flexible part-time format. This data science-related doctorate degree requires you to complete a series of high-level courses in addition to a practicum experience during which you apply what you’ve learned in your courses in a public health informatics setting. A dissertation is also required.

Some of the courses you’ll take include the following:

• Statistical Reasoning in Public Health
• Advanced Data Analysis Workshop
• Qualitative Research Theory and Methods
• Introduction to Qualitative Data Analysis for Public Health
• Mixed Methods for Research in Public Health

Once you graduate, you can seek employment in mid- to senior-level positions in government agencies, non-profit organizations, or international public health organizations.

• Johns Hopkins is a prestigious university
• Healthcare-focused data science degree has high value
• The program can be completed entirely online
• Extremely expensive
• A Master of Public Health is required to apply

#8. Grand Canyon University – Phoenix, Arizona

Dba degree in data analytics – quantitative.

• Student to Faculty Ratio: 20:1
• Average Graduate Tuition: $10,138/year
• Total Points: 5

Grand Canyon University features an online, cost-effective DBA in data analytics that focuses on evidence-based theories and practices. You will learn to use statistical tools to clean numerical data, determine data’s reliability, and conduct data analyses. You’ll gain these skills in courses like:

• Analytic Foundations for Business Leaders
• Approaches to Research Design and Data Analysis
• Emerging Issues in Financial Management
• Foundations of Research Design
• Enterprise Data Complexity

You will also complete a dissertation. Unlike traditional PhD programs, this one requires you to begin the dissertation from the outset. You’ll work closely with faculty members to conduct original research and utilize what you learn to inform a detailed data analytics dissertation.

This program allows you to complete your coursework where and when it’s convenient.  However, you must come to campus for two or three five-day residencies in Phoenix. These on-campus experiences allow you to network with other students and participate in mentorship opportunities and professional development with faculty members.

• Emphasis on mentorship from faculty members
• Quantitative research focus has wide applicability for careers
• Several on-campus residencies are required
• Program is designed specifically for business executives, which may preclude you from applying

#9. Colorado Technical University – Colorado Springs, Colorado

Doctor of computer science – big data analytics.

• Student to Faculty Ratio: 31:1
• Average Graduate Tuition: $14,104/year

The online doctorate in big data analytics from Colorado Technical University helps you learn the latest industry tools to produce meaningful insights from unstructured data. The data science curriculum is primarily delivered online, so you can build your course schedule around other obligations. Multiple start dates are offered throughout the year as well.

Another advantage of this program is that courses begin every five weeks, so that you can begin at your convenience. Despite requiring 100 credits to graduate, you can complete this degree in about three years of full-time studies. During that time, you’ll take courses focusing on:

• Advanced Topics in Database Systems
• Advanced Quantitative Analysis
• Artificial Intelligence in Real World Problem Solving
• Business Intelligence
• Futuring and Innovation

The big data analytics specialty also requires completing a research proposal and a dissertation. You’ll work on each project extensively throughout the program in preparation for defending your dissertation before graduation.

• Training in big data is in high demand in business and industry
• Fully online program
• Poor student-to-faculty ratio
• The program does not lead to licensure or certification

#10. University of the Southwest – Hobbs, New Mexico

Dba – data analytics.

• Student to Faculty Ratio: 19:1
• Average Graduate Tuition: $23,364/year

The University of the Southwest is a private, non-profit Christian university offering accredited degrees to online students. USW features an online doctorate in data analytics that prepares you to solve business challenges using a quantitative research approach. This data science program follows a mentorship model. You will work closely with faculty members to achieve your professional goals while benefitting from their real-world expertise.

This program’s coursework is delivered 100% online and there are no required residencies (though you have the option of participating in an on-campus or online residency if you choose). The curriculum focuses on foundational business data analytics skills. This includes data visualization, predictive modeling, and forecasting, to name a few.

A free laptop is included with the first course. There is no additional cost for textbooks or course materials – these components are included with tuition. You also benefit from a transfer policy allowing you to use up to 12 credits earned in a program from another regionally accredited university.

• The program is completely online
• Data analytics skills are widely applicable in various businesses and industries
• Free laptop, textbooks, and other course materials
• Relatively expensive tuition
• At least three years of data analytics experience is required for admission

Frequently Asked Questions

Is a phd in data science worth it.

A PhD in data science is the most advanced level of data science education available and can take years to complete. By the end, you’ll have a wide span of knowledge and understanding of the data science field. You will also have the education you’ll need to get top positions in research and academia.

A PhD is typically a research-oriented program. It can take some time before you hit a financial payoff after earning your PhD. High-paying jobs, such as data scientist jobs requiring a PhD, also usually require significant experience. You’ll need to be patient!

The decision to earn a PhD is not to be taken lightly.  There are many benefits (as well as some downsides) of getting a PhD in data science.

Advantages:

• Research is a key piece of earning a PhD.  You can conduct research during your dissertation that could advance the industry or solve a long-standing issue.  Very exciting!
• There are several specializations in data science.  Some master’s degree programs even offer concentration areas that PhD programs can build upon.  Areas like biomedical data science, health informatics, and business analytics allow you to build upon your previous education and make meaningful contributions to the field.
• University partnerships provide you with the opportunity to work with real data sets. You can collaborate with others on research efforts, gaining new perspectives and insights.
• Some programs accept students with only a bachelor’s degree.  Popular bachelor’s degree majors include analytics and computer science.  You can earn a master’s while pursuing a PhD!
• Graduates with a PhD may see an increase in job opportunities in areas like academia and research.

Potential Disadvantages:

• A PhD takes a significant amount of time to complete.  If you opt to complete a full-time program, you could be missing out on three to five years of financial earnings and real-world skill development.
• You’ll need to find a way to fund your PhD.  Some students finance their degree on their own with the help of financial aid.  Others apply to programs that fully fund students and offer stipends.
• Technology changes rapidly.  Your time spent completing research could take away from gaining on-the-job industry data analysis knowledge and skills.
• Make certain your desired position requires a PhD.  Many industries are perfectly happy with a bachelor’s degree or master’s in data science. It could also be a related area like computer science, as long as the applicant has relevant work experience.

If you decide to pursue a PhD, your hard work and commitment to your doctoral program and the field of data science will eventually pay off. You’ll be eligible for some of the field’s highest-paying and most prestigious jobs.

What jobs are available to a data scientist with a PhD?

Whether you complete your  data science PhD online  or on-campus, the job prospects are the same. A PhD can open the door to various exciting data science careers. Data science PhD graduates are primarily in demand in academic and industrial settings.

PhD data science programs are the primary avenue for getting a data-related position in academia. These positions can be fiercely competitive, though. Data science grads who can stay up to date on the latest trends and technologies are highly sought after. Business intelligence and machine learning are two specialized areas in demand by industry executives.

How long does it take to get a PhD in Data Science?

A data science doctoral program is a significant investment of time and money. You can expect to spend three to four years working on your PhD in data science. The specific amount of time it takes to complete a doctoral program in data science is determined by the degree requirements and your schedule.

Hybrid and online data science programs usually provide extra flexibility if you have a non-traditional schedule (e.g., you work a regular 8-5 job). Accelerated doctoral program options may be a great option for completing your doctorate in data science, saving you time and money along the way.

What are the requirements for a PhD in data science degree program?

Admissions requirements.

Admission to a PhD program is extremely competitive.  Because of the intense nature of the program, an extremely limited number of doctoral degree applicants are accepted each year.  You must submit a strong application packet that makes you stand out from the competition.

You should have a minimum of a master’s degree with a cumulative GPA of at least 3.0 on a 4.0 scale. This does not need to be from a data science masters program , but of course, it will help. Some online data science programs may also require you to submit letters of recommendation, a statement of purpose, and GRE scores. You might also be required to provide a curriculum vitae or a resume. In some instances, an interview (either in person or web-based video) may also be required.

A doctorate degree is typically comprised of several different parts. These include the following:

Coursework:

A PhD in data science requires coursework that helps build a strong educational foundation. Core courses may cover areas that include:

• statistical methods
• knowledge management
• machine learning.

Comprehensive Exam:

Before beginning the doctoral dissertation process, you may need to complete a comprehensive examination. This exam allows faculty to assess your level of knowledge and understanding of the field before beginning your dissertation.

Dissertation Proposal

The dissertation proposal is a comprehensive statement describing the research you want to accomplish. As part of this process, you will:

• state your research question
• discuss the general scope of your project
• discuss how you’ll do it
• explain how it contributes value to the field

Dissertation Research:

Dissertation research is the final piece of doctoral study. You complete the dissertation research project you designed and summarize your research and findings in your dissertation paper.

Field Experience:

Some data science graduate programs require you to complete a field experience as part of the curriculum. You may work with an outside organization or a research center on-campus to create original research or solve a real-world data science problem.

Special Projects:

Many data science doctoral programs offer you the opportunity to participate in special projects. These unique professional development opportunities allow you to develop areas of specialized knowledge that can help them build your resume.

Will I need any licenses or certificates to get a job in data science?

There’s no standard industry license or certification for data science professionals. Data professionals who want to stand out from the competition have a variety of certification options available. These specialized training programs allow you to develop technical skills that recruiters and hiring managers look for. Some popular certifications include:

Cloudera Data Platform (CDP) Generalist Certification

This certification validates proficiency with the Cloudera platform. Certification applies to multiple roles including, data analysts, developers, and system architects. The exam takes 90 minutes to complete and consists of 60 questions.

Data Science Council of America (DASCA) Principal Data Scientist

The Data Science Council of America is an industry leader for professional development opportunities in data science. It offers the Principal Data Science (PDS) certification, which is designed for professionals with at least ten years of experience working with big data. The primary audience is “seasoned and high-achiever data science thought and practice leaders.” The certificate is awarded after meeting eligibility criteria and passing a self-paced online exam. At a minimum, completing the certification process will take several months. You have a year to finish, though.

Microsoft Certified Azure AI Fundamentals

If you want a certification to validate your knowledge and skills in machine learning and artificial intelligence related to Azure services, this might be the certificate for you. This is a fundamental certification, so it’s great if you’re new to AI and need to demonstrate your proficiency to your employer. The certification test takes 45 minutes to complete.

Doctor of Philosophy in Data Science

Developing future pioneers in data science

The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics. 

Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.

LEARNING OUTCOMES

Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:

• Understand data as a generic concept, and how data encodes and captures information
• Be fluent in modern data engineering techniques, and work with complex and large data sets
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.

A Week in the Life: First-Year Ph.D. Student

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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Graduates of our program—the first of its kind in both Indiana and the Big Ten—develop the skills to make pioneering research contributions to data science theory and practice in academic and the industrial sectors.

Our students acquire the skills to develop inventive and creative solutions to data research problems—solutions that demonstrate a high degree of intellectual merit and the potential for broader impact. The Ph.D. curriculum also prepares students to make research contributions that advance the theory and practice of data science.

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• Novel techniques for Natural Language Processing and Text Analytics.
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• Intelligent conversational agents and models of Human-AI collaboration.
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Home / Data Science Programs / PhD in Data Science

Data Science PhD Programs

If you’re passionate about big data and interested in an advanced degree, you may be wondering which degree is right for you. Should you go with a Master of Science (M.S.) or a PhD in data science?

Our guide to getting a PhD in data science is here to help. Here, we’ll break down potential pros and cons of choosing either option, related job opportunities, dissertation topics, courses, costs and more.

SPONSORED SCHOOLS

Syracuse university, master of science in applied data science.

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

• Complete in as little as 18 months
• No GRE scores required to apply

Southern Methodist University

Master of science in data science.

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

• No GRE required.
• Complete in as little as 20 months.

University of California, Berkeley

Master of information and data science.

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

• Complete in as few as 12 months
• No GRE required

info SPONSORED

Just want the schools? Skip ahead to our  complete list of data-related PhD programs .

Why Earn a PhD in Data Science?

A PhD in Data Science is a research degree designed to equip you with knowledge of statistics, programming, data analysis and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.).

The keyword here is  research . Throughout the course of your studies, you’ll likely:

• Conduct your own experiments in a specific field.
• Focus on theory—both pure and applied—to discover why certain methodologies are used.
• Examine tools and technologies to determine how they’re built.

PhD Benefits vs. Downsides

There are a number of benefits and downsides to earning a PhD in data science. Let’s explore some of them below.

Benefits of a PhD in Data Science

In a PhD in data science program, you may have the opportunity to:

• Research an area in data science that may potentially change the industry, have unexpected applications or help solve a long-standing problem.
• Collaborate with academic advisors in data science institutes and centers.
• Become a critical thinker—knowing when, where and why to apply theoretical concepts.
• Specialize in an upcoming field (e.g.  biomedical informatics ).
• Gain access to real-world data sets through university partnerships.
• Work with cutting-edge technologies and systems.
• Automatically earn a master’s degree on your way to completing a PhD.
• Qualify for high-level executive or leadership positions.

Downsides of a PhD in Data Science

On the other hand, some PhDs in data science programs may:

• Take four to five years on a full-time schedule to complete. These are years you could be earning money and learning real-world skills.
• Be expensive if you don’t find or have a way to fund it.
• Entail many solitary hours spent reading and writing
• Not give you “on-the-job” knowledge of corporate problems and demands.

Is a PhD in Data Science Worth It?

A PhD in data science may open the door to a number of career opportunities which align with your personal interests. These include, but aren’t limited to:

• Data scientist.   Data scientists  leverage large amounts of technical information to observe repeatable patterns which organizations can strategically leverage.
• Applications architect.  When you work as an applications architect, your main goal is to design key business applications.
• Infrastructure architect.  Unlike an applications architect, infrastructure architects monitor the functionality of business systems to support new technological developments.
• Data engineer.   Data engineers  perform operations on large amounts of data at once for business purposes, while also building pipelines for data connectivity at the organizational level.
• Statisticians :  Statisticians  analyze and interpret data to identify recurring trends and data relationships which can be used to help inform key business decisions.

At the end of a day, whether a data science PhD is worth it will be entirely dependent upon your personal interests and career goals.

Do You Need a PhD to Land a Job?

In most cases, you don’t need a PhD in data science to land a job. Most  computer and information research-related careers  require a master’s degree, such as an  online master’s in data science .

As you begin your search, pay attention to prospective employers and qualifications for your desired position:

• Companies and labs that specialize in data science—and tech players like  Amazon  and  Facebook  — may have a reason for specifying a PhD in the education requirements.
• Other industries may be happy with a B.S. or M.S. degree and relevant work experience.

Careers for Data Science PhD Holders

People who hold a PhD in data science typically find careers in academia, industry and university research labs,  government  and tech companies. These places are most likely seeking job candidates who can:

• Research and develop new methodologies.
• Build core products, tools and technologies that are based on data science (e.g.  machine learning  or  artificial intelligence  algorithms for Google or the next generation of  big data management systems ).
• Reinvent existing methods and tools for specific purposes.
• Translate research findings and adopt theory to practice (e.g. evaluating the latest discoveries and finding ways to implement them in the corporate world).
• Design research projects for teams of statisticians and data scientists.

Sample job titles include:

• Director of Research
• Senior Data Scientist/Analyst
• Data/Analytics Manager
• Data Science Consultant
• Laboratory Researcher
• Strategic Innovation Manager
• Tenured Professor of Data Science
• Chief Data Officer (CDO)

PhD in Data Science Curriculum

Typical Program Structure Data science PhDs are similar to most doctoral programs. That means you’ll typically have to:

• Complete at least two years of full-time coursework.
• Pass a comprehensive exam—comprising oral and written portions—that shows you have mastered the subject matter.
• Submit a dissertation proposal and have it approved.
• Devote 2-3 years to conducting independent research and writing your dissertation. You may be teaching undergraduate classes at the same time.
• Defend your work in a “dissertation defense”—usually an oral presentation to academics and the public.

During these years, you’ll likely engage in professional activities that may help improve your career prospects. Such opportunities include attending and speaking at conferences, applying for summer fellowships, consulting, paid part-time research and more.

Dissertation

PhD students are expected to make a creative contribution to the field of data science—that means you’re encouraged not to go over old ground or rehash what’s already out there. Your contribution will be summed up in your dissertation, which is a written record of your original research.

Some students go into a PhD program already knowing what they want to research. Others use the first couple of years to explore the field and settle on a dissertation topic. Your advisor may be your closest ally in this process.

Data Science vs. Business Analytics vs. Specialties

Doctoral programs in data science may also fall under the related disciplines such as statistics,  computational sciences  and informatics. It is important to evaluate each program’s curriculum. Will the foundation courses and electives prepare you for the research area that you want to explore?

A related degree you may consider is a PhD in Business Analytics (or Decision/Management Sciences). These degree programs are typically administered through a university’s School of Business, which means the curriculum includes corporate topics like management science,  marketing , customer analytics, supply chains, etc.

Interested in a particular subset of data science? Some universities offer specialty PhD programs. Biostatistics and biomedical/health informatics are two examples, but you’ll also find a number of doctoral programs in machine learning (usually run by the Department of Computer Science) and sub-specialties in fields like artificial intelligence and data mining.

Considerations When Choosing a PhD Program

Typical Admissions Requirements PhD candidates typically submit an application form and pay a fee. Universities often look for applicants who have:

• A  Bachelor of Science (BS) in computer science , statistics or a relevant discipline (e.g. engineering) and a similar master’s degree with an official transcript from an accredited institution
• A GPA of 3.0 or higher on a 4.0 scale
• GRE test scores
• TOEFL or IELTS for applicants whose native language is not English
• Letters of recommendation
• Statement of purpose/intent
• Résumé or CV

If you don’t already have certain skills (e.g. stats, calculus, computer programming, etc.), the university may ask you to complete prerequisite courses.

Programs for PhD in Data Science – Online vs. On-Campus Online programs may require you to attend a few campus events (e.g. symposiums), but allow you to complete coursework and conduct research in your own hometown.

While online learning can be a convenient way of obtaining your PhD from the comfort of home, there are a few important factors to consider.

• Are you  extremely  passionate about an area of research?
• Do you mind committing to 4-5 years of study?
• Does your university have funding sources (private and government) for data science research?
• Will you have access to exciting data resources, labs and industry partners?
• Do you know how you’re going to pay for the program?

How Much Does a PhD Cost?

As you research PhD in data science programs, you’ll probably find information on relevant fellowships on some university websites, as well as advice on financial matters. Here are a few ways that you may be able to fund your education:

• PhD Fellowships:  You’ll find a number of fellowships sponsored by the university, by companies and by the government (e.g. National Science Foundation). Be aware that some external fellowships will only cover the years of your dissertation research.
• Teaching/Research Assistantships:  Assistantships are a common way for universities to support PhD students. In return for teaching undergraduates or working as a researcher, you’ll often receive a break on tuition costs and a living stipend.
• In-State Tuition : Public universities may offer in-state students a much lower cost per credit.
• Regional Discounts:  Many state universities have agreements to offer reduced tuition costs to students from neighboring states (e.g.  New England Board of Higher Education Regional Student Program (RSP) . Check to see if this applies to your PhD.
• Travel Grants:  Doctoral students may have the opportunity to attend research conferences and network with future collaborators. Some grants are designed with this purpose in mind.
• Student Loans:  In addition to grants, you can consider applying for student loans to finance your PhD studies. Remember, a doctorate is a long-term commitment—you may not see a financial return on your education for a number of years.

Some PhD students in data science are  fully funded . For example:

• U.S. citizens and permanent residents in  Stanford’s PhD in Biomedical Informatics  are funded by a National Library of Medicine (NLM) Training Grant and Big Data to Knowledge (BD2K) Training Grants

If you’re coming from overseas, try talking to your school about any differences between funding for citizens and international students.

How Long Does a PhD in Data Science Take?

The length of time it takes to obtain a PhD will likely vary depending on your chosen program. Programs for similar or identical degrees can have differing completion requirements at different schools, meaning how many years your PhD program takes will differ as well.

Of course, the amount of time you spend working toward a PhD in data science can also vary depending on whether you choose to take it part-time or full-time. Assuming you consistently pass your classes, a full-time commitment to your PhD program will expedite your way through it.

But a commitment like that won’t fit everyone’s lifestyles. For example, you might need to work to support yourself financially, or you might be raising a family. These sorts of important commitments are time-consuming and can take a lot of energy. So, in that case, a part-time commitment to your PhD program might make more sense for you.

Interested in STEM Careers? 

If you’re looking for information on  career paths that involve STEM , see our guides below:

Data Science and Analytics Careers:

• Data Scientist
• Data Analyst
• Business Analyst

Computer Science, Computer Engineering and Information Careers:

• Computer and Information Research Scientist

Marketing and User Research Careers:

• UX Designer  

Compare Careers and STEM Fields:

• Cybersecurity vs. Computer Science

Related Graduate STEM Degrees

• Master’s in Business Analytics
• Master’s in Information Systems
• Master’s in Computer Engineering
• Master’s in Computer Science  
• Master’s in Cybersecurity Programs
• Master’s Applied Statistics
• Master’s in Data Analytics for Public Policy
• Data Science MBA Programs
• Master’s in Geospatial Science and
• Geographic Information Systems
• Master’s in Health Informatics
• Master of Library and Information Science

Related Undergraduate STEM Degrees

• Online Bachelor’s in Data Science
• Sponsored:  Computer Science at Simmons

PhD in Data Science School Listings

We found 57 universities offering doctorate-level programs in data science. If you represent a university and would like to contact us about editing any of our listings or adding new programs, please send an email to [email protected].

Last updated August 2021. The program’s website is always best for most up to date program information.

PhD in Data Science/Analytics Online

Looking for on-campus programs? See the  full list of on-campus PhD in Data Science/Analytics programs .

Colorado Technical University

Doctor of computer science – big data analytics, colorado springs, colorado.

Name of Degree: Doctor of Computer Science – Big Data Analytics

Enrollment Type: Self-paced

Length of Program: 4 years

Credits: 100

Admission Requirements:

Carnegie Mellon University

School of computer science, ph.d. program in machine learning, pittsburgh, pennsylvania.

Name of Degree: Ph.D. Program in Machine Learning

Enrollment Type: N/A

Length of Program: 2 years

Credits: N/A

• Recent transcripts
• Statement of purpose
• Three letters of recommendation
• TOEFL scores if your native language is not English

Chapman University

Schmid college, ph.d. in computational and data sciences, orange, california.

Name of Degree: Ph.D. in Computational and Data Sciences

Enrollment Type: Full-Time and Part-Time

Credits: 70

• GRE required
• Statement of intent 
• Resume or curriculum CV.                                       
• TOEFL score for international students

Indiana University – Indianapolis

School of informatics and computing, ph.d. in data science, indianapolis, indiana.

Name of Degree: Ph.D. in Data Science

Credits: 90

• Bachelor’s degree; master’s preferred
• Transcripts
• TOEFL or IELTS

Kennesaw State University

School of data science analytics, doctoral degree in analytics and data science, kennesaw, georgia.

Name of Degree: Doctoral Degree in Analytics and Data Science

Enrollment Type: Full-Time

Credits: 78

• Statement of how this degree facilitates your career goals

PhD in Data Science/Analytics On-Campus

Looking for online programs? See the  full list of online PhD in Data Science/Analytics programs .

New York University

Center for data science, new york , new york.

Credits: 72

• Resume or curriculum CV
• TOEFL or IELTS (TOEFL Preferred)
• Statement of Academic purpose

Institute for Computational and Data Sciences

Phd computational and data enabled science and engineering, buffalo, new york.

Name of Degree: PhD Computational and Data Enabled Science and Engineering

Computational Data Sciences  

• Master’s degree
• Resume or CV
• GRE scores (Temporarily suspended)

University of Maryland

College of information studies, doctor of philosophy in information studies, college park, maryland.

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

• Transcripts 
• Resume or CV or CV
• academic writing sample
• TOEFL/IELTS/PTE (required for most international applicants)

University of Massachusetts in Boston

College of management, doctor of philosophy in information systemaster of science for data science and management, boston, massachusetts.

Name of Degree: Doctor of Philosophy in Information SysteMaster of Science for Data Science and Management

Credits: 42

• Official transcripts official
• GMAT or GRE scores scores
• Official TOEFL or IELTS score.

University of Nevada – Reno

College of science, ph.d. in statistics and data science, reno, nevada.

Name of Degree: Ph.D. in Statistics and Data Science

Length of Program: 4+ years

• Undergraduate/Graduate Transcripts
• TOEFL/IELTS (only required for international students)

University of Southern California

School of business, ph.d. in data sciences & operations, los angeles, california.

Name of Degree: Ph.D. in Data Sciences & Operations

• Undergraduate/Graduate Transcripts 
• GRE or GMAT
• (3) letters of recommendation
• Passport Copy

University of Washington

Mechanical engineering, doctor of philosophy in mechanical engineering: data science, seattle, washington.

Name of Degree: Doctor of Philosophy in Mechanical Engineering: Data Science

Worcester Polytechnic Institute

Worcester, massachusetts.

PhD in Data Science

One of the first programs of its kind, in the nation, WPI’s interdisciplinary PhD in Data Science recognizes that traditional data processing applications can no longer handle today’s large and complex datasets. New models are needed to handle big data; and knowledgeable graduates with expertise in turning those observations into meaningful recommendations are in high demand.

Value Proposition Description

You’ll be working alongside faculty and industry partners to analyze, capture, search, share, store, transfer, query, and visualize huge amounts of data to solve real-world challenges. Some broad-stroke examples:

• using predictive analytics to identify cyber threats
• employing big data analytics to improve healthcare outcomes
• empowering “smart” cities to make data-driven policy changes critical for societal well-being

Applying to the Data Science PhD Program

Students applying to the data science PhD program will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.

WPI’s PhD in data science is interdisciplinary, drawing from Computer Science , Mathematical Sciences , and the Business School . Together, courses and dissertation research revolve around five key areas:

• Integrative Data Science
• Business Intelligence and Case Studies
• Data Access and Management
• Data Analytics and Mining
• Mathematical Analytics

PhD requirements include coursework as well as a research component. Together they total a minimum of 60 credit hours beyond the  Data Science master’s degree requirement . Students entering the Ph.D. Program with a bachelor’s degree first complete the M.S. in data science at WPI using the M.S.  Thesis  option as first step towards their Ph.D. degree. Each Ph.D. student is assigned an Academic Advisor and together they formulate a Plan of Study that then is approved by the Data Science Steering Committee.

• Core coursework requirements in the 5 categories as detailed below – 21 credits / 7 courses
• Electives in coursework – 9 credits / 3 courses
• Research credits – 30 credits TOTAL 60 credits (beyond MS program)

A Ph.D. student must obtain core competency by taking 7 courses from the below list of Data Science core areas, with an A grade in 4 out of the 7 courses and at least a grade B for the remaining 3 courses,  within 2 years after starting the Ph.D. 60 program.

Integrative Data Science  (Required) DS 501. Introduction to Data Science (3 credits)

Mathematical Analytics 3 credits (Select at least one) DS 502. Statistical Methods for Data Science (3 credits) MA 542. Regression Analysis MA 554. Applied Multivariate Analysis

Data Access and Management 3 credits (Select at least one) CS 542. Database Management Systems (3 credits) MIS571. Database Applications Development DS 503. Big Data Management (3 credits) CS 561. Advanced Topics in Database Systems

Data Analytics and Mining 3 credits (Select at least one) CS 548. Knowledge Discovery and Data Mining (3 credits) DS 504. Big Data Analytics (3 credits) CS 539. Machine Learning

Business Intelligence and Case Studies 3 credits (Select at least one) MIS 584. Business Intelligence MKT 568. Data Mining Business Applications

Nine more course credits must be taken, with the listing of courses pre-approved as electives for the Data Science program found in the WPI Graduate Catalog under  Data Science . Other graduate courses, graduate research credits, or ISGs may also be used, with prior approval of the Data Science Steering Committee. 

Two elective courses designed as ramp up courses for students who may be lacking in sufficient background in either statistics or programming, respectively, are listed below. They can count towards the 33 credits of the DS MS degree, However, they cannot be used to meet the above requirements in five core categories.

MA 511. Applied Statistics for Engineers and Scientists

CS 5007. Intro to Applications of CS with Data Structures and Algorithms (Programming for non-CS majors)

At least 30 credits must be research credits, consisting of DS 597 Directed Research and DS 699 Dissertation Research. Prior to Admission to Candidacy, a student may receive up to 18 credits of Pre-Dissertation Research under DS 597. Only after Admission to Candidacy by passing the Research Qualifier may a student receive credit toward Dissertation Research under DS 699.

As part of the research component, PhD students pass a Qualifying Examination and propose and defend Dissertation Research.  Learn more about the Ph.D. milestones, including the Ph.D. Qualifying Examination, the Ph.D. dissertation proposal and Ph.D. final dissertation defense.

Software Tools & Labs

The Data Science Innovation Lab is dedicated workspace for project work by students in the Data Science program. Robust servers and computer clusters are available for experimenting with large-scale datasets throughout labs at WPI, including many interdisciplinary facilities.

State-of-the-art software programs:

Close faculty interaction, cutting-edge equipment, and personal attention let you structure your program so it suits your individual career goals. You’ll leave with a degree that will help you succeed in your distinctive path.

Data science research gives you opportunities to work on grand challenge problems with societal importance, including topics such as cybersecurity, healthcare, and sustainability.

Our data science graduate program offers expertise in computer science, statistics, and business topics while giving you essential opportunities to work with industry partners.

WPI’s innovative and multidisciplinary graduate program prepares students to become talented and effective leaders in this rapidly evolving field.

WPI faculty and candidates in the PhD in data science are exploring every aspect of this burgeoning field. Together, they’re fueling breakthroughs that have direct, real-world impact in health, genetic analysis, sustainability, educational software, financial trading, and more.

A faculty advisor will help you design a Plan of Study for your dissertation as well as coursework in the core areas of data analytics and big data computing, statistical foundations and mathematical analytics, and business intelligence and innovation.

Cassandra DB2 Hadoop IBM Cognos IBM ILOG CPLEX IBM SPSS Modeler InfoSphere Big Insights InfoSphere Streams Mahout Maple MATLAB

MySQL Oracle Server Palisade DecisionTools Suite R RapidMiner SAS Spotfire SQL Server Tableaux Weka

Faculty Profiles

As founding Head of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, and new educational initiatives to our innovative industry-sponsored and mentored Graduate Qualifying projects at the graduate level.

Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.

Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI). His research interests focus on artificial intelligence (AI) and data science, with applications in smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization.

My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.

I am Associate Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy professorships in Mathematical Sciences and Data Science. I hold a Ph.D. in Industrial Engineering from the University of Pittsburgh. My objective is to use science and technology to assist real human need by improving systems that serve vulnerable peoples, such as refugees and asylum seekers, survivors of human trafficking, and children in the foster care system.

The Data Science Community

Getting Involved

We’re data scientists – we use tools of the trade and big data analytics to innovate. Follow department happenings and industry trends via our social media channels on  Facebook  and  LinkedIn .

Refer a Friend

Do you have a friend, colleague, or family member who might be interested in Worcester Polytechnic Institute’s (WPI) graduate programs? Click below to tell them about our programs.

Need to Earn a Master’s First? Explore Our Pioneering MS in Data Science

Not quite ready to apply for our data science PhD program since you first need to earn a master’s? Our pioneering master’s in data science dives into how to articulate findings into how to synthesize huge amounts of data and articulate findings into innovative solutions. WPI is one of a handful of universities that offers a MS in data science. Are you a working professional and prefer to study online? Our master’s in data science online makes it possible for you to advance your expertise from wherever you are conveniently online. Our core courses dive into the same on campus data-science essentials like analysis techniques, database management, and more. Maybe you’re excited about elevating your career in big data, but have questions about PhD data science salary and popular job titles. Check out our career information for data science.

Advance Your Data Science Skills with a Graduate Certificate

Individuals who know how to interpret and harness large amounts of data are in high demand and our graduate certificates are a perfect way to customize your data science skills and aspirations. Our on campus graduate certificate in data science enables students to select six courses that dive into mathematical analytics, data management, business intelligence, and more. Maybe you have a busy work-life balance and prefer to study online? Our online data science certificate brings world-class instruction right to you with flexible course offerings that enable you to enhance your data analytics skills.

Ready to Start Your Data Science Path?

If data intrigues you and you love the idea of finding patterns and revealing the information in massive amounts of data, a future in data science likely appeals to you. If you have your sights set on a PhD in data science, you can get started on the right academic path with a bachelor’s in data science . WPI’s bachelor’s program offers hands-on projects to increase your understanding of the field while giving you real skills you can use. If you’re majoring in another field, such as business or computer science, a minor in data science will give you a solid understanding of data science concepts. With a minor in data science, you’ll gain skills and learn how to apply them to your chosen discipline.

WPI is proud to be the recipient of not one, but two National Science Foundation Research Traineeship programs. The programs provide exceptionally talented graduate students with specialized training and funding assistance to join careers at the forefront of technology and innovation. The programs are for graduate students in research-based master's and doctoral degree programs in STEM. Learn more .

The BioPoint Program for Graduate Students has been designed to complement traditional training in bioscience, digital and engineering fields. Students accepted into one of the home BioPoint programs will have the flexibility to select research advisors and take electives in other departments to broaden their skills. BioPoint curriculum is designed to be individual, interactive, project-focused and diverse, and includes innovative courses, seminars, journal clubs and industrial-based projects. Learn more .

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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

We launched the first formal PhD program in Data Science in 2015.  Our program sits at the intersection ofcomputer science, statistics, mathematics, and business.  Our students engage in relevant research with faculty from across our eleven colleges.  As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community.   

Herman Ray , Director, Ph.D. in Data Science and Analytics

About the Doctoral Degree in Data Science and Analytics

This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.

Because this degree is a Ph.D., it creates flexibility. Graduates can either pursue a position in the private or public sector as a "practicing" Data Scientist – where continued demand is expected to greatly outpace the supply - or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation.

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

• Successful applicants will have completed a masters degree in a computational field (e.g., engineering, computer science, statistics, economics, finance, etc.)
• Applicants are expected to have deep proficiency in at least one analytical programming language (e.g., SAS, R, Python). SQL and Java are helpful but not required.
• Interested applicants who have earned an undergraduate degree are encouraged to apply to the Ph.D. Program with the embedded MS in Computer Science or with the MS in Applied Statistics.

Stage Two: Coursework

The Ph.D. in Data Science and Analytics requires 78 total credit hours spread over four years of study. Example Program of Study: 

• CS 8265  - Big Data Analytics
• CS 8267  - Machine Learning
• MATH 8010  - Theory of Linear Models (optional)
• MATH 8020  - Graph Theory
• MATH 8030  - Applied Discrete and Combinatorial Mathematics 
• STAT 8240  - Data Mining I
• STAT 8250  - Data Mining II
• Comprehensive Exam 
• 21 credit hours of electives in computer science, statistics, mathematics, information technology, or other area by permission. 
• Research Proposal 
• DS 9700 Doctoral Internship/Research Lab
• DS 9900 Dissertation
• Dissertation Proposal Defense
• DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

Relevant, interdisciplinary research forms the foundation of the Ph.D. in Data Science and Analytics. While students are encouraged to engage in research from their first semester, the last two years of the program are structured to help students transition into becoming independent, lead researchers. In this last stage of the program, students will work with research faculty, including their advisor, in one of our data science research labs.

Program Student Learning Outcomes

At the end of the program, students will be able to:

• Demonstrate their understanding of the research process
• Demonstrate mastery of core concepts relevant to three key areas in mathematics, statistics and computer science
• Develop themselves as professionals prepared for work as a doctoral-educated individual beyond graduation

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

• Applied Computer Science
• Applied Economics and Statistics
• Applied Statistics
• Applied Mathematics
• Bioinformatics
• Business Analytics
• Chemical Biology
• Computer Science
• Data Science
• Forecasting & Strategic Management
• Integrative Biology
• Public Admin in Economic Policy Mgmt
• Mathematics
• Mechanical Engineering
• Software Engineering

What is the Project Engagement requirement?

Can I pursue the program part- time while I am working full-time?

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

• Ajou University, South Korea
• Albert-Ludwigs University of Freiburg
• Auburn University
• Bowling Green State University
• Clemson University
• Columbia University
• Columbus State University
• Florida State University
• Georgia Southern University
• Georgia State
• Georgia Tech
• Iran University of Science and Technology
• Kennesaw State University
• Marshall University
• Michigan State University
• Murray State University
• North Carolina State University
• St. Petersburg State University, Russia
• University of KwaZulu-Natal, South Africa
• University of Michigan
• University of North Carolina
• University of Toledo

Ph.D. in Data Science and Analytics Student Cohorts

Royce Alfred

Bachelor's Degree:   Psychology, Kennesaw State University

Master's Degree:   Applied Statistics and Analytics, Kennesaw State University

Work History:   4 years as a Data Scientist at Equifax

Professional Objective:   Work as a research data scientist in the corporate environment

Venkata Abhiram Chitty

Bachelor's Degree:   Mathematics, Statistics and Computer Science, Osmania University, Telangana, India

Master's Degree:   Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India

Professional Objective:   To apply my Data Science skills in public health domain and help the society

Caleb Greski

Bachelor's Degree: 

Master's Degree: 

Work History: 

Courses Taught: 

Publications: 

Professional Objective: 

Moukthika Kadaparthi

Bachelor's Degree:   Electrical and Electronics Engineering, SASTRA Deemed University

Master's Degree:   Computers and Information Science, Cleveland State University

Work History:  

• Business Intelligence Analyst, Philips Healthcare, Georgia
• Graduate Research Assistant, Cleveland State University, Ohio 

Professional Objective:   My objective is to enter academia with the aim of sharing the practical applications of data science in diverse domains and its potential positive impacts. With my unique blend of academic rigor and industry experience, I am driven to analyze complex data sets using cutting-edge data science techniques, to provide actionable insights and support data-driven decision-making.

Bachelor's Degree:   Civil Engineering, Huazhong University of Science and Technology, China

Master's Degree:   Business Analytics, Syracuse University

• Credit Modeling Analyst, Agricultural Development Bank of China
• Research Assistant, Changjiang Securities
• Graduate Assistant, Syracuse University

Courses Taught:  Calculus I, Marketing Analytics, Data Mining

Awards:   Merit-Based Scholarship, Syracuse University

Professional Objective:   To secure a challenging position in a reputable organization to expand myself within the field of Artificial Intelligence.

Kausar Perveen

Bachelor's Degree:   Bachelor in Engineering Software Engineering, National University of Sciences and Technology, Pakistan

Master's Degree:   Masters in Data Science, Illinois Institute of Technology, Chicago

• Fullstack Developer at ItRunsInMyFamily, Charleston, South Carolina
• Software Engineer II , Xgrid Pakistan
• Senior Research Coordinator, Aga Khan University Pakistan
• Machine Learning Engineer, Agoda Thailand

Publications:  National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan 

Service and Awards:

• Fulbright Scholarship award for Master’s degree in Data Science
• Aga Khan Education Service Pakistan, merit cumulative need based scholarship for Bachelors in Software Engineering 

Professional Objective:  My main motivation behind getting a degree in Data Science is to receive and perform qualified research experience in Data Science and public health

Bachelor's Degree:   Statistics, University of Dhaka, Dhaka, Bangladesh

Master's Degree:   Mathematics (Statistics Concentration), University of Toledo, Ohio

• Analytics Engineer Intern, Cooper Smith, Toledo, Ohio
• Business AnalystAkij Food and Beverage Limited, Dhaka, Bangladesh

Courses Taught:   Introduction to Statistics

Professional Objective:   I am interested to work as a data scientist in the industry

Ayomide Isaac Afolabi

Bachelor's Degree:  Chemical Engineering, Ladoke Akintola University of Technology 

Master's Degree:  Data Science, Auburn University 

Work History:   Graduate Research Assistant, Auburn University 

Courses Taught:   Python Programming 

Publications:   Larson EA, Afolabi A, Zheng J, Ojeda AS. Sterols and sterol ratios to trace fecal contamination: pitfalls and potential solutions. Environ Sci Pollut Res Int. 2022 Jul;29(35):53395-53402.  doi: 10.1007/s11356-022-19611-2 . Epub 2022 Mar 14. PMID: 35287190

Professional Objective:  To work as a research data scientist in the industry

Dinesh Chowdary Attota

Bachelor's Degree:   Computer Science, Jawaharlal Nehru Technological University Kakinada (JNTUK), India

Master's Degree:   Computer Science, Kennesaw State University

Work History:   Associate Consultant, SL Techknow Solutions India Pvt Ltd, India  2018 - 2020

Publications:  

• An Ensemble Multi-View Federated Learning Intrusion Detection for IoT
• A Conversational Recommender System for Exploring Pedagogical Design Patterns
• An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning  

Professional Objective:   I'd like to be a faculty member at a university so that I can continue to do research.

Nzubechukwu Ohalete

Bachelor's Degree:   Mathematics,University of Nigeria, Nsukka

Master's Degree:   Applied Statistics, Bowling Green State University

Work History:   Graduate Assistant/Data Analyst, Federal University of Technology, Owerri - Mathematics Department

Courses Taught:  Elementary Mathematics, Mathematical Methods

Awards:   James A. Sullivan Outstanding Graduate Student Award, Applied Statistics and Operations Research Department, April 2022

Professional Objective:   To use data science techniques to solve problems which makes our lives better and also makes our world a better place

Ryan Parker

Bachelor's Degree:  Microbiology, University of Tennessee - Knoxville

Master's Degree:   Integrative Biology, Kennesaw State University

Work History:  Instructor of Biology, Kennesaw State University

Courses Taught:   Nursing Microbiology Lectures and Labs, Introductory Biology Labs, Biotechnology Lectures and Labs

• Parker RA, Gabriel KT, Graham K, Cornelison CT. Validation of methylene blue viability staining with the emerging pathogen Candida auris. J Microbiol Methods. 2020 Feb;169:105829.   doi: 10.1016/j.mimet.2019.105829 . Epub 2019 Dec 27. PMID: 31884053.
• Parker RA, Gabriel KT, Graham KD, Butts BK, Cornelison CT. Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens. 2022 Jul 22;11(8):821.   doi: 10.3390/pathogens11080821 . PMID: 35894044; PMCID: PMC9331469.

Awards:   Best Graduate Poster: Symposium for Student Scholars hosted by Kennesaw State University (Fall 2018) for Poster: "Antifungal Activity of Select Essential Oils and Synergism with Antifungal Drugs against Candida auris"

Professional Objective : To apply Data Science techniques to large scientific datasets, such as genomic and astronomical data, and to help bridge the gap between disparate fields by working in an interdisciplinary space to offer integrative and data-driven solutions to the increasingly complex problems presented to the traditional Sciences.

Askhat Yktybaev

Bachelor's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia

Master's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia; Public Administration in Economic Policy Management, School of International and Public Affairs, Columbia University

Work History:

• from Data Analyst to Head of Research Unit, Central Bank of Kyrgyz Republic
• Sr. Data Scientist in OJSC, Aiyl Bank, Kyrgyzstan
• Consultant, The World Bank, Washington D.C.

Courses Taught:   Financial Programing in the Central Bank, Monetary Policy Transmission Mechanism

Service and Awards:   Winner of the Joint Japan/World Bank Graduate Scholarship Program, National Bank Silver Medal for Best Forecast

Professional Objective:   I want to found a successful Fintech startup one day.

Sanad Biswas

Bachelor's Degree:   Statistics, Biostatistics and Informatics, University of Dhaka, Bangladesh

Master's Degree:   Statistics, University of Toledo, OH

• Research Assistant: US Army Research Lab, Kennesaw State University
• Consultant, Statistical Consulting Service, University of Toledo
• Graduate Teaching Assistant, University of Toledo

Courses Taught:   Calculus and Business Calculus, Facilitated students’ study of Statistics courses at the University of Toledo.

Professional Objective:   To work as a researcher in the industry or as a faculty. I am primarily interested in the application of machine learning in different fields.

Mallika Boyapati

Bachelor's Degree:  Electronics and Computer Engineering, K L University, India

Master's Degree:  Applied Computer Science, Columbus State University

• T-Mobile, Seattle, WA, USA: Sr. Data analyst, 2018- 2021
• UITS, Columbus State University, Columbus, GA, USA: Data Analyst -Graduate assistant, 2016-2018
• Menlo Technologies, India: Jr. Data Analyst, Intern, 2014- 2016

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

• Anti-Phishing Approaches in the Era of the Internet of Things. In: Pathan, AS.K. (eds) Towards a Wireless Connected World: Achievements and New Technologies. Springer, Cham -   https://doi.org/10.1007/978-3-031-04321-5_3
• An empirical analysis of image augmentation against model inversion attack in federated learning -   https://doi.org/10.1007/s10586-022-03596-1
• M. Boyapati and R. Aygun, "Phishing Web Page Detection using Web Scraping," SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 167-174, doi: 10.1109/SoutheastCon51012.2023.10115148.
• M. Boyapati and R. Aygun, "Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 139-142, doi: 10.1109/ICSC56153.2023.00029.
• Boyapati, M., Aygun, R. (2023) Explainable Machine Learning for Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering. In Encyclopedia with Semantic Computing and Robotic Intelligence VOL. 0 https://doi.org/10.1142/S2529737623500119
• Winners of Dataiku March Madness Bracket-thon, 2021 in predicting the NBA bracket
• Winners of 2021 Analytics Day Ph.D. level research poster presentation 

Professional Objective:   To leverage strong analytical and technical abilities to research and develop effective data models, visualize data, and uncover insights that makes an impact in field of data science

Nina Grundlingh

Bachelor's Degree:   Applied Mathematics and Statistics, University of KwaZulu-Natal, South Africa

Master's Degree:   Statistics, University of KwaZulu-Natal, South Africa

Courses Taught:   Introduction to Statistics, University of KwaZulu-Natal

• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in South Africa. The 61st conference of the South African Statistical Association, 27-29 November 2019, Nelson Mandela University, South Africa.
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in the South African population. College of Agriculture, Engineering and Science Postgraduate Research & Innovation Symposium 2019, 17 October 2019, University of KwaZulu-Natal, Westville, South Africa (the award for best MSc presentation was also received for this).
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling risk factors of diabetes and pre-diabetes in South Africa. IBS SUSAN-SSACAB 2019 Conference, 8-11 September 2019, Cape Town, South Africa.
• University of KwaZulu-Natal Postgraduate Research & Innovation Symposium 2019 – Best Masters oral presentation
• South African Statistical Association Honours Project Competition 2018/2019 – 2nd place and special prize for best use of SAS

Professional Objective:   To work in a teaching position – sharing how data science can be applied to different fields and the positive impact it could have. I would like to use my theological background and passion to bring insight, clarity, and wisdom to data science problems. 

Namazbai Ishmakhametov

Bachelor's Degree:   Specialist in Mathematical Methods in Economics, Kyrgyz-Russian Slavic University

Master's Degree:   Analytics, Institute for Advanced Analytics at North Carolina State University

• Expert at the Centre for Economic Research, National bank of the Kyrgyz Republic
• Consultant in World Bank project dedicated to strengthening the regulatory practices in Kyrgyz Republic
• Consultant at Deloitte Consulting LLP, Science Based Services group, Analytics & Cognitive offering
• Macroeconomic modeling expert in the Economic Department, National bank of the Kyrgyz Republic

Courses Taught:   Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan

• Ishmakhametov Namazbai, Abdygulov Tolkunbek, Jenish Nurbek. 2020. “ Impact of 2014-2015 shocks on economic behavior of the households in the Kyrgyz Republic ". Working Paper of the National Bank of the Kyrgyz Republic
• Sherrill W. Hayes, Jennifer L. Priestley, Namazbai Ishmakhametov, Herman E. Ray. 2020. “ I’m not Working from Home, I’m Living at Work ”: Perceived Stress and Work-Related Burnout before and during COVID-19”. PsyArxiv Preprints
• Ishmakhametov Namazbai, Arykov Ruslan. 2016. “ Credit Risk Model on the Example of the Commercial Banks of the Kyrgyz Republic ”. Working Paper of the National Bank of the Kyrgyz Republic
• Namazbai Ishmakhametov, Anvar Muratkhanov.2015. “Modeling strategy of the Bank of the Kyrgyz Republic”. National bank of Poland – Swiss National bank joint seminar. Zurich, Switzerland

Professional Objective:   To apply my quantitative skills in the field of biotech either in corporate or government sector

Symon Kimitei

Bachelor's Degrees:   Mathematics, Kennesaw State University, and Computer Science,  Kennesaw State University

Master's Degree:   Mathematics (Scientific Computing Concentration), Georgia State University 

Work History:   Senior Lecturer and Math Department Coordinator of Supplemental Instruction, Kennesaw State University

Courses Taught:   Calculus 1, Precalculus, Applied Calculus & College Algebra 

• Haskin, S., Kimitei, S., Chowdhury, M., Rahman, F., Longitudinal Predictive Curves of Health-Risk Factors for American Adolescent Girls. Journal of Adolescent Health.  JAH-2021-00601R1
• Symon K Kimitei,   Algorithms for Toeplitz Matrices with Applications to Image Deblurring . 2008. Georgia State University, Masters thesis. ScholarWorks 

Poster Presentations:

• Kimitei, Symon & Sammie Haskin. "Nadaraya-Watson Kernel Regression Longitudinal Analysis of Healthcare Risk Factors of African American and Caucasian American Girls." Kennesaw State University R Day Presentation.  11 Nov. 2019. Poster presentation.
• Kimitei, Symon. " Social Network Analysis in Supreme Court Case Rulings by Precedence Using SAS Optgraph/Python." 23rd Annual Symposium of Scholars. Kennesaw State University.  19 April. 2018. Poster presentation.

Professional Objective:   As a Ph.D. student in Analytics & Data Science, I hope to gain skills in the program that will propel me into a Data Scientist / Machine Learning Engineer with a specialization in the design and implementation of deep learning & machine learning algorithms.

Jitendra Sai Kota

Bachelor's Degree:   Computer Science & Engineering, Amrita Vishwa Vidyapeetham, India

Master's Degree:   Computer Science, Florida State University

Work History:   Teaching Assistant Professor in Computer Science at an Engineering College in India

Courses Taught:   Problem Solving & Program Design through C, Artificial Intelligence, Data Mining

Publications:  Kota, Jitendra Sai, Vayelapelli, Mamatha. 2020. "Predicting the Outcome of a T20 Cricket Game Based on the Players' Abilities to Perform Under Pressure". IEIE Transactions on Smart Processing and Computing 9(3):230-237.   DOI: 10.5573/IEIESPC.2020.9.3.230

Professional Objective:   to work in Data Science in a Corporate Environment

ResearchGate

Catrice Taylor

Bachelor's Degree:   Economics, Clemson University 

Master's Degrees:  Applied Economics and Statistics, Clemson University, and Applied Statistics, Kennesaw State University 

Professional Objective:   To work as an industry data scientist in a corporate environment 

Sahar Yarmohammadtoosky

Bachelor's Degree:   Applied Mathematics, Sheikh Bahaei University, Isfahan, Iran 

Master's Degree:   Applied Mathematics, Iran University of Science & Technology, Tehran, Iran

Courses Taught:  Numerical Analysis and Linear Algebra, Iran University of Science & Technology

Publications:   Noah, G., Sahar, Y., Anthony P. & Hung, C.C. "ISODS: An ISODATA-Based Initial Centroid Algorithm". Accepted to: 10th International Conference on Information, March 6 - 8, 2021, Hosei University, Tokyo, Japan

Professional Objective:   My goal is to become a competent Data Science specialist capable of using my skills to bring meaning to data, getting a faculty position at a university

Martin Brown

Bachelor's Degree:  Mathematics, Swansea University, United Kingdom

Master's Degree:  Mathematics, Murray State University

• Graduate Research Assistant, Kennesaw State University, August 2020 to present
• Graduate Teaching Assistant, Murray State University, August 2018 to May 2020

Course Taught:  Problem Solving in Mathematics

Publications:   Brown, Martin K. W. "Evaluating an Ordinal Output using Data Modeling, Algorithmic Modeling, and Numerical Analysis" (2020).   Murray State Thesis and Dissertations 168 .

Awards:  David Pryce History of Mathematics Prize 2017-2018

Professional Objective:  To pursue a career in data science, machine learning, and predictive analytics to solve real-world issues 

Inchan Hwang

Bachelor’s Degree: Computer Science, Georgia Southwestern State University

Master’s Degree: Software Engineering, Ajou University, South Korea

Courses Tutored: Precalculus, College Algebra, Calculus I at Georgia Southwestern State University

Tutoring College Algebra, Calculus I and II at Academic Skills Center, Georgia Southwestern State University Research Assistant at Intelligence of HyperConnected Systems Lab of Ajou University Fullstack web developer, windows system programmer in the cybersecurity industry Professional Objective: To work in big data analytics, and research and development of machine learning in engineering, and security

Duleep Prasanna Rathgamage Don

Bachelor's degree:   Physics and Mathematics, The Open University of Sri Lanka

Master's degree:   Mathematics, Georgia Southern University

• Graduate Teaching Assistant, Georgia Southern University, 2016 - 2018
• Graduate Teaching Assistant, University of Wyoming, 2019 - 2020

Courses Taught:   Trigonometry, and Calculus I & II

Publications/Presentations:

• Don, R. D. and Iacob, I. E., ‘DCSVM: Fast Multi-class Classification using Support Vector Machines’,   International Journal of Machine Learning and Cybernetics .
• Rathgamage Don, D., Iacob, E., ‘Divide and Conquer Support Vector Machine for Multiclass Classification’, Research Symposium (2018), Georgia Southern University.
• Rathgamage Don, D., Iacob, E., ‘Multiclass Classification using Support Vector Machines’, MAA Southeastern Section Meeting (2018), Clemson University.

Professional Objective:   To work in big data analytics, and research and development of machine learning in engineering, and medicine

Linglin Zhang

Bachelor’s Degree:   Biological Sciences, Hubei University, China

Master’s Degree:   Chemical Biology, University of Michigan and Bioinformatics, Georgia Institute of Technology

Selected Publications:   Rebecca Shen, Zhi Li, Linglin Zhang, Yingqi Hua, Min Mao, Zhicong Li, Zhengdong Cai, Yunping Qiu, Jonathan Gryak, Kayvan Najarian. (2018). Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 690-693, 2018.

Professional Objective:  To become a researcher in industry or academia. My background in Biology and Bioinformatics could provide me strong theoretical support on a research role in the health industry. The experience of doing an internship at Equifax equipped me of certain knowledge on business cases. 

Yihong Zhang

Bachelor’s Degree:   Psychology Mathematics Interdisciplinary, Chatham University

Master’s Degree:   Mathematics and Statistics Allied with Computer Science, Georgia State University

• Research Assistant - Collaborated with biomedical department to analyze and visualize microarray gene expression data, Facilitated in data pre-processing and machine learning modeling of clinical liver cirrhosis image data, Assisted in feature engineering of image analysis in deep learning for pathology diagnosis with Mayo Clinic’s pilot project.
• Graduate Lab Assistant - Tutored students with statistics and math subjects.

Professional Objective:   Make better use of data in healthcare and bioinformatic industry as a data scientist.

2019 - 2020

Trent Geisler

Graduation Date:   Summer 2022

Dissertation:   Novel Instance-Level Weighted Loss Function for Imbalanced Learning

Dissertation Advisor:   Dr. Herman Ray

Current Position:   Assistant Professor, Department of Systems Engineering, United States Military Academy West Point

Srivatsa Mallapragada

Bachelor’s Degree:  Mechanical Engineering, Andhra University College of Engineering, India

Master’s Degree: Mechanical Engineering, University of North Carolina at Charlotte

Continuous Improvement Intern, Daimler Trucks North America at Cleveland, North Carolina, USA Computational Fluid Dynamics (CFD) Graduate Research Assistant, NC Motorsports and Research Laboratory Manufacturing Intern, Caterpillar India Pvt Ltd, Sriperambudur, India Selected Publications/Presentations:

Mallapragada, S. (2017). Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side Wall (Master’s thesis, The University of North Carolina at Charlotte). Uddin, M., Mallapragada, S., & Misar, A. (2018). Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side-Wall (No. 2018-01-0704). SAE Technical Paper. Dimensionality Reduction of Hyperspectral Images for Classification, Srivatsa, M., Michael, W. & Hung, C. C. Ninth International Conference on Information ISSN: 1343-4500 Bounds, C., Mallapragada, S., and Uddin, M., "Overset Mesh-Based Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side-Wall," SAE Int. J. Passeng. Cars - Mech. Syst. 12(3):211-223, 2019, https://doi.org/10.4271/06-12-03-0015. Service and Awards: Base SAS Programmer V9 Professional Objectives: I am currently working in unsupervised pattern recognition in high dimensional data sets. After I graduate, I would like to pursue a career in Data Science and Machine Learning in the corporate environment.

Sudhashree Sayenju

Graduation Date:   Spring 2023

Dissertation:   Quantification and Mitigation of Various Types of Biases in Deep NLP Models

Dissertation Advisor:   Dr. Ramazan Aygun

Christina Stradwick

Bachelor’s Degree:  Music Performance and Mathematics, Marshall University

Master’s Degree:  Mathematics with Emphasis in Statistics, Marshall University

Courses Taught:  Prep for College Algebra at Marshall University

Selected Presentations:

• Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
• Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
• Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

Professional Objectives:  To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.

2018 - 2019

Md Shafiul Alam

Graduation Date:   Fall 2022

Dissertation:   Appley:   App roximate Shap ley   Values for Model Explainability in Linear Time

Dissertation Advisor:   Dr. Ying Xie

Current Position:   AI Framework Engineer, Intel Corporation

Jonathan Boardman

Dissertation:   Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics

Current Position:   Data Scientist, Equifax

Tejaswini Mallavarapu

Bachelor’s Degree:   Pharmacy, Acharya Nagarjuna University, India

Master’s Degree:   Computer Science, Kennesaw State University

• Graduate Research Assistant, Kennesaw State University, 2017-present
• Research Analyst, Divis Laboratories, 2013-2014

Selected Publications:

• T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Accepted, 2017.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Ch. MadhusudhanaRao, M. Tejaswini, "Design and Evaluation of Binding Properties of Cassia roxburghii Seed Galacto mannan and Moringa oleifera Gum in the Formulation of Paracetamol Tablets," Research Journal of Pharmacy and Technology(RJPT). 3(1): Jan.-Mar. 2010; Page 254-256.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Y.V. Kishore Reddy, M. Tejaswini, Ch. MadhusudhanaRao, V. Tejopavan, "Cassia roxburghii Seed Galacto manna— a potential binding agent in the tablet formulation," Journal of Biomedical Science and Research(JBSR), Vol 2 (1), 2010, 18-22

Professional Objective:   To be a data scientist in the field of health care or bioinformatics where I can leverage my analytical skills and knowledge towards the advancement of the research field.

Seema Sangari

Dissertation:   Debiasing Cyber Incidents - Correcting for Reporting Delays and Under-reporting

Dissertation Advisor:   Dr. Michael Whitman

Current Position:   Principal Modeler, HSB 

Srivarna Settisara Janney

Bachelor’s Degree:   Mechanical Engineering, Visveswaraiah Technological University, India

• Graduate Research Assistant, Kennesaw State University, 2016-2018
• Senior Software Engineer, Torry Harris Business Solutions (THBS), United Kingdom, 2010-2012 and India, 2012-2014
• Software Engineer, Torry Harris Business Solutions (THBS), India, 2007-2010

Selected Publications/Presentations:

• S.S. Janney, S. Chakravarty, “New Algorithms for CS – MRI: WTWTS, DWTS, WDWTS”, One-page research paper, 40th International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Jul 2018
• Master thesis presented at Southeast Symposium on Contemporary Engineering Topics (SSCET), UAH Engineering Forum, Alabama, Aug 2018
• Master thesis poster is accepted to be presented at Biomedical Engineering Society (BMES) 2018 Annual Meeting, Oct 2018
• Submitted draft copy for book chapter contribution on “Bioelectronics and Medical Devices”, Elsevier Publisher, May 2018
• Showcased 3MT, Georgia Council of Graduate Schools (GCGS), Apr 2018
• Master thesis presented in workshop for “Medical Signal and Image Processing” at Department of Biotechnology & Medical Engineering, NIT Rourkella, Feb 2018
• S.S. Janney, I. Karim, J. Yang, C.C Hung, Y. Wang, “Monitoring and Assessing Traffic Safety Using Live Video Images”, GDOT project showcase, 4th Annual Transportation Research Expo, Sept 2016
• 1st Place Winner, Graduate Research Project, C-day Poster Presentation, Kennesaw State University, Spring 2018
• People's Choice Award, 3 Minute Thesis (3MT), Apr 2018
• CCSE Dean’s 4.0 Club, Jan 2018
• 3rd Place Winner, Hackathon 2017 - HPCC Systems Big Data
• Foundation of Computer Science, Certified by Kennesaw State University, Jun 2016
• Fundamental of RESTful API Design, Certified by APIGEE, Nov 2014
• Member of HandsOnAtlanta, since 2014
• SOA Associate, Certified by IBM, Jun 2008

Professional Objective:   I would like to be a researcher in Data Science and Analytics in medical imaging technologies contributing to advancements that would help medical and healthcare professionals provide value-based and personalized health care. I would like to look at career opportunities in industry and academia that fuel my interest in research.

2017 - 2018

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

Current Position: Assistant Professor, Saint Joseph's University - Erivan K. Haub School of Business

Mohammad Masum

Dissertation: Integrated Machine Learning Approaches to Improve Classification Performance and Feature Extraction Process for EEG Dataset

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

Dissertation: A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in the Episodes of Care Healthcare Delivery System

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

Dissertation: Tree-BERT - Advanced Representation Learning for Relation Extraction

Dissertation Advisor: Dr. Ying Xie

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

Dissertation: Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

Dissertation: Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

Dissertation: A Novel Penalized Log-likelihood Function for Class Imbalance Problem

Current Position: Data Scientist/Research Engineer, Hewlett Packard Enterprise

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date:  Spring 2020

Dissertation: A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data

Dissertation Advisor:  Dr. Stefano Mazzotta

Current Position: Assistant Professor, Business Analytics, Tennessee Technological University

Bogdan Gadidov

Graduation Date:  Summer 2019

Dissertation: One- and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

Dissertation:  Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Dissertation Advisor:  Dr. Mingon Kang

Current Position:  Assistant Professor, Chinese Academy of Medical Sciences, Peking Union Medical College

Graduation Date:  Spring 2019

Dissertation:  Deep Embedding Kernel

Current Position: Assistant Professor, Information Technology, Kennesaw State University

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation:  Ordinal Hyperplane Loss

Dissertation Advisor:  Dr. Ying Xie

Current Position:  Principal Data Scientist, Microsoft

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Statistics and Data Science

Wharton’s phd program in statistics and data science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. these include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer science..

Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

• analysis of observational studies;
• Bayesian inference, bioinformatics;
• decision theory;
• game theory;
• high dimensional inference;
• information theory;
• machine learning;
• model selection;
• nonparametric function estimation; and
• time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

For information on courses and sample plan of study, please visit the University Graduate Catalog .

Get the Details.

Visit the Statistics and Data Science website for details on program requirements and courses. Read faculty and student research and bios to see what you can do with a Statistics PhD.

Statistics and Data Science Doctoral Coordinator 

Dr. Bhaswar Bhattacharya Associate Professor of Statistics and Data Science Associate Professor of Mathematics (secondary appointment) Email: [email protected] Phone: 215-573-0535

Ph.D. Data Science Online

By Brandon Mario D`Souza

Updated january 7, 2024.

Brandon Mario D’Souza

Registered Social Worker – Social Work England

Brandon is a registered social worker with Social Work England. He obtained his master’s in social work (personnel management & industrial relations) and used it in fields such as water treatment, construction, software, and education. Then he transitioned to the health and social care sector with an M.Sc. in health psychology. Brandon loves to write, cook, and play musical instruments (piano, guitar, melodica, ukulele, and kazoo) and enjoys the calmness and serenity of nature.

Master of Social Work, Master of Health Psychology

Areas of Expertise & Credentials

None to disclose in particular; the basics are covered in the short bio.

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• Best Online Bachelor’s Degree In Psychology Program
• Find Online Bachelor’s Degree In Psychology
• Types Of Online Bachelor’s Degree In Psychology Programs
• What To Expect From The Online Bachelor’s Degree In Psychology Program?
• Areas Of Specialization For Online Bachelor’s Degree In Psychology
• Why Should I Pursue An Online Bachelor’s Degree In Psychology?

The study of data science focuses on developing computational and statistical skills, helping data scientists handle data by understanding integral knowledge to recognize relevant data sets. According to the U.S. Bureau of Labor Statistics[1], the number of data science jobs and jobs in related fields in 2021 reached 113,300, much faster than the average for other professions. Doing a Ph.D. in data science online would help students acquire advanced skills in extracting knowledge and picking up insights from both unstructured and structured data. Students completing an online Ph.D. in data science would be able to develop high-end models and algorithms for interpreting big data and work in senior positions in academia or the industry.

Best Ph.D. Data Science Online Programs

When selecting online Ph.D. Programs students would have to narrow down their options judiciously, as earning a Ph.D. could take up a substantial amount of time and resources. Therefore, students must consider factors like the duration of the program, resources required to complete the curriculum, research facilities, availability of suitable guides and mentors, accreditation, availability of financial aid, etc., to find a program that suits them most. Based on these and several other factors, FBD teams have drawn up a list of some of the best Ph.D. data science online: *

Indiana University-Purdue University-Indianapolis(ONCAMPUS)

Northcentral university, colorado technical university-colorado springs, university of central florida, capitol technology university, stevens institute of technology(oncampus), columbia university(oncampus), kennesaw state university, grand canyon university, harrisburg university of science and technology(oncampus).

Universities and programs are ranked by various factors, such as affordability, curriculum and coursework, reputation and availability, program length, statistics, the potential of employment, and return on investment for the students. For a more in-depth analysis, please read about our rankings methodology page . 

What to Expect from Ph.D. Data Science Online Programs

A doctorate data science online program requires exhaustive academic work and will take 5 to 7 years to complete if pursued full-time. The online Ph.D. data science program would typically require 60 to 75 credits; the number of credits depends on the program’s curriculum and requirements. Along with the coursework, students will also be responsible for teaching and research. While some courses in the data science doctoral program are standard across programs at various institutions, most curricula are built on the foundation of statistics.

Students will learn techniques to analyze dynamic, structured, and unstructured datasets. The program will also train students to understand how to articulate the limitations and strengths of analytical methods. They must generally complete about 21 credit hours of elective courses, a preliminary exam, a qualifying exam, and a dissertation.

Some of the basic components of the online Ph.D. data science curriculum would be:

The courses students that when pursuing a data science program are varied. Students can choose different courses per their requirements from one data science concentration.

As part of the doctorate data science online requirements, students must complete certain residency hours before completing the comprehensive exams. Students who have opted for graduate assistantships may be exempted from this requirement.

Although teaching is not a compulsory requirement of the Ph.D. in data science online program, most colleges recommend the teaching experience for students who have not taken graduate assistantships.

Comprehensive Exams

After students complete their coursework, they become eligible to apply for comprehensive examinations. They are usually required to pick up topics of their choice and write a manuscript on that topic. They are also required to orally defend their manuscripts when presenting them in front of a committee. A practical and theory exam could also be included to ensure that students are at a professional level of expertise.

Field Experience

Students are usually encouraged to do independent study and research to integrate and combine their research and classroom theory with varied field activities. They can engage with professionals from their field and collect research data from various sources.

Dissertation

Ph.D. students must work with their advisors by choosing topics that will help them advance in their careers. They often start working on the dissertation after passing the comprehensive exam after completing the curriculum.

Examples of research topics in the data science field are:

• Mathematical Analysis
• Data Analytics and Mining
• Data Access and Management
• Integrative Data Science
• Machine Learning Algorithms
• Big Data Analytics in Medical Services
• Business Intelligence and Case Studies

The Online Ph.D. Programs Student Guide explains doctoral programs in detail.

Areas of Specialization in Ph.D. Data Science Online Programs

Specializing in data science can lead to niche jobs and better pay. Ph.D. in data science online students should consider picking a concentration that aligns with their academic or research interests and professional goals. Here are a few concentration options found in many data science colleges across the country:

Why should I do a Ph.D. Data Science Online?

Students who complete a Ph.D. in data science online program learn how to build and design a data science environment where innovations are made in both scientific and social domains. By combining fundamental research on the methodologies and principles of data science, professionals in this field create advanced data science methodologies and transform the process through which many discoveries are made.

Since there is a lot of demand for real-world projects using new kinds of data science methodologies, organizations try to find individuals with advanced degrees in data science. Therefore, students who have completed online Ph.D. data science will readily find employment and be able to develop such models and become leaders in the research field of data science. Alternatively, those interested in education and academia will find that this doctoral degree prepares them for outstanding careers in both.

Upon completing the online Ph.D. data science, individuals will display industry-leading and in-demand skills, such as the ability to:

• show leadership skills in data science research and exhibit utmost professionalism in careers in industry or academia;
• show in-depth knowledge of advances in data availability and computational speed, and develop methods of novel data analysis;
• demonstrate ethical and responsible skills as trained data science professionals to develop methods and statistical tools to find answers to questions in varied disciplines of data science;
• communicate how to extract, analyze, and interpret critical questions from heterogeneous, big, and uncertain data;
• effectively translate insights of fundamental research into practice in the data science field and integrate the results in fields like medicine, industry, science, and governmental programs;
• actively participate in organizations of data science, and assist other individuals working in different domains by providing insights from data science,
• carry out research that is novel and original to be able to publish them in esteemed scientific and technical journals.

Free Courses for Ph.D. Data Science Online Students

Prospective or current Ph.D. students can benefit from some of the many free online courses on data science. These courses could help them understand which areas of data science interest them and which areas require more research and exploration. Here are some examples of free Ph.D. data science online courses:

How to get into the Ph.D. Data Science Online Program?

Each institution has different requirements for students to get into the data science doctoral program. Across most universities, students who have completed their master’s degree in data science are preferred for candidature to Ph.D. programs, although some do not have this requirement. Since data science is a very versatile subject, students with diverse profiles are encouraged to apply to the program. Some of the standard online Ph.D. data science admission requirements at most schools are:

• Online application form and fee
• Academic transcripts from universities attended before
• Statement of purpose
• Letters of Recommendation (usually 3 are required)
• Scores of standardized tests, like GRE scores
• TOEFL or IELTS scores for non-native English speakers
• Curriculum Vitae or Resume (highlighting relevant experience in data science)
• Writing sample (required for some programs)

Ph.D. Data Science Online No GRE Programs

The Graduate Record Examination or GRE scores assess whether students can independently complete their coursework while pursuing their Ph.D. program. Although the GRE is a common requirement at many colleges, some offer online Ph.D. data science no GRE programs. A few universities, like the NYU Center for Data Science and Yale University , may waive the requirement of the GRE under specific conditions or make it optional, where the student can submit their scores by choice.

How Long does it take to complete a Ph.D. in Data Science Online?

The average time it would take students to finish a Ph.D. in data science online is between 5 to 7 years. While some programs can be completed within 4 years in an accelerated format, others could take up to 6 years to complete if the degree is not pursued full-time. Regardless of the timeframe, Ph.D. students are required to do detailed coursework and research work. Here is a quick timeline overview of data science Ph.D. programs in the USA:

• Minimum time required for a full-time Ph.D. in data science: about 4 years
• Maximum time required for a Ph.D. in data science: up to 7 years
• Typical time required to complete a Ph.D. in data science: about 5 to 7 years

Accelerated Ph.D. Data Science Online Programs

Accelerated programs at the Ph.D. level can help students earn their degrees in lesser time than the usual 5 to 7 years. By opting for an accelerated Ph.D. data science online degree, students can complete their research and doctoral coursework in four years or less. However, finishing the dissertation and fieldwork could take an additional year or more, depending on the research topic and the student’s efforts. To fast-track their doctoral studies, students can also begin their Ph.D. coursework while pursuing their master’s programs. At some colleges, a master’s thesis can be upgraded and continued during the research work of a Ph.D.

Accreditations for Ph.D. Data Science Online Programs

Accreditation is a process that ensures that institutions and programs meet an expected setup by independent accrediting agencies. Accreditation is a seal of trust that demonstrates to students and the industry that the education offered is of excellent quality and substance. The Data Science Council of America offers programmatic accreditation for data science programs. In place of programmatic accreditation, students would do well to look for accredited Ph.D. data science online colleges, usually certified by six regional accrediting agencies in the U.S.:

• New England Association of Schools and Colleges ( NEASC )
• Middle States Commission on Higher Education ( MSCHE )
• Higher Learning Commission ( HLC )
• Southern Association of Colleges and Schools Commission on Colleges ( SACSCOC )
• Northwest Commission on Colleges and Universities ( NWCCU )
• Western Association of Schools anad Colleges (WASC) Senior College and University Commission ( WSCUC )

The Accreditation Guide explains accreditation and its importance in detail.

How to Pay for a Ph.D. Data Science Online Program?

Ph.D. programs could be very beneficial for students in advancing their careers. However, the cost of these programs could average about $34,000 per year. Due to the high cost of tuition fees and other additional expenses, students might be hesitant to pursue a doctoral degree. While some students may take up funded research projects, part-time work, or full-time work to fund their education, others can explore some of the other financial aid options available:

Scholarships

Scholarships are financial awards to students depending on their academic performance. They can also be given for extracurricular activities such as sports, music, and art. The best part about scholarships is that students are not required to repay the sum received.

Organizations such as educational institutions, non-profit organizations, and state and federal governments can provide grants to students. Grants can be given to students based on their financial needs rather than merit.

Graduate Assistantships

A graduate assistantship allows students to build valuable contacts in their disciplines while pursuing their studies. Graduate assistants support faculty members, departments, or other departments within the institution in teaching undergraduate students and undertaking other academic activities. In exchange for their services, their tuition fees are reduced or waived.

Private student loans

Students can obtain educational loans with interest from private institutions, national banks, and other financial entities. If it is difficult to repay the loan on time, one could look for grants or donors to help them pay off their debts.

The Free Application for Federal Student Aid, or FAFSA, is a form used to evaluate if a student is eligible for federal financial aid or can receive funds from an institution. To learn more, consult the FAFSA guide .

To understand other funding options and how to pay for college , read the financial aid guide .

Fully-Funded Ph.D. Data Science Online Programs

Full-funded full-time students receive full tuition reimbursement and an annual stipend or pay for the 5 to 6 years they spend obtaining their doctorate. Funding is typically granted in exchange for graduate assistantships in teaching and research relating to one’s program. Here are some examples of universities that provide fully funded Ph.D. data science online:

FAQs Related to the Ph.D. Data Science Online Program

How many credits would you need for a ph.d. degree in data science.

Students usually require around 71 credits to graduate from a Ph.D. in data science, of which about 24 are typically for core courses, 18 for the methodology and research courses, 18 for specialization, and 30 for the dissertation. However, these figures vary by institution and program.

What is the data science Ph.D. degree salary?

Data science is a high-paying field, and with an advanced degree like a Ph.D. in data science, students could easily land high-paying, senior roles across various sectors. The U.S. Bureau of Labor Statistics reports $100,901 as the average median annual salary for data scientists as of 2021.

What are the employment opportunities in the data science field?

Data scientists could work in multiple roles in different fields like marketing and advertising, language processing, big data analytics, database systems, and more.

What skills will students learn from a Ph.D. degree in data science?

Online Ph.D. students in data science are taught how to make challenging decisions in the field of data science. They can make client-oriented judgments for organizations rather than solely research-oriented decisions. They can study trends and develop models compatible with current technologies using scientific processes, systems, and algorithms to extract valuable data and insights from structured and unstructured data.

Is a Ph.D. in data science online worth it?

Getting a data science Ph.D. online can be worth it for students interested in statistics, math, and computers who have analytical minds. With almost every business requiring people to manage and analyze data for insights and better decision-making, it is easy to find senior roles and high-paying jobs, making the degree worth it.

How long does a Ph.D. in data science online take?

An online Ph.D. data science program could take about 5 to 7 years to complete if completed full-time. The duration of the program hinges on factors such as coursework, the research topic, the extent of research, and one’s time commitment.

How much does it cost to get a data science Ph.D.?

Unverified data pegs the range of a Ph.D. data science online tuition from $1,300 to $2,000 per credit. In total, students could spend up to $150,000 throughout their Ph.D. studies.

Career Opportunities and Salaries after a Ph.D. Data Science Online

With the advancement in technology, the demand for data science experts has been increasing rapidly. The U.S. Bureau of Labor Statistics reports a job growth of 36% between 2021 and 2032, which is much faster than average . Professionals working in varied sub-fields of data science often see increasing opportunities in various industries, spanning education and healthcare to aviation and logistics. Below are some career opportunities available to data science Ph.D. online students, along with their median annual pay and expected job growth:

Certifications and Licensing for Ph.D. Data Science Online Students

Professional certifications provide students with qualifications that indicate their ability to provide quality services while maintaining high levels of professionalism. Certifications improve one’s prospects of employment and greater compensation by demonstrating to employers and the industry that one is an expert in a specific area of data science. Students pursuing an online Ph.D. in data science should consider credentials from various professional organizations to improve their career growth and prospects. Here are some examples to explore:

Cloudera Certified Associate Data Analyst

The Cloudera Certified Associate Data Analyst certification helps students become SQL Developers to effectively use Cloudera’s CDH platform to generate reports using Hive and Impala. This certification will help students advance their careers as certified data scientists using SQL.

Data Science Council of America Senior Data Scientist

This certification covers a wide range of technical skills that help a professional understand data better and drive organizational success. It provides capabilities to individuals for developing, driving, and designing integrated data and building business intelligence ecosystems.

Principal Data Scientist

This certification equips individuals to understand data analytics and intelligence and how those principles will help them compete and grow businesses. It will help advance their careers and build up their stature as high-worth leaders in technology.

Data Scientist-Advanced Analytics

In this certification, students learn about practical aspects that enable them to participate in big data analytics projects effectively. Candidates also learn about the role data scientists play in analyzing and exploring data, building statistics, and evaluating theories and methods of advanced data analytics.

Licensing in the data science sector may be determined by employer requirements, educational qualifications, and a person’s level of expertise. Although most private employers do not require licensure, governmental and military entities do, which is obtained through training, certification, and exams provided by them.

Additional Resources for Ph.D. Data Science Online Students

Joining professional organizations provides students with numerous options to advance their careers in data science. By utilizing the many resources provided by these organizations, students pursuing data science Ph.D. online programs will be able to gain in-depth knowledge, upskill, and interact with a variety of industry specialists. They can also get grants and scholarships, networking opportunities, career advice, etc. Here are a few organizations offering all this and more:

• U.S. Bureau of Labor Statistics – Occupational Outlook for Data Scientists

Author Bio:

Disclaimer:.

The average tuition (based on degree type for in-state students), average graduation rates, and rankings are based on data from various sources, including the Integrated Postsecondary Education Data System (IPEDS), and are variable over time. All rankings and statistics are subject to change. The rankings are solely the opinion of Find Best Degrees (FBD) and are based on our  proprietary methodology . They do not represent the views of the institutions or organizations mentioned, nor do they represent any official government census or survey. Furthermore, any views or opinions expressed on this page are of FBD’s researchers and teams. Unless otherwise specified, they do not represent the thoughts and opinions of the individuals, institutions, or organizations mentioned. This page’s content is provided solely for informational purposes, with data drawn from various sources, including IPEDS. FBD and its employees make no guarantees regarding the accuracy or completeness of any information found on this page or by following any link. FBD will not be held liable for any errors or omissions in this material nor any losses, injuries, or damages resulting from the exposure or use of this information. Although the information on this page is/was correct at the time of publication, readers should exercise caution because some or all of the provided information may have changed over time, potentially resulting in inaccuracies. For more information, please read our  Terms of Service . Trademarks and logos are the property of their registered owners.

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Information Science Ph.D. With a Concentration in Data Science

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We're so glad you're interested in UNT! Let us know if you'd like more information and we'll get you everything you need.

Why Earn an Information Science Ph.D. With a Concentration in Data Science?

The UNT Information Science Ph.D. program with a concentration in Data Science responds to the varied and changing needs of an information age, increasing recognition of the central role of information and information technologies in individual, social, economic, and cultural affairs.

The mission of UNT's Information Science (IS) Ph.D. program is to provide a center of excellence in graduate education and research. Its primary goals are to:

• Nurture critical and reflective thinking on the fundamental issues and elements of problems of utilization of information
• Foster an environment of substantive and productive mentoring and apprenticeship
• Prepare scholars passionate about the role of information in human affairs
• Foster cross-disciplinary thinking and research

Students are recruited to the program from a wide range of disciplines and encouraged to expand and refocus their expertise and skills in cutting-edge areas of information science that cross disciplinary boundaries. The multifaceted nature of information science warrants the focusing of resources, courses, and faculties from a broad range of academic units.

• Research and publication
• Pedagogical practices
• Critical thinking
• Leadership ability
• Data analysis

Information and Data Science Ph.D. Highlights

What can you do with an information science ph.d. with a concentration in data science, information and data science ph.d. courses you could take.

Learn More About UNT

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Linguistics Information Science Ph.D.

Health Informatics Information Science Ph.D.

It’s easy to apply online. Join us and discover why we’re the choice of nearly 47,000 students.

• Mission and Goals
• DEI Commitment and Resources
• In Memoriam
• The Halıcıoğlu Challenge
• 5-Year Report
• Administration
• Visiting Scholars
• Founding Faculty
• Artificial Intelligence and Machine Learning
• Biomedical Data Science
• Data Infrastructure and Systems
• Data Science for Scientific Discovery
• Data and Society
• Theoretical Foundations of Data Science
• Visiting Scholar Program

MS / PhD Admissions

• MSDS Course Requirements
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• PhD Student Resources
• Research Rotation
• Spring Evaluation Requirements
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• Prospective Double Majors
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• Partnership Programs
• Research Collaboration
• Access to Talent
• Professional Development
• UCTV Data Science Channel
• Alumni Relations
• Giving Back

Give us a call or drop by anytime, we endeavor to answer all inquiries within 24 hours.

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[email protected] / [email protected]

Phone support

Phone: + (066) 0760 0260 / + (057) 0760 0560

Application Checklist

Applications to HDSI graduate programs must be submitted online using UCSD’s Application for Graduate Admission . 

Statement of Purpose

An applicant’s statement of purpose is very important and is given careful consideration in the selection process. For additional guidelines, please refer to  Statement of Purpose . There is no specific word limit, but be concise and specific in preparing your statement, giving information that demonstrates your level of preparation and potential for success in graduate school.The statement of purpose must be submitted online through the  UCSD Application for Graduate Admission .

NOTE : Requests to update the Statement of Purpose  are not granted  after submission. Please carefully double-check edits prior to official submission.

An applicant’s Curriculum Vitae or Resume is used to gain a better understanding of the individual’s potential for success in this program. It should highlight details such as academic history, relevant work experience, skills, publications, honors/awards, etc. A resume or curriculum vitae should be uploaded to the   UCSD Application for Graduate Admission .

Three Letters of Recommendation

Three (3) letters of recommendation are required. Additional letters may be submitted, however, you must submit a minimum of three (3) letters.

LORs are submitted via the online application only. For additional information about the procedures and policies for letters of recommendation, please review the UCSD Graduate Division guidelines at  Procedures for Letters of Recommendation .

Transcripts

Unofficial transcripts should be uploaded online to the  UCSD Application for Graduate Admission . More information about how to upload unofficial transcripts can be found on this website:  Academic Records and Transcripts

Official transcripts from all prior higher education institutions attended will be required  AFTER  an applicant receives their official offer of admission from UCSD. Applicants who attended any campus of the University of California, including UCSD, must provide official transcripts of the UC coursework. Transcripts from UCSD may be ordered by an applicant from the Office of the Registrar. There is no charge for UCSD transcripts of record sent to departments in support of an application for graduate student.

Test Score Reports

Applicants for Fall 2024 Admission to the Data Science Graduate Programs (MS and PhD) will not need to provide a GRE score with their applications for Admission.

If applicants have already completed the GRE and would like to include their scores with their application, they can include these scores when submitting the application for review. Applicants with valid test scores should request that ETS submit the scores directly to the UC San Diego institution code 4836 ; the department codes are not needed. Information about the GRE is available from the  Educational Testing Service  (ETS) website.

English Language Proficiency 

Demonstrated proficiency in the English Language is required for all international applicants whose native language is not English.

UC San Diego accepts both the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) scores.  To learn more about the minimum score requirements and possible exemptions, please visit UC San Diego’s English Language Proficiency  website.

Students may us the following Institution code when submitting official scores:  4836 . The Department code can be left blank.

Application Fee

Applicants will be required to pay the application fee for each application submitted or to request a fee waiver at the end of the online application. Your application cannot be reviewed until this fee is paid.

• US Citizens, Permanent Residents, and Undocumented Applicants: $135.00
• International Applicants: $155.00

Applicants may submit multiple applications for the same term. Please note that each application must be submitted to a different department and to only one degree level (Master or PhD) per department.

For information regarding fee waivers, please review the  Graduate Division  website.

After You Apply

• The HDSI admissions team reviews applications as they are received. There is no need to follow up with the admissions team, you will receive communication regarding your status by March 2023.

Dates and Deadlines: Fall 2024 Cohort

• Application submission deadline: December 15, 2023
•   *PhD DELAY*  PhD applicants can expect to receive offer letters by April 15, 2024.
• **PhD DELAY**   Admitted PhD students will have until May 1, 2024 to submit their decisions.
• Mandatory Orientations take place before instruction begins and are announced to admitted students.

In order to provide the tools and resources needed to achieve academic, professional, and personal development goals, the Data Science program has a committed staff available to support students and their needs.

If you have any questions or concerns regarding admission, please reach out to our team via [email protected] .

Laura Horton

Responsible for graduate student admissions and recruitment.

Back up: Julia Nemeth

What are the requirements for admission to the program?

Our program does not have specific prerequisite courses or undergraduate degree requirements for admission. Our aim is to build a diverse cohort with a range of expertise and interests.

More information regarding the courses required once admitted can be found on our MS Program Requirements and PhD Program Requirements webpages.

What is the cost of attendance for a graduate program at UCSD?

The tuition and fees breakdown for the in-person MS and PhD programs can be found on the UCSD Graduate Division Tuition & Fees webpage.

Are the in-person MS and PhD programs in Data Science STEM designated?

Yes, Our in-person graduate degrees are STEM designated.

For more information about the designation or OPT Extensions, please visit the UCSD Graduate Division STEM OPT Extension webpage.

I am having trouble submitting part of my application, who should I contact?

If you are a current or former UCSD student, you will not be able to submit your application online. Please make sure your application is complete then email HDSI’s Graduate Team ( [email protected] ) so that our staff can submit your materials for review.

For non-UCSD applicants, please reach out to the UCSD Graduate Division office via email ( [email protected] ) or by visiting their website for more contact information.

I am a current undergraduate student at UCSD, how do I apply to a graduate program in Data Science?

I am a current graduate student at ucsd, how do i change my program of study to data science, are gre scores required.

No, GRE scores are not required for the Fall 2024 cohort applications.

Please check back on this website for GRE requirements in future academic years.

I am an international student with a degree from an international institution where English was the primary language of instruction. Do I need to submit TOEFL/IELTS scores?

Non-native english speakers may be exempt from the English Proficiency Requirement (TOEFL/IELTS) if they earned or will be earning a bachelor’s, master’s, or doctoral degree with grades of B (3.0) or better from either

• A regionally accredited U.S. college or university where English is the sole language of instruction, or
• A foreign college or university which provides instruction solely in English. You may verify whether your institution meets this requirement by looking up your institution in the IAU World HIgher Education Database (WHED).

More information can be found on the UCSD Graduate Division English Language Proficiency webpage.

Home › Stories › Different by Design, Distinctly Notre Dame: The Online Master’s in Data Science

Different by Design, Distinctly Notre Dame: The Online Master’s in Data Science

At a place as steeped in residential life and campus traditions as the University of Notre Dame, the considerations around creating the University’s first online degree program were never going to be just technological.

Was there a defined educational goal that would be difficult to meet using traditional tools? Would online students feel connected to the University? Could you deliver an authentic Notre Dame experience to students participating in the program from locations around the world?

It was these types of questions that those involved in creating the online master’s in data science had to ask themselves before announcing it in August 2016. The answer to all these questions, they thought at the time, was yes.

Seven and a half years later, there is no doubt.

“From the outset, we recognized that the online master’s would need to be purposefully crafted for an online environment,” said Roger Woodard , a teaching professor in the Department of Applied & Computational Mathematics & Statistics (ACMS) and director of the program. “By embracing the distinct challenges working professionals face, our faculty and staff deliver a program that imparts rigorous content knowledge but also fosters a genuine sense of connection people expect of a Notre Dame experience.”

Built by Notre Dame Learning ’s Office of Digital Learning (ODL), the online master’s was envisioned by the University’s Office of the Provost and College of Science in collaboration with AT&T, which provided a generous grant to help launch the program and continues to partner in its delivery. The idea was to give highly driven individuals already pursuing careers an opportunity to advance their skills in data science in response to industry need.

Add the rapidly expanding use of AI to the continued growth in big data, and the demand for professionals with those skills has only increased since the program’s founding.

Housed in ACMS in the College of Science, the online master’s in data science is delivered over 21 months at a half-time pace. This allows students to engage more deeply with their coursework, their professors, and their peers through both asynchronous (i.e., on-demand) and live sessions.

There are also exclusive immersion weekends that bring students and faculty together for study and networking as well as a capstone course that connects students with corporate partners to solve a data question by building AI and data science solutions.

If this sounds like the product of deliberate and intentional planning, that is no accident. In addition to members of ACMS, the program’s architects included faculty from the Department of Computer Science and Engineering , the Mendoza College of Business , and the Department of Psychology . They worked with the ODL, which led learning design and media production efforts, and industry experts from AT&T to shape an online program that would meet the high standards of a Notre Dame degree.

The program has its own dedicated learning designer, Jade Liggett, who ensures pedagogical best practices continue to be reflected in the way its courses are organized and delivered. Associate Director Samantha Adamczewski serves as students’ 24/7 link to Notre Dame, working with them from the time they confirm their enrollment, through their studies, and then as their alumni contact.

“The level of personalized support Samantha provides every student makes this program truly exceptional in a crowded market space,” said Sonia Howell , the director of the ODL.

This May will mark the graduation of the sixth class of Notre Dame students to earn the online master’s in data science. Almost all online students attend and participate in the University’s in-person commencement ceremony, where they get to walk across the stage to receive their Notre Dame degree.

Commencement is not the first time they come to campus, though, with students traveling to Notre Dame for orientation in the fall of their first year. As for the immersion weekends, those are held in various places across the country.

It all adds up to alumni who, to borrow a phrase from the University’s strategic framework , are prepared to be “the humane, informed leaders the world needs,” no matter what time zone they call home.

“When I started the data science program in 2018, I expected to have great professors, challenging classes, and a supportive cohort,” said Annemarie Colino, lead data scientist at AT&T. “However, with a part-time, online program, it was a nice surprise to really feel like a part of the University community. I know the kickoff weekend played an important part: being on campus, learning through trivia games, getting our IDs, going on the football field. More importantly, the Notre Dame mission to ‘Be a force for good’ is woven through the curriculum, and I always felt like I was engaged in education, not just getting through a class.

“Thank you again for creating and leading a program that not only made me a data scientist but a Domer, too.”

Click the links to learn more about the online masters in data science program and Notre Dame Learning.

This article was a collaboration between Notre Dame Learning’s Ted Fox and Notre Dame Data Science. 

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Language Technologies Institute

School of computer science.

Master of Computational Data Science

The Master of Computational Data Science (MCDS) program focuses on engineering and deploying large-scale information systems, and includes concentrations in Systems, Analytics, and Human-Centered Data Science.

Requirements

The MCDS program offers three majors: Systems, Analytics, and Human-Centered Data Science. All three require the same total number of course credits, split among required core courses, electives, data science seminar and capstone courses specifically defined for each major. The degree can also be earned in two different ways, depending on the length of time you spend working on it. Regardless of the timing option, all MCDS students must complete a minimum of 144 units to graduate.

Here are the options:

• Standard Timing — a 16-month degree consisting of study for fall and spring semesters, a summer internship, and fall semester of study. Each semester comprises a minimum of 48 units. This timing is typical for most students. Students graduate in December.
• Extended Timing — a 20-month degree consisting of study for fall and spring semesters, a summer internship, and a second year of fall and spring study. Each semester comprises a minimum of 36 units. Students graduate in May.

Core Curriculum

All MCDS students must complete 144 units of graduate study which satisfy the following curriculum:

• Five (5) MCDS Core Courses (63 units)
• Three courses (3) from one area of concentration curriculum (36 units)
• Three (3) MCDS Capstone courses (11-635, 11-634 and 11-632) (36 units)
• One (1) Electives: any graduate level course 600 and above in the School of Computer Science (12 units)

Area of Concentration

• During the first two semesters in the program, all students take a set of five (5) required core courses: 11-637 Fundamentals of Computational Data Science, 15-619 Cloud Computing, 10-601 Machine Learning, 05-839 Interactive Data Science, and 11-631 Data Science Seminar.
• By the end of the first semester, all students must select at least one area of concentration — Systems, Analytics, or Human-Centered Data Science — which governs the courses taken after the first semester.
• To maximize your chances of success in the program, you should consider which concentration area(s) you are best prepared for, based on your educational background, work experience, and  areas of interest as described in your Statement of Purpose.
• You are strongly encouraged to review the detailed curriculum requirements for each concentration area, in order to determine the best fit given your preparation and background.

For a complete overview of the MCDS requirements read the  MCDS Handbook .

To earn an MCDS degree, students must pass courses in the core curriculum, the MCDS seminar, a concentration area, and electives. Students must also complete a capstone project in which they work on a research project at CMU or on an industry-sponsored project.

In total, students must complete 144 eligible units of study, including eight 12-unit courses, two 12-unit seminar courses, and one 24-unit capstone course. Students must choose at minimum five core courses. The remainder of the 12-unit courses with course numbers 600 or greater can be electives chosen from the SCS course catalog. Any additional non-prerequisite units taken beyond the 144 units are also considered electives.

Students who plan to select the Systems concentration may wish to enroll in 15-513 “Introduction to Computing Systems” during the summer session preceding their enrollment in the program; this course is a prerequisite for many advanced Systems courses, so it should be completed during Summer if you wish to enroll in advanced Systems courses in the Fall.

Click here   to see the MCDS Course Map.

Some example courses of study are included below.

Example 1: Analytics Major, 16 Months

Example 2: Systems Major, 16 Months

Example 3: Human-Centered Data Science Major, 16 Months

Carnegie Mellon's School of Computer Science has a centralized  online application process . Applications and all supporting documentation for fall admission to any of the LTI's graduate programs must be received by the application deadline. Incomplete applications will not be considered.  The application period for Fall 2024 is now closed. Information about the Fall 2025 admissions cycle will be available in summer 2024.

Application Deadlines

Fee Waivers

Fee waivers may be available in cases of financial hardship, or for participants in select "pipeline" programs. For more information, please refer to the  School of Computer Science Fee Waiver page .

The School of Computer Science requires the following for all applications:

• A GPA of 3.0 or higher.
• GRE scores: These must be less than five years old. Our Institution Code is 2074; Department Code is 0402. (This requirement is waived for CMU undergrads.)
• The GRE At Home test is accepted but we prefer you take the GRE at a test center if possible.
• Unofficial transcripts from each university you have attended, regardless of whether you received your degree there.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation
• A short (1-3 minutes) video of yourself. Tell us about you and why you are interested in the MCDS program. This is not a required part of the application process, but it is STRONGLY suggested.  
• Proof of English Language Proficiency

Proof of English Language Proficiency: If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo. We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored.

We do not issue waivers for non-native speakers of English. In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university. We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States. No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Applicants applying to MCDS are required to submit scores from an English proficiency exam taken within the last two years. Scores taken before Sept. 1, 2021, will not be accepted regardless of whether you have previously studied in the U.S. For more information about their English proficiency score policies, visit the  MCDS  admission website.  Successful applicants will have a minimum TOEFL score of 100, IELTS score of 7.5, or DuoLingo score of 120. Our Institution Code is 4256; the Department Code is 78. Additional details about English proficiency requirements are provided on the  FAQ  page. 

Applications which do not meet  all  of these requirements by the application deadline (see above) will not be reviewed.

For more details on these requirements, please see the  SCS Master's Admissions page.

In addition to the SCS guidelines, the LTI requires:

• Any outside funding you are receiving must be accompanied by an official award letter.

No incomplete applications will be eligible for consideration.

For specific application/admissions questions, please contact  Jennifer Lucas  or Caitlin Korpus .

Program Contact

For more information about the MCDS program, contact Jennifer Lucas or Caitlin Korpus

Jennifer Lucas

Caitlin korpus, online graduate certificate program, program handbook.

10 free data analytics courses you can take online

Data analytics is the science of taking raw data, cleaning it, and analyzing it to inform conclusions and support decision making. From business to health care to social media, data analytics is changing the way organizations operate.

“It’s not hyperbole to say that data analytics has really taken over the world,” says Brian Caffo, professor of biostatistics at Johns Hopkins University’s Bloomberg School of Public Health and director of academic programs for the university’s Data Science and AI Institute. “Every domain has become increasingly quantitative to inform decision making.”

Berkeley's Data Science Master's

And this space isn’t slowing down anytime soon: The U.S. Bureau of Labor Statistics projects that employment for data scientists will grow 35% from 2022 to 2032, with 17,700 new job openings projected each year on average during that decade. 

Interested in becoming a data analyst? Below, we’ve compiled ten free data analytics courses to help give you a firmer grasp of this rapidly growing field.

A/B Testing  

About: This course covers the design and analysis of A/B tests, which are online experiments that compare two versions of content to see which one appeals to viewers more. A/B tests are used throughout the tech industry by companies like Amazon and Google. This course is offered through Udacity. 

Course length: Six self-paced modules

Who this course is for: Beginners

What you’ll learn: In this course you’ll learn about A/B testing, experiment ethics, how to choose metrics, design an experiment, and analyze results.

Prerequisites: None  

Data Analytics Short Course  

About: In this quick, five-tutorial course you’ll get a broad overview of data analytics. You’ll learn about the different types of roles in data analytics, a summary of the tools and skills you’ll need to develop, and a hands-on introduction to the field. This course is offered by CareerFoundry.

Course length: 75 minutes, divided into five 15-minute lessons

What you’ll learn: In this course you’ll get an introduction to data analytics. You’ll also analyze a real dataset to solve a business problem through data cleaning, visualizations, and garnering final insights.

Prerequisites: None 

Data Science: R Basics  

About: This program gives you a foundational knowledge of programming language R. Offered by HarvardX through the EdX platform, this course is offered for free; the paid version includes a credential. It’s the first of ten courses HarvardX offers as part of its Professional Certificate in Data Science.

Course length: Eight weeks, 1–2 hours per week

What you’ll learn: In this course you’ll learn basic R syntax and foundational R programming concepts, including data types, vectors arithmetic, and indexing. You’ll also perform operations that include sorting, data wrangling using dplyr, and making plots. 

“It’s the basics of how to wrangle, analyze, and visualize data in R,” says Dustin Tingley, Harvard University’s deputy vice provost for advances in learning and a professor of government in the school’s government department. “That gets you writing a little bit of code, but you’re not doing anything that heavy.”

Prerequisites: HarvardX recommends having an up-to-date browser to enable programming directly in a browser-based interface 

Fundamentals of Qualitative Research Methods  

About: This course will teach you the fundamentals of qualitative research methods. Qualitative research provides deeper insights into real-world problems that might not always be immediately evident. This course is offered through Yale University on YouTube.

Course length: 90 minutes spread out over six modules

What you’ll learn: In this course you’ll learn how qualitative research is a way to systematically collect, organize, and interpret information that is difficult to measure quantitatively. This includes developing qualitative research questions, gathering data through interviews and focus groups, and analyzing this data. 

“Qualitative research is the systematic, rigorous application of narratives and tools to better understand a complex phenomenon,” says Leslie Curry, a professor of public health and management at the Yale School of Public Health and a professor of management at the Yale School of Management. She adds that this approach can help understand flaws in large data sets. “It can be used as an adjunct to a lot of the really important work that’s happening in large data analysis.”

Getting and Cleaning Data  

About: This course covers the basic ways that data can be obtained and how that data can be cleaned to make it “tidy.” It will also teach you the components of a complete data set, such as raw data, codebooks, processing instructions, and processed data. This course is offered by Johns Hopkins University through Coursera, and is part of a 10-course Data Science Specialization series.

Course length: Four weeks, totaling approximately 19 hours

What you’ll learn: Through this course you’ll learn about common data storage systems, how to use R for text and date manipulation, how to use data cleaning basics to make data “tidy,” and how to obtain useable data from the web, application programming interfaces (APIs), and databases. 

“It’s the starting point” when it comes to data analysis, Caffo says. “Without a good data set that is cleaned and appropriate for use, you have nothing. You can talk all you want about doing models or whatnot—underlying that has to be the data to support it.”

Prerequisites: None

Introduction to Data Science with Python  

About: This course teaches you concepts and techniques to give you a foundational understanding of data science and machine learning. Offered by HarvardX through the EdX platform, this course can be taken for free. The paid version offers a credential.

Course length: Eight weeks, 3–4 hours a week

Who this course is for: Intermediate

What you’ll learn: This course will give you hands-on experience using Python to solve real data science challenges. You’ll use Python programming and coding for modeling, statistics, and storytelling. 

“It gets you up and running with the main workhorse tools of data analytics,” says Tingley. “It helps to set people up to take more advanced courses in things like machine learning and artificial intelligence.”

Prerequisites: None, but Tingley says having a basic background in high school-level algebra and basic probability is helpful. Some programming experience—particularly in Python—is recommended 

Introduction to Databases and SQL Querying  

About: In this course you’ll learn how to query a database, create tables and databases, and be proficient in basic SQL querying. This free course is offered through Udemy.

Course length: Two hours and 17 minutes

What you’ll learn: This course will acquaint you with the basic concepts of databases and queries. This course will walk you through setting up your environment, creating your first table, and writing your first query. By the course’s conclusion, you should be able to write simple queries related to dates, string manipulation, and aggregation.

Introduction to Data Analytics  

About: This course offers an introduction to data analysis, the role of a data analyst, and the various tools used for data analytics. This course is offered by IBM through Coursera.

Course length: Five modules totaling roughly 10 hours 

What you’ll learn: This course will teach you about data analytics and the different types of data structures, file formats, and sources of data. You’ll learn about the data analysis process, including collecting, wrangling, mining, and visualizing data. And you’ll learn about the different roles within the field of data analysis.

Learn to Code for Data Analysis  

About: This course will teach you how to write your own computer programs, access open data, clean and analyze data, and produce visualizations. You’ll code in Python, write analyses and do coding exercises using the Jupyter Notebooks platform. This course is offered through the United Kingdom’s Open University on its OpenLearn platform.

Course length: Eight weeks, totaling 24 hours

What you’ll learn: In this course you’ll learn basic programming and data analysis concepts, recognize open data sources, use a programming environment to develop programs, and write simple programs to analyze large datasets and produce results.

Prerequisites: A background in coding—especially Python—is helpful  

The Data Scientist’s Toolbox  

About: This course will give you an introduction to the main tools and concepts of data science. You will learn the ideas behind turning data into actionable knowledge and get an introduction to tools like version control, markdown, git, GitHub, R, and RStudio. This course is offered by Johns Hopkins University through Coursera, and is part of a 10-course Data Science Specialization series.

Course length: 18 hours

What you’ll learn: This course will teach you how to set up R, RStudio, GitHub, and other tools. You will learn essential study design concepts, as well as how to understand the data, problems, and tools that data analysts use. 

“That course is a very accessible introduction for anyone who wants to get started in this,” Caffo says. “It’s an overview that covers the full pipeline, from things like collecting and arranging data to asking good questions, all the way to creating a data deliverable.”

The takeaway  

From businesses estimating demand for their products to political campaigns figuring out where they should run advertisements to health care professionals running clinical trials to judge a drug’s efficacy, data analytics has a wide variety of applications. Getting a better understanding of the field on your own time can be done easily and freely. And the field is only growing.

“Just about every field is having a revolution in data analytics,” Caffo says. “In fields like medicine that have always been data driven, it’s become more data-driven.”

Syracuse University MS in Applied Data Science Online

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Surfacing race and gender representations in online reading materials in a k-12 digital learning platform using nlp..

Richard (Chenming) Tang

Online learning resources provide multiple advantages to education stakeholders, but prior research has shown that there exists significant disparity in how characters from diverse racial, ethnic, and gender backgrounds are portrayed. This study surfaces race and gender representation from learning materials used in a popular K-12 literacy development platform using natural language processing techniques. Results from topic modeling and word embeddings show that 1) there is some degree of gender disparity in the topics men and women are associated with but not so much difference in the contexts they are mentioned in, 2) there is a large degree of racial disparity in the topics certain race groups are associated with and the contexts they are mentioned in, and 3) certain race groups are associated with certain topics and contexts more often than others. Overall, this study contributes to a more nuanced understanding of gender and race representation in digital native learning platforms, demonstrates the affordances of NLP techniques in educational research, and highlights the importance of culturally responsive education in the digital age.

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