best universities for phd in data science

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

View Course Offering

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

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

Get the latest news.

Subscribe to receive updates from the School of Data Science.

• Prospective Student
• School of Data Science Alumnus
• UVA Affiliate
• Industry Member

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 .

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 .

Best Universities for Data Science in the World

Updated: February 29, 2024

• Art & Design
• Computer Science
• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
• Mathematics

Below is a list of best universities in the World ranked based on their research performance in Data Science. A graph of 24.8M citations received by 921K academic papers made by 2,336 universities in the World was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. University College London

For Data Science

2. University of Oxford

3. Stanford University

4. Harvard University

5. Massachusetts Institute of Technology

6. University of Michigan - Ann Arbor

7. University of California - Berkeley

8. University of Washington - Seattle

9. University of Wisconsin - Madison

10. University of California-San Diego

11. Carnegie Mellon University

12. Imperial College London

13. University of Toronto

14. University of Illinois at Urbana - Champaign

15. Cornell University

16. University of Bristol

17. Johns Hopkins University

18. University of Minnesota - Twin Cities

19. University of Manchester

20. Pennsylvania State University

21. University of Cambridge

22. University of Maryland - College Park

23. Georgia Institute of Technology

24. University of Illinois at Chicago

25. Columbia University

26. University of British Columbia

27. University of California - Los Angeles

28. University of Melbourne

29. Yale University

30. University of Edinburgh

31. University of Southern California

32. New York University

33. University of Southampton

34. Arizona State University - Tempe

35. Simon Fraser University

36. University of Pennsylvania

37. University of Birmingham

38. University of California - San Francisco

39. University of Texas at Austin

40. Northwestern University

41. University of Sydney

42. King's College London

43. University of Chicago

44. St George's, University of London

45. University of New South Wales

46. Iowa State University

47. University of Pittsburgh

48. University of Sheffield

49. University of North Carolina at Chapel Hill

50. Catholic University of Leuven

51. University of York

52. Rutgers University - New Brunswick

53. University of California - Santa Barbara

54. Ohio State University

55. University of Amsterdam

56. Tsinghua University

57. University of Glasgow

58. University of Liverpool

59. University of Arizona

60. University of California - Irvine

61. Boston University

62. Princeton University

63. University of California - Davis

64. Duke University

65. Swiss Federal Institute of Technology Zurich

66. University of Auckland

67. Texas A&M University - College Station

68. University of Alberta

69. National University of Singapore

70. McMaster University

71. University of London

72. McGill University

73. University of Virginia

74. Newcastle University

75. Monash University

76. Leiden University

77. Virginia Polytechnic Institute and State University

78. Delft University of Technology

79. University of Nottingham

80. University of Leeds

81. University of Utah

82. University of Tokyo

83. University of Florida

84. Federal Institute of Technology Lausanne

85. Vanderbilt University

86. Seattle University

87. University of Waterloo

88. Michigan State University

89. University of Queensland

90. California Institute of Technology

91. University of Hong Kong

92. University of Zurich

93. University of Colorado Boulder

94. Nanyang Technological University

95. Peking University

96. Eindhoven University of Technology

97. University of Calgary

98. University of Notre Dame

99. Purdue University

100. University of Warwick

Computer Science subfields in the World

PhD in Data Science

First Year Requirements

The standard first-year program requires students to complete nine courses: four required courses (1-4 below); one elective either in mathematical foundations or scalability and computing (pick from either 5 or 6); and finally four other electives that can come from proposed courses in data science or existing graduate courses in Computer Science or Statistics. Some students, after consulting with the committee graduate advisor, might decide to take the nine courses over the first two years.

Required courses:

• Foundations of Machine Learning and AI Part 1
• Responsible Use of Data and Algorithms
• Data Interaction
• Systems for Data and Computers/Data Design
• Foundations of Machine Learning and AI Part 2 
• Data Engineering and Scalable Computing

Synthesis project

Students will take courses during the first two years after which they focus primarily on their research. A milestone in this transition is completion of a synthesis project before the end of the second year in the program. Thesis projects can be done in partnership with any of DSI affiliates, and aims to meaningfully connect PhD students to their chosen focus areas.

Thesis Advisor and Dissertation Committee

Students typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student, after consultation with their advisor, shall establish a thesis committee of at least three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science.

Proposal Presentation and Admission to Candidacy

By the end of the third year, students should have scheduled and completed a proposal presentation to their committee, in order to be advanced to candidacy. The proposal presentation is typically an hourlong meeting that begins with a 30-minute presentation by the student, followed by a question and discussion period with the committee.

Dissertation Defense

The PhD degree will be awarded following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

Explore your training options in 10 minutes Get Started

• Graduate Stories
• Partner Spotlights
• Bootcamp Prep
• Bootcamp Admissions
• University Bootcamps
• Coding Tools
• Software Engineering
• Web Development
• Data Science
• Tech Guides
• Tech Resources
• Career Advice
• Online Learning
• Internships
• Apprenticeships
• Tech Salaries
• Associate Degree
• Bachelor's Degree
• Master's Degree
• University Admissions
• Best Schools
• Certifications
• Bootcamp Financing
• Higher Ed Financing
• Scholarships
• Financial Aid
• Best Coding Bootcamps
• Best Online Bootcamps
• Best Web Design Bootcamps
• Best Data Science Bootcamps
• Best Technology Sales Bootcamps
• Best Data Analytics Bootcamps
• Best Cybersecurity Bootcamps
• Best Digital Marketing Bootcamps
• Los Angeles
• San Francisco
• Browse All Locations
• Digital Marketing
• Machine Learning
• See All Subjects
• Bootcamps 101
• Full-Stack Development
• Career Changes
• View all Career Discussions
• Mobile App Development
• Cybersecurity
• Product Management
• UX/UI Design
• What is a Coding Bootcamp?
• Are Coding Bootcamps Worth It?
• How to Choose a Coding Bootcamp
• Best Online Coding Bootcamps and Courses
• Best Free Bootcamps and Coding Training
• Coding Bootcamp vs. Community College
• Coding Bootcamp vs. Self-Learning
• Bootcamps vs. Certifications: Compared
• What Is a Coding Bootcamp Job Guarantee?
• How to Pay for Coding Bootcamp
• Ultimate Guide to Coding Bootcamp Loans
• Best Coding Bootcamp Scholarships and Grants
• Education Stipends for Coding Bootcamps
• Get Your Coding Bootcamp Sponsored by Your Employer
• GI Bill and Coding Bootcamps
• Tech Intevriews
• Our Enterprise Solution
• Connect With Us
• Publication
• Reskill America
• Partner With Us

• Resource Center
• Bachelor’s Degree
• Master’s Degree

Best Doctorates in Data Science: Top PhD Programs, Career Paths, and Salaries

If you are tech-savvy and like to stay up-to-date on the latest developments in the computing field, you might be interested in getting a data science job. The industry is interdisciplinary, with those working within it proficient in statistics, computer science, and operations research.

As such, this career isn’t pursued casually, as extensive education is required to become a data scientist and enter the industry. While some jobs can be obtained with a master’s degree, earning one of the best PhDs in Data Science is a much better option if you want the freedom to be able to conduct your own research.

Find your bootcamp match

Within your data science PhD studies, you will spend the latter two years of the four-year program doing your own unique research and writing a dissertation on your findings, preparing you to do the same in the real world. Those with a master’s degree, by contrast, don’t have as many creative liberties and don’t usually develop their own research, but rather analyze existing studies.

With the amount of schooling and skills required to work within the field, you might be wondering what a PhD in Data Science Salary looks like. Below, we’ll discuss the top schools for getting a PhD in Data Science, as well as the career outlook once you get your degree.

What Is a PhD in Data Science?

A PhD in Data Science is a four- or five-year, full-time degree pursued after a bachelor’s or master’s degree. Faculty in university PhD programs often like students to have a prior master’s degree, but if not, they might offer integrated master’s and PhD studies.

Within a PhD in Data Science program, you’ll spend the first two years of your program learning foundational knowledge, taking advanced courses in statistics, computer programming, data mining, research methodology, and so much more. The latter two years of the degree involve conducting your own unique research. You will then record your findings in a dissertation and defend your research before a committee to get your doctorate.

How to Get Into a Data Science PhD Program: Admission Requirements

You can get into a Data Science PhD program by meeting a university’s admission requirements, which will differ between each school. Typically, the minimum educational requirement is a bachelor’s degree in a related STEM degree, but most programs prefer a prior master’s degree.

If you don’t have a master’s degree, it is highly recommended to at least be proficient in a coding language and to have taken classes in calculus, statistics, and engineering. You will also need to have a minimum 3.0 GPA across your postsecondary studies and send the school your academic transcripts. There are also supplemental materials you would need for an application. These include a statement of purpose, two or three letters of recommendation, GRE scores, and a professional resume.

PhD in Data Science Admission Requirements

• A postsecondary degree in a related field
• Academic transcripts
• Graduate record examination (GRE) scores
• Coursework in data structures, algorithms, calculus, and linear algebra
• A background in a programming language
• Letters of recommendation
• Admission essays
• Personal statement

Data Science PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Data Science?

It can be hard to get into a PhD program in Data Science. PhD programs within universities are very exclusive. While they receive a sea of applications, most schools only accept about a dozen of them. For example, Yale, one of the best schools in the country, had over 300 applicants but only made around 13 offers. As such, it is wise to apply for multiple PhD programs in order to increase your chances of getting an offer of admission.

How to Get Into the Best Universities

[query_class_embed] how-to-get-into-*school

Best PhDs in Data Science: In Brief

Best universities for data science phds: where to get a phd in data science.

Since data science is a new field of scientific inquiry, it can be difficult to find the best universities for getting a data science PhD. In order to help you on your educational path, we’ve listed the 10 best schools for a data science PhD, below.

Arizona State University (ASU) was ranked the nation's most innovative university by US News & World Report. Originally founded in 1885, ASU has grown to now offer more than 160 programs at the graduate level in everything from art to engineering. The graduate school is well-known for its research work.

PhD in Data Science, Analytics, and Engineering

This program is geared toward those who want to work in either the data science industry, academia, or government to solve real-world problems through data-informed methods. The 12 credits of core courses within this program include data mining, statistics, security and assurance of information, and database management. 

If you want to focus on engineering, you’ll take nine credits across cloud computing, database systems, and databases for web and other multimedia. If you want to focus on analytics, you'll take Machine Learning Statistics, Regression Analytics, and Data Visualization. 

As a culmination of your studies, you’d produce a thesis. This requires you to propose a topic of study to the dissertation supervisory committee, and upon passing their comprehensive exam, you can begin your research. Ten days before you are to defend your dissertation, which must come less than a year after completing your 60th credit, you’ll submit a version of it to the committee for review.

PhD in Data Science, Analytics, and Engineering Overview

• Program Length: 4-6 years
• Acceptance Rate: 88% (school acceptance rate)
• Tuition: $11,720/year (in state); $23,544/year (out of state)
• PhD Funding Opportunities: Awards, fellowships, and scholarships

PhD in Data Science, Analytics, and Engineering Admission Requirements

• Application
• Application fee
• Official transcripts 
• Three letters of recommendation 
• Letter of intent  
• GRE scores 

Bowling Green State University (BGSU) offers more than 20 PhD programs in a variety of disciplines including engineering, psychology, business, and music. Since its beginnings in 1910, BGSU has been noted for its engineering and scientific research, being one of eight universities in the nation with the Carnegie Foundation’s Community Engagement Classification.

PhD in Data Science

This research-oriented program is interdisciplinary, incorporating teachings from applied statistics, operations research, and computer science. A unique aspect of BGSU’s program is that you’ll need to take ethics classes in order to understand the moral ramifications of gathering data and in communications to learn to effectively present their findings. Before beginning your 16- to 30-credit dissertation, you will need to pass the qualifying examination that involves oral and written sections. 

PhD in Data Science Overview

• Program Length: 4-5 years
• Acceptance Rate: 79% (school acceptance rate)
• Tuition and Fees: $523/credit (in state); $856/credit (out of state)
• PhD Funding Opportunities: Assistantships, scholarships, fellowship 
• Payment of $45 application fee
• Minimum GPA of 3.0
• Official or unofficial transcripts from previous institutions 
• Graduate Record Examination (GRE) scores
• Graduate Management Admission Test (GMAT) scores
• Three letters of recommendation
• Resume 

Chapman offers a variety of graduate programs, with 66 master’s and seven doctoral degrees in disciplines like business, law, education, and health sciences. It was founded in 1861 and is known for its research, with more than 31,000 citations from its 5,283 publications. Chapman University is also known for its strong alumni network, which can help graduates find jobs and networking opportunities.

PhD in Computational and Data Sciences

The PhD in computational and data sciences is designed for students who want to work in fields like population genetics, earth systems, biotechnology, bioinformatics, and economic science. The curriculum includes coursework in mathematical modeling, mining data, data analysis, and computational science, as well as research and thesis guidance from faculty. 

The program is structured so that students can specialize in an area of computational science that interests them, such as scientific computing, data science, or machine learning, allowing students to also choose their dissertation topic. Before becoming doctoral candidates, students take qualifying exams for their core curriculum and do presentations on their elective courses. 

PhD in Computational  Data Sciences Overview

• Acceptance Rate: 60% (school acceptance rate)
• Tuition and Fees: $32,400 tuition
• PhD Funding Opportunities: Assistantships, work-study, loans 

PhD in Computational and Data Sciences Admission Requirements

• Proof of satisfactory coursework in computer programming, differential equations, and statistics
• $60 application fee
• Two letters of recommendation 
• 750-word statement of interest

This circa 1889 school offers more than 130 programs at the graduate level, with 1,687 students currently taking on one of its 50-plus doctoral programs. The school has various innovation clusters of research types, including those related to environmental sustainability, innovations in health, data science and cyberinfrastructure, transportation, and advanced manufacturing. 

PhD in Biomedical Data Science and Informatics

This joint program through the college and the Medical University of South Carolina (MUSC) aims to teach students how to remedy issues in medicine through the combined study of information and computer sciences. Courses within the program will cover statistical theory, data management, machine learning, and bioinformatics. 

Students spend the first two years doing coursework, the third year completing professional development training and research electives, and the fourth year solely researching. Research, seminars, and lab rotations will consist of 24 credit hours. Before completion of the program you’ll need to take a qualifying exam alongside proposing, writing, and defending your dissertation. 

PhD in Biomedical Data Science and Informatics Overview

• Acceptance Rate: 49% (school acceptance rate)
• Tuition and Fees: $691/credit (in state); $1491/credit (out of state) 
• PhD Funding Opportunities: Assistantships, scholarships and fellowships

 PhD in Biomedical Data Science and Informatics Admission Requirements

• Bachelor’s degree in a STEM field, with one year of calculus and biology classes
• Graduate record examination (GRE) scores 
• Prior computer programming work experience or coursework
• Work or research experience (recommended) 
• Personal essay
• Two or three letters of recommendation 

This university provides more than 30 doctoral programs just within the College of Science. Something unique to the circa 1949 school—known for its research in physics, immunology, molecular medicine, and biodiversity—is that its staff encourages research teams to incorporate members across various disciplines, bringing the insight and strengths of those respective fields together.

PhD in Computational Sciences and Informatics

Throughout this 72-credit program in the Department of Computational and Data Sciences, you’ll choose two out of four core courses in statistical and scientific visualization, advanced computing, databases, or numerical methods, and then choose from a rotating list of emphasis courses. Emphasis classes might cover topics like knowledge mining, statistical inference, or Bayesian inference decision theory. 

By the end of your first year, you’ll need to obtain a research advisor, then get your proposal approved by the department committee to be considered a candidate for a PhD. A month before defending their dissertations, students conduct a pre-defense to get final revision recommendations. 

"Career Karma entered my life when I needed it most and quickly helped me match with a bootcamp. Two months after graduating, I found my dream job that aligned with my values and goals in life!"

Venus, Software Engineer at Rockbot

PhD in Computational Sciences and Informatics Overview

• Acceptance Rate: 91% (school acceptance rate)
• Tuition and Fees: $12,594/year (in state); $33,906/year (out of state)
• PhD Funding Opportunities: Fellowships, assistantships, lecturer positions, faculty grants, scholarships, work-study 

PhD in Computational Sciences and Informatics Admission Requirements

• Mathematics background
• Knowledge of programming languages such as C, C++, and Python
• Personal statement 

Though Harrisburg only joined the Commonwealth of Pennsylvania in 2001, the private, not-for-profit university has grown to have an enrollment of 4,000 students from over 100 countries. While it only offers three PhD degree programs in data science, computational science, and information systems engineering, it is known for its impressive research in supercomputer datamining, aquaponics, and virtual reality. 

PhD in Data Sciences 

This program strives to teach PhD candidates diverse methods of data science and train them to be able to apply their analytical knowledge across disciplines beyond data science. The first two years of the program are Harrisbug's Analytics Master's Degree, and students apply for the actual PhD in the final semester of that program. 

If, however, students have a prior master’s degree from Harrisburg in computer science, they can transfer those credits and complete this four-to-five year doctoral degree in a shorter period. After completing the classwork portion of the degree, taking labs, seminars, classes, and doing fieldwork, they’ll begin their dissertation research. The defense of their dissertation will function as their final exam.

PhD in Data Sciences Overview

• Acceptance Rate: N/A
• Tuition and Fees : $800/credit hour (in state); $4,800/credit hour (out of state)
• PhD Funding Opportunities: Scholarships, grants, loans, work-study

PhD in Data Sciences Admission Requirements

• GRE/GMAT (strongly recommended)
• Essay on career goals
• Proof of research potential (courses or projects) 
• Minimum master’s degree GPA of 3.3
• A letter of intent 

The 1969-founded Indiana University-Purdue University Indianapolis (IUPUI) is an eponymous merger between the two schools and offers 550 programs across all levels. Of those, 57 are PhDs, covering everything from American studies and economics to addiction neuroscience and epidemiology. Some of their latest research breakthroughs were in the fields of informatics and computing, cardiology, nanosystems, and artificial intelligence. 

With data science being a field in its infancy, IUPU’s School of Informatics and Computing strives to have its graduates be leaders within this ever-evolving industry. Students will take classes in system analysis and design, monitoring social media, and data mining and visualization. 

PhD candidates can collaborate with professors on groundbreaking research in information infrastructures, Android science, computer security, machine learning, dataset integration, and computational social science. After earning this interdisciplinary degree, doctoral graduates will be ready to work in academia, health care, or even business intelligence. 

• Acceptance Rate: 84% (school acceptance rate)
• Tuition and Fees: $425/credit (in state) ; $1,350.00/credit (out of state)
• PhD Funding Opportunities: Faculty grants, work-study, loans, internal funding, foundation or corporate funding, funding agencies
• GPA of 3.5 or higher 
• GRE scores in the 70th percentile or higher for all sections
• Bachelor’s degree (master’s degree preferred)
• Completed classes in computer programming, statistics theory, linear algebra, and multivariable calculus
• Online application
• 500- to 750-word statement of purpose

Founded in 1966, Kennesaw provides more than 170 programs to its 40,000-plus students. Its 11 doctoral programs include studies in computer science, education, engineering, international diplomacy, business administration, and more. The core of the university's studies relates to technology and computing, medicine, human well-being and development, and sustainability. 

Doctoral Degree in Analytics and Data Science 

This interdisciplinary program combines business, math, stats, and computer science to make for well-rounded PhD candidates. Furthering that mission, the school also teaches written and oral communication skills to help graduates thrive in business or research fields. 

In the 78-credit program, students will take classes on machine learning, mining data, analyzing big data, and graph theory in their first year. This is followed by 21 credits of electives in their second year. Though students often participate in research projects during their first two years, the latter two of their programs will involve independently-led studies for their dissertation. 

Doctoral Degree in Analytics and Data Science Overview

• Acceptance Rate: 82% (school acceptance rate)
• Tuition and Fees: Qualified students are given a research stipend and waived tuition
• PhD Funding Opportunities: Foundations and institutes, corporate programs, scholarships, grants, loans, clearinghouses, coalitions, research stipends

Doctoral Degree in Analytics and Data Science Admission Requirements

• Master’s degree in computational-related discipline
• If no master’s degree, apply to the combined Master’s Degree in Applied Statistics or Computer Science program
• Strong proficiency in a programming language like Python
• Online application and $60 application fee
• Official transcripts from previous colleges or universities 
• The graduate record examination (GRE) scores 
• Statement of purpose 
• Completion of math courses through Calculus II
• SAS Certification (preferred) 

This 1874-founded school has 150 graduate programs, with 50-plus PhD programs in disciplines like medicine, physics, economics, and chemical physics. They have research programs in more than just pure and applied mathematics, as they also perform studies on wildfires, disinfectants, and autoregulation. 

PhD in Statistics and Data Science 

Prospective employees in academia, business, or government should consider this interdisciplinary research-based program in the Department of Mathematics and  Statistics in the College of Science. The 72 credit hours of this degree are broken up into 48 hours of classwork covering topics like linear models, statistical theory and computing, and quantitative methods, 30 of which should be at the 700-level. There are 24 dissertation credits and 24 master’s classes from a previously finished graduate degree. 

In order to continue within the PhD program after the third year, candidate hopefuls will need to pass a written qualifying test. After the qualifying test, students need to score highly on an oral exam in their chosen concentration before submitting and defending their dissertations.

PhD in Statistics and Data Science Overview

• Program Length: 4-6 years (8 years max)
• Acceptance Rate: 88% for overall school
• Tuition and Fees: $305.50/credit 
• PhD Funding Opportunities: Work-study, assistantships, scholarships, stipends, tuition waiver, subsidized medical plan

PhD in Statistics and Data Science Admission Requirements

• Online application 
• Bachelor’s and master’s degree transcripts
• Mathematics test scores (recommended)
• Financial aid application

Founded in 1701, Yale University is one of the oldest universities in the United States and is ranked fifth-best school in the nation by US News & World Report. Yale has 12 different professional schools and 73 different graduate degree programs. The university is especially well-known for its research in the humanities, environmental science, social sciences, and biotechnology. 

PhD program in Statistics and Data Science 

Students entering this degree program will focus on probability, statistics, information theory, data mining, machine learning, neural networks, and more as their foundational studies. After that, students take elective classes on one-off special topics classes that change between semesters. 

Those in the program need to take an oral and practical exam in their first year and begin their dissertation work in either their second or third year. This is usually a five-year program, with students getting a dissertation fellowship in their fifth year. Yale is a very exclusive school, and last year only made between 12 and 14 offers to the 300 applicants it received. As such, applying to other schools, in addition to Yale, would be a wise choice. 

PhD program in Statistics and Data Science Overview

• Program Length: 5 years
• Acceptance Rate: 5% (school acceptance rate)
• Tuition and Fees: $45,700/year (waived through provided fellowship)
• PhD Funding Opportunities : PhD students get a fellowship that covers all tuition through first five years in addition to an annual stipend of $36,000, Teaching fellowships, stipends, and health care benefits

PhD program in Statistics and Data Science Admission Requirements

• Graduate record examination (GRE) scores (optional)
• Strong mathematical background 
• Unofficial transcripts from previous colleges

Can You Get a PhD in Data Science Online?

Yes, you can get a PhD in Data Science online. There are a few fully-online PhD programs in data science provided by schools like Northcentral University. If you wish to pursue your PhD online but haven’t been accepted into a program for data science, you can consider a computer science program that has a concentration in data science. Since data science is a subset of computer science, you would learn the same foundational skills in either program.

Best Online PhD Programs in Data Science

How long does it take to get a phd in data science.

It typically takes four to five years to complete a PhD in Data Science. While four years is the standard for most schools, some programs take a fifth year to complete due to the exhaustive research conducted. Most of the programs we’ve covered above require students to complete between 70 to 80 credits.

While that only requires between eight and 10 credits per semester, students’ schedules are filled with doing research, being a teacher’s assistant, and completing a fellowship. The amount of coursework required, the research component, and the dissertation are all factors that can affect the time it takes to earn a PhD in Data Science.

Is a PhD in Data Science Hard?

Yes, a PhD in Data Science is hard as it involves taking incredibly technical classes and conducting your own novel research within data science. The academic discipline is the merging of computer science, statistics, operations research, and more, meaning that successful students must be proficient in a wide range of technical skills.

How Much Does It Cost to Get a PhD in Data Science?

It costs, on average, $19,314 per year to get a PhD in Data Science , according to the National Center of Education Statistics. The cost will change depending on the type of school a student attends. If a doctoral student studies at a public university, the tuition is only $12,171, on average. By contrast, if a doctoral student studies at a private institution, tuition costs about $14,208 at for-profit universities, and $27,776 for nonprofit universities.

How to Pay for a PhD in Data Science: PhD Funding Options

There are many different avenues students can look into to pay for a PhD in Data Science. Some schools, such as Yale and Kennesaw State University, waive tuition for eligible students, and might even give students a yearly stipend. Another common option is to do an assistantship, in which you’d work within your data science department by teaching or doing research.

Students can also apply for various scholarships or grants to help cut down the cost of tuition. While scholarships for undergraduate students are typically merit-based, PhD funding is achieved through a student’s specific field, supporting their research and cutting tuition costs.

Best Online Master’s Degrees

[query_class_embed] online-*subject-masters-degrees

What Is the Difference Between a Data Science Master’s Degree and PhD?

The difference between a data science master’s degree and PhD is that the former program only takes about two years to complete, while the latter is the educational step past a master’s degree that takes at least four years to complete. In fact, the first two years of a PhD program are usually a master’s degree program.

As such, some schools prefer applicants to have master’s degrees to cut down on the length of time. A master’s degree, and the first two years of a PhD program, are more so classroom-based. For PhD students, this is when students learn the foundations they’ll need to conduct their own research in the final two years of their program. 

Master’s vs PhD in Data Science Job Outlook

The job outlook for people with a Master’s or PhD in Data Science is very positive. Data science is a new scientific field, so workers within its industries are in high demand. For example, computer and information research scientists , which have a minimum requirement of a master’s degree, should see their careers grow by 22 percent between 2020 and 2030, according to the US Bureau of Labor Statistics (BLS).

Medical scientists , which have a minimum educational requirement of a PhD, should see job growth of 17 percent between 2020 and 2030. While The PhD job outlook is lower in this instance, a PhD is highly desirable, which is evident by the salary discrepancy below.

Difference in Salary for Data Science Master’s vs PhD

A PhD typically leads to a higher salary than a master’s degree. For example, the US Bureau of Labor Statistics reports that computer and information research scientists average $131,490 per year, while medical scientists in the scientific research and development services industry make $129,800. By contrast, those with a master’s degree tend to earn an average salary of $106,000 , according to PayScale.

While the BLS states that the minimum educational requirement for that job is a master’s degree, typically those with master’s degrees work in analyzing existing data. With a PhD, you can conduct research within this innovative field.

Related Data Science Degrees

[query_class_embed] https://careerkarma.com/blog/data-science-bachelors-degrees/ https://careerkarma.com/blog/best-online-data-science-bachelors-degrees/ https://careerkarma.com/blog/data-science-masters-degrees/

Why Should You Get a PhD in Data Science?

You should get a PhD in Data Science because you will be one of the pioneering leaders in this budding field. While a master’s program teaches students how to analyze data, a PhD program empowers students to do their own research. Read below for why you should get a PhD in Data Science.

Reasons for Getting a PhD in Data Science

• Lead the Field. Data science is a new field, so those that get a doctoral degree will be at the forefront of new developments. As you’ll be analyzing data, it’s incredibly exciting to think that your PhD research will be groundbreaking.
• Ever-evolving job: Technology is constantly advancing at incredible speeds, so being the one to learn about these advancements will never get old. With artificial intelligence technologies on the rise, one can only imagine in just 10 years’ time how different our understanding of data science will be.
• Specialize in interest. As students go along their educational paths, they go from learning foundational knowledge to increasingly specific information. Thus, if you’re passionate about a subset of data science but didn’t get to focus on it in your bachelor’s or master’s degree program, a PhD is the perfect opportunity to study, research, and work within your interests.
• High salaries. As the field of data science grows, the need for data science experts will also increase. PhD graduates will be uniquely equipped for the industry’s changing landscape and will be highly sought-after.
• Research opportunities. While this is an enriching hands-on experience, it lays the groundwork for you to be able to conduct your own studies in the latter two years of your program. You will be able to follow your passions rather than just helping a faculty member succeed in their work.
• Job Market. The BLS projects that job openings in computer and information research sciences will grow by 22 percent from 2020 to 2030. In getting a PhD, you will be a more competitive applicant than those with a lesser degree. It’s likely that you can even negotiate higher salaries because of your specialties.

Getting a PhD in Data Science: Data Science PhD Coursework

Data science is an interdisciplinary field, involving bioinformatics, computer science, statistics, and operations research. As such, the coursework PhD students undertake is diverse, including data mining, bioinformatics, ethics, and data visualization. Below, we’ll discuss some of the common classes found throughout most PhD programs in data science.

Introduction to Data Science

This class will teach you the baseline information you’ll need to know to advance in your data science career. Since you’re in an advanced degree program, you’ll likely be working with real data from case studies. You’ll take that information and learn how to build and manage databases, visualize data, and run statistical analyses.

Data Mining

Raw data, though important, isn’t useful until it can be contextualized and analyzed. Data mining is also called “knowledge discovery,” meaning that mining is the process of digging through mounds of data to learn information. Students will code, select and visualize data, use machine learning, and clean information to make novel findings.

Bioinformatics

As the combination of terms implies, bioinformatics is where biology and informatics meet and involves the study of biological data. This field of study is essential for those that want to go into medicine as data scientists. If you haven’t yet completed your bachelor’s degree, pursuing one of the best undergraduate degrees in bioinformatics is a wise choice.

Ethics of Data Science

Since data science involves collecting and storing information, mostly on people, there are possible moral ramifications to this. Within an ethics class, you’ll learn the proper methodology for conducting research to assure that your work meets codes of conduct.

Data Visualization

An important aspect of conducting research is being able to articulate your discoveries. Through visualization in programming languages like R, you’ll learn how to plot data and make reports. This process helps you organize your findings as well as snuff out any errors made during computation.

Best Master’s Degrees

[query_class_embed] *subject-masters-degrees

How to Get a PhD in Data Science: Doctoral Program Requirements

You can get a PhD in Data Science by meeting your chosen university’s degree requirements. Though these can vary, there are commonalities across different schools, such as completing a set number of credits, taking exams, and crafting a dissertation. We’ll now go into more detail about these common components of the doctoral degree.

Data Science PhD programs typically require the completion of 70 and 80 credit hours. This is often split down the middle, with the first half of credits being done in a classroom, and the latter half being done through your research and dissertation. 

While full-time undergraduate students take 15 or so credits per semester, the number of PhD students is lower as they conduct work outside of classroom hours through assistantships. Candidates typically complete this degree studying full-time for four to five years, taking between eight and 10 credits per semester. Most schools have a cap on the maximum number of years a student has to complete their PhD. For example, University of Nevada’s maximum allowance is eight years.

Before entering a PhD program, students already have a bachelor’s or master’s degree in a relevant STEM discipline. With the basics in coding languages, a statistical method, calculus, and engineering out of the way, doctorate students can take a deep dive into more difficult and focused courses. Some examples of classes PhD students will take include machine learning, data visualization, and bioinformatics. 

Often students will be able to choose a specialization to narrow the focus of their research. This allows them to take more niche classes on topics like asymptotics, stochastic processes, and Bayes theorem. After completing two years of classwork, students then begin their dissertation studies.

In order to be considered candidates for a PhD, students will need to pass exams between the first and third year of their degree program. The tests, which often consist of a verbal exam and written assessment, determine what the candidate-hopefuls have learned so far and whether they will be effective researchers with the school’s department.

After completing two years of coursework and passing their qualifying exams, PhD candidates begin research for their eventual dissertations. Candidates collaborate with a chosen faculty member to help guide them in their approved topic of study. Students then write about their findings in a dissertation or thesis, which they will need to defend in front of a committee before being considered doctors. 

Potential Careers With an Data Science Degree

[query_class_embed] how-to-become-a-*profession

PhD in Data Science Salary and Job Outlook

The salary and job outlook for those with a PhD in Data Science is very positive. The field of study is fairly new, and while there might not be many jobs that specifically require a PhD, having a terminal degree will make you a competitive applicant for prospective employers.

Data science PhD holders can work as medical scientists in the scientific research and development services industry or as information and computer research scientists. For the former industry, which requires a PhD, there will be a 17-percent increase in job openings between 2020 and 2030, per the US Bureau of Labor Statistics (BLS). The US BLS reports that computational and informational research sciences will see a 22-percent increase in job openings during that decade. Average salaries for those professionals are $116,430 and $131,490, respectively.

Though the salary for those in computational and informational research sciences is higher and typically only requires a master’s degree, those with a PhD are more likely to work in those positions. This is because PhD holders often conduct research, having done so in their doctoral programs, while former graduate students often analyze existing findings instead.

What Can You Do With a PhD in Data Science?

With a PhD in Data Science, there are a plethora of jobs within reach . The field of study is interdisciplinary, meaning that you’d be equipped with the skills to thrive in careers relating to computer science, bioinformatics, engineering, data management, and so much more. Let’s discuss further some of the highest-paying jobs that you can get with a PhD in Data Science.

Best Jobs with a PhD in Data Science

• Computer and Information Research Scientist
• Mathematicians/Statistician
• Medical Scientist
• Machine Learning Engineer
• Data Scientist

What Is the Average Salary for a PhD in Data Science?

The average salary for a PhD in Data Science is around $131,000. Payscale reports this is the average salary for those with a PhD in Computer Science, and since data science is a specialization of computer science, one can infer the salaries would be similar.

While $131,000 can be the expected salary for those with a Doctorate in Computer Science—and data science, by extension—the average salary you might earn will depend on a few variables. These include the amount of work experience you have, the industry you are working in, the organization you are working for, and the region of the country you are working in.

Highest-Paying Data Science Jobs for PhD Grads

Best data science jobs with a doctorate.

The best data science jobs with a doctorate are as a computer and information research scientist, mathematician or statistician, medical scientist, machine learning engineer, or data scientist. All of the above careers earn over $100,000 per year, but the actual salary a job might offer can differ.

These professionals are found across health care, corporate, and scientific fields and work to optimize the computer systems for their organization. This is done through distilling overly-complicated algorithms, troubleshooting issues with other engineers, and conducting research into developing new electronic programs. 

Though these jobs usually have a minimum education requirement of a master’s degree, those with a PhD are likely to also populate this sector and will likely be given preference by employers. This is because those with a PhD conduct research more frequently than those with a master’s degree, who usually analyze existing data. 

• Salary with a Data Science PhD: $131,490
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts

While both statisticians and data scientists analyze sets of information, a difference between the two is that while the field of computational statistics can be broad, data science is more focused on computer science and machine learning. They do, however, use the same methodology for analysis, so with your PhD in Data Science, you’d be equipped to be a statistician in a variety of industries.

According to the US Bureau of Labor Statistics, statisticians and mathematicians that work in technical, professional, and scientific services make on average $129,800, and those that work in life, engineering, and physical science development and research earn $114,770 per year. 

• Salary with a Data Science PhD: $129,800
• Job Outlook: 33% job growth from 2020 to 2030
• Number of Jobs: 44,800
• Highest-Paying States: Connecticut, New York, Massachusetts, Wyoming, and California  

Working in what the BLS calls the “ scientific research and development services industry ,” you could use your data science know-how in the medical field, especially if you concentrated or did your dissertation in health care. You’d likely data mine through patient information, analyzing it to then make recommendations to those in your health system organization. 

• Salary with a Data Science PhD: $116,430
• Job Outlook: 17% job growth from 2020 to 2030
• Number of Jobs: 133,900
• Highest-Paying States: Maine, New Jersey, Tennessee, Connecticut, Delaware

Artificial intelligence is growing alongside the data science field. Pursuing this career would allow you to be able to help foster artificial intelligence (AI) programs. You’d code your own AI system, teaching it how to analyze large amounts of data and how the system should respond to it. 

• Salary with a Data Science PhD: $112,709
• Job Outlook: 22% job growth from 2020 to 2030 
• Number of Jobs: 33,000 (for computer and information research scientists) 
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts (for computer and information research scientists) 

Those that work within this field combine their knowledge of informatics, computer programming, data mining and management, and more to conduct research by studying data sets. Data Scientists can work in business, devising avenues to optimize profits by looking at reports.health care, presenting their studies to guide decisions bettering the medical system; and academia, working to innovate this budding field of study. 

• Salary with a Data Science PhD: $108,660
• Number of Jobs: 105,980
• Highest-Paying States: New Jersey, New York, Delaware, Washington, California

Is a PhD in Data Science Worth It?

Yes, a PhD in Data Science is much worth it. Though not all data science jobs require a PhD, with some upper-level careers only requiring a master’s degree, you would have an advantage over others with lower levels of education. You’d have experience conducting your own research method, which would prepare you for running your own studies in the real world.

Most with master’s degrees don’t actually develop their own studies, rather, they analyze existing information. A PhD would give you a competitive edge, making you a more impressive candidate to prospective employers. You’d be more likely to get hired, and more plausibly able to negotiate a higher salary.

Data science is a new field of inquiry, so by having a doctorate in it, you would be at the forefront of the technological advancements within the industry. You would likely make at least $100,000 yearly in data science and have the interdisciplinary skills to work in other industries if you desire.

Additional Reading About Data Science

[query_class_embed] https://careerkarma.com/blog/introduction-to-data-science/ https://careerkarma.com/blog/how-to-get-a-job-in-data-science/ https://careerkarma.com/blog/data-science-degree/

PhD in Data Science FAQ

Yes, many data scientists have PhDs, but it is not a requirement for many jobs in the industry. Some require only master’s degrees instead, but there are advantages to having a PhD. With a PhD, you’ll have conducted your own research to get your doctorate, allowing you to more easily create your own studies in the real world. Those with a master’s degree, by contrast, usually only analyze existing studies, giving you less creative liberties with what you work on.

There are many career options for those with a PhD in Data Science, as the course of study is interdisciplinary. This means that those with the degree are taught technical skills that can apply to multiple different industries. Often, those with a data science degree go on to work in either business, medicine, or academia.

The US Bureau of Labor Statistics (BLS) reports that the median annual salary for data scientists is $131,490 . This report states that those within this job often have a master’s degree, meaning that with your PhD, you’d be a more impressive candidate and be able to negotiate a higher salary. Additionally, the BLS estimates that demand for data scientists will grow by 22 percent from 2020 to 2030, a much higher rate than the national average of eight percent.

A PhD is the best degree to become a data scientist if you want to conduct your own studies. Many within data science have lesser degrees, usually a master’s and sometimes a bachelor’s, which in turn gives them less responsibility within the field. If you have a scientific inquiry that you are passionate about and want the freedom to study what you want, a PhD is the best degree to obtain.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

What's Next?

Get matched with top bootcamps

Ask a question to our community, take our careers quiz.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

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

Boston University Academics

Boston University

• Campus Life
• Schools & Colleges
• Degree Programs
• Search Academics

PhD in Computing & Data Sciences

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solution of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented minorities in computing and data science disciplines.

Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.

For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.

Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

Learning Outcomes

The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

Sixteen semester courses (64 credits) are required for post-BA/BS students and 12 semester courses (48 credits) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 credits) as long as these credits were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.

Of the 16 courses, up to 3 undergraduate courses (12 credits) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.

The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.

The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.

During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-semester training course (4 credits) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-semester doctoral seminar (4 credits) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 credits) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.

A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred credits. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

No later than the end of the sixth semester (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one semester prior to the exam.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth semester (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th semester (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

Both the prospectus and final dissertation must be administered by a dissertation committee of at least three readers (including the dissertation advisor or coadvisors) and chaired by a CDS faculty member who is not one of the readers.

Related Bulletin Pages

• Abbreviations and Symbols

Beyond the Bulletin

• Faculty of Computing & Data Sciences
• Data Science for Good
• Impact Labs & Co-Labs
• BS in Data Science
• MS in Data Science
• PhD in Computing & Data Sciences
• Minor in Data Science

Terms of Use

Note that this information may change at any time. Read the full terms of use .

Accreditation

Boston University is accredited by the New England Commission of Higher Education (NECHE).

• © Copyright
• Mobile Version

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar

Data Science PhD Programs

A PhD in data science is a research-intensive degree that relies heavily on mathematics and computation to extract information from large data sets to make deductions or spot patterns and trends. 

Typically, a PhD in data science degree is interdisciplinary and is mainly offered as a part of a STEM program in computer science, engineering, mathematics, or statistics. A PhD in a specific data science-related area can be a good option for curious people who want to learn independently. 

Ad techguide.org is an advertising-supported site. Clicking in this box will show you programs related to your search from schools that compensate us. This compensation does not influence our school rankings, resource guides, or other information published on this site. Got it! Featured Analytics Programs

The field of data science and data science careers can be found in almost every industry, and the role of a data scientist continues to grow and evolve.

The actual job of a data scientist looks different for different organizations, and there is much more to it than the knowledge of software tools and the domain in which it is applied. 

To be successful as a data scientist , you need to have the right kind of data to solve your problem, the ability to understand business problems and the skills to apply the right kind of process to solve the problem. These skills get honed with practice and experience. 

Before we get into more details about getting a PhD in data science, one thing to know is that it is not mandatory to get a PhD to get most data science-related jobs.

Whether to get a PhD or not in data science depends on the kind of data science roles you are pursuing. Check out the master’s in data science guide for more details .

However, some roles are more research-oriented or need niche expertise, such as natural language processing (NLP), linguistics, speech recognition, etc.

At times, these roles might need you to create and develop algorithms from scratch. This requires research experience, and a PhD becomes relevant to the role of a data scientist. 

Through a PhD, individuals learn many skills to prepare for the commercial and academic world. This includes code, formulating questions, researching, creating technical documents, and solving problems. 

Trending Research Areas in Data Science

Insights into the research trends that have been going on in data science can be seen from the proceedings of well-regarded research conferences. For curious people, we have listed below some of the research areas in the field of data science and artificial intelligence trending today:

• Big Data on the Cloud
• Use of Augmented Analytics
• Focus on Edge Intelligence
• Automation of Data Cleaning
• Responsible AI
• Data-centric AI
• Increase in Use of Natural Language Processing
• Generative AI for Deepfake and Synthetic Data

Best PhD in Data Science Programs for 2024

Boise state university, capitol technology university, chapman university, clemson university, colorado technical university, florida atlantic university, george mason university, harrisburg university of science and technology, icahn school of medicine at mount sinai, indiana university.

These rankings were compiled from data accessed in December 2023 from Integrated Post-Secondary Education Data System (IPEDS) and College Navigator (both services National Center for Education Statistics). Tuition data was pulled from individual university websites and is current as of December 2023. If available, we also use additional criteria such as accreditation or designations by outside organizations or agencies.

Data Science PhD Program Overview and Curriculum

Here is a walkthrough of what a journey into a PhD looks like from beginning to end:

Admission Process

In general, admission requirements for most of the institutions include:

• Undergraduate and graduate transcripts
• GRE scores (may or may not be optional)
• TOEFL (English as a foreign language test, which may or may not be optional)
• A statement of intent for the program (reason for applying and plans)
• Letters of reference from undergrad professors or work supervisors if already working.
• Application fee (may be waived or reduced)
• Online application
• A curriculum vitae or resume (outlining all of your academic and professional accomplishments)

Post-Admission Process

• Every PhD program requires a student to complete a minimum number of credits to fulfill eligibility criteria. These credits can be a test of your knowledge, either at a foundational or advanced level. 
• Working on research projects over the first one or two years of the program will prepare you to frame the right questions, work on real-world data issues, and develop the necessary skill set required in the chosen data science-related topic.
• A qualifying exam is mandatory for every PhD program. These exams are designed to assess the candidate’s ability to meet the prerequisite standards/eligibility criteria. The assessment analyzes theoretical and practical understanding of the subject needed/required to work on your research project.
• Teaching undergraduate classes provides you with opportunities and experiences that will set you up for a future in academia.
• The dissertation proposal contains the hypothesis of your research that should meet the standards of publications in data analytics. The committee/faculty members need to approve the proposal before any proceedings to work on it.
• Students are expected to present their original work on the dissertation proposal. They are supposed to have expertise in their topic of dissertation. This is a crucial aspect of obtaining a doctoral degree in data science. It denotes that the student has mastered all of the necessary skills to undertake independent research that will contribute to the advancement of the field after completing their degree.
• Besides credits and qualifying exams, attending or presenting your research work at conferences, seminars, conventions, and summits provides you with an opportunity to network and form connections to boost your career. PhD can be fun as well as stressful. You can consider this degree as a marathon race that requires you to focus while testing your endurance for four to five years and finally providing you with the experience of a lifetime.

Online PhD Data Science Degree Programs

An online version of a PhD program allows individuals who have other work or family responsibilities to continue their education, albeit taking away some opportunities to network in person and in traditional ways. Students should verify the authenticity of online PhD programs by validating the institution’s accreditation before deciding to enroll. 

Traditionally, not many universities have offered completely online data science PhD programs. Mainly this reflects the hands-on nature of the degree that requires teaching, collaboration, and research. 

However, due to the COVID-19 pandemic, many traditional education programs have shifted courses and learning opportunities to digital platforms.

Because of technological advances in the EdTech industry, universities are becoming more comfortable hosting online classes and communication whenever needed. This might mean that some data science PhD work can be done online or remotely.

The best practice would be to contact the PhD programs you are interested in and inquire about online options. It is also a good idea to reach out to professors that would act as PhD advisors and be sure that they are willing and able to support long-term online learning and research.

Most universities provide funding for PhD students and programs in stipends, research, and teaching assistantships. If your preferred choice of university does not provide funding, it is a good idea to look into external financial assistance such as scholarships and fellowships.

Prior admission tuition fees must be considered when enrolling in a PhD program.

Career Paths and Outcomes

A PhD in data science may be offered from different departments at the university, such as statistics, computer science, mathematics, business, or even medical sciences.

Due to the interdisciplinary nature of data science PhD programs, career tracks, and research opportunities are numerous and diverse.

Academic Outcomes

• With a PhD, one can get hired in academia as a postdoctoral researcher or a fellow to advance their experience further. They can also begin their career as an associate professor in a university.

Industry Outcomes

• A data scientist ’s main focus or mission is to assist companies, organizations, and research efforts to resolve data-related problems. These problems range from user behavior to security risk factors and understanding consumer sentiment concerning the company’s products and services.
• A research scientist/quantitative researcher will be a part of the R&D team of the company or industry. A research scientist is responsible for developing hypotheses, conducting research, and building profitable business outcomes. A typical example would be handling a survey that identifies the latest trends and patterns of consumers’ lifestyle, income, and expenditures, then building/ improving a product or a service to generate profits.
• A chief data officer is the head of the organization’s data operations. A CDO brings their expertise to lead strategies and the ability to create models that use data to transform business strategies. A typical day as a CDO involves formulating data governance and management frameworks. Other tasks revolve around building data warehouses as a central repository for all information within a corporation.

Frequently Asked Questions

A doctoral degree is the training expected at many top universities for professors, researchers, and principal investigators in academia. A doctoral degree provides an edge where professional duties are research-oriented in corporate offices. According to Stitchdata, over 79 percent of data scientists who list their education data have earned a graduate degree. Of those, 38 percent have earned a PhD.

Regardless of how much funding the institution provides, it is advisable to always look for external financing and scholarships. The reason is that funding could change year-to-year at the institution or program level, so having a backup plan is always a good idea because doctoral studies usually take four to five years to complete.

An educational background in a quantitative field with a strong research interest is preferred by most schools. Since data science is interdisciplinary, universities may have prerequisites other than a background in mathematics or statistics.

Like other PhD programs, the GPA (which reflects previous academic experience) carries significant weight during the admissions process. However, since the PhD admissions process can vary greatly from program to program, it is good to check with the university about what factors factor into its admissions decisions.

PhD in Data Science Program List

Bowling green state university, indiana university-purdue university indianapolis, jackson state university, kennesaw state university, new jersey institute of technology, new york university, northcentral university, stevens institute of technology, university of massachusetts boston, university of nevada reno, university of tennessee-knoxville, university of vermont, washington university in st. louis, worcester polytechnic institute, yale university, related resources.

• PhD in Data Analytics Programs
• PhD Programs in Business Analytics
• How to Become a Data Scientist
• Find Your Data Science Certification
• Data Science and Data Scientist Jobs

Analytics Insight

Top 10 Universities in USA Offering Ph.D In Data Science

Brown University – Providence, Rhode Island

Indiana university-purdue university indianapolis  – indianapolis, indiana, new york university – new york, new york, yale university – new haven, connecticut, the university of maryland, college park, maryland, kennesaw state university – kennesaw, georgia, university of massachusetts boston – boston, massachusetts, california institute of technology, pasadena, california, university at buffalo, buffalo, new york, clemson university / medical university of south carolina (musc) – joint program– clemson, south carolina & charleston, south carolina.

Disclaimer: Any financial and crypto market information given on Analytics Insight are sponsored articles, written for informational purpose only and is not an investment advice. The readers are further advised that Crypto products and NFTs are unregulated and can be highly risky. There may be no regulatory recourse for any loss from such transactions. Conduct your own research by contacting financial experts before making any investment decisions. The decision to read hereinafter is purely a matter of choice and shall be construed as an express undertaking/guarantee in favour of Analytics Insight of being absolved from any/ all potential legal action, or enforceable claims. We do not represent nor own any cryptocurrency, any complaints, abuse or concerns with regards to the information provided shall be immediately informed here .

You May Also Like

Shiba Inu Price Prediction As SHIB Burn Rate Jumps 330%

Dogetti and Other Ethereum-Based Cryptocurrencies Brace for Price Surge Ahead of Ethereum’s Shanghai Upgrade

Scorpion Casino Presale Nearing $4 Million: Leading the Crypto Presale Race with Pullix and Algotech

5 requirements for success with DataOps strategies

Analytics Insight® is an influential platform dedicated to insights, trends, and opinion from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

• Select Language:
• Privacy Policy
• Content Licensing
• Terms & Conditions
• Submit an Interview

Special Editions

• Dec – Crypto Weekly Vol-1
• 40 Under 40 Innovators
• Women In Technology
• Market Reports
• AI Glossary
• Infographics

Latest Issue

Disclaimer: Any financial and crypto market information given on Analytics Insight is written for informational purpose only and is not an investment advice. Conduct your own research by contacting financial experts before making any investment decisions, more information here .

Second Menu

Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.

Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

PhD Program

Program overview.

Now Reading 1 of 4

Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

Admissions Events

Check out our event schedule, and learn when you can chat with us in person or online.

Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

Now Reading 2 of 4

The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

May phd program overview.

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

June PhD Program Overview

July phd program overview, august phd program overview.

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

Now Reading 3 of 4

Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

Now Reading 4 of 4

The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

How Should We Measure the Digital Economy?

2020 PhD Doctoral Research Forum Winner - Avinash Collis

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

• [email protected]

Keep Exploring

Ask a question or register your interest

Faculty Directory

Meet our faculty.

College of Science

Data Science, Ph.D.

Data science is an emerging discipline that combines mathematics, computing, and statistics to develop and apply methodologies required for data-driven industries. High demand for data scientists exists in technology, government, and banking. Students graduate prepared to obtain positions in industry using data for decision-making to improve production, reduce costs, and optimize operations.

All data science Ph.D. candidates offered a teaching assistant or research assistant position, if needed

Emphasis on moving from theory to practice in data science for working in interdisciplinary settings involving data-intensive analysis.

Current collaborative research projects applied to finances and Hispanic health disparities in the West Texas Borderplex.

Alum finds new career path in research

After four years teaching high school math, Clarissa Reyes was ready for a career outside the classroom. She returned to UTEP for a master's degree in statistics and was introduced to the Ph.D. in Data Science. The coursework has exposed Clarissa to data scientist careers in research and business. She looks forward to conducting research in clustering techniques and dimension reduction and hopes to work in a national lab after graduation.

Career Opportunities

Data science experts are needed in virtually every job sector, including tech companies such as Google, Amazon, Apple, Microsoft, and Facebook. Other corporations include banking and financial institutions, healthcare, and consulting.

Request information

• Events Calendar
• Parking and Transportation
• University Bookstore
• UTEP Directory
• Undergraduate
• Public Course Information
• Cares Act Compliance
• Clery Crime Statistics
• Emergency Information
• Mental Health Resources
• Texas Veterans Portal
• Report Fraud
• Required Links
• State Reports
• Title IX Reporting
• Title IX Sexual Misconduct Policy
• Web Accessibility
• Web Privacy Policy

U.S. News Releases 2024 Best Graduate Programs Rankings

Find the top-ranked graduate schools in business, education, law, nursing and other fields.

U.S. News Ranks Best Graduate Schools

Photo Library

To help prospective graduate students find a school that fits their needs, U.S. News released the 2024 rankings for multiple graduate fields.

Depending on the job or field, earning a graduate degree may lead to higher earnings, career advancement and specialized skill development.

But with several types of degrees and hundreds of graduate schools, it can be difficult to narrow down the options. To help prospective graduate students find a school that fits their needs, U.S. News released its 2024 Best Graduate Schools rankings today. They evaluate business, education, fine arts, health, law, library studies, nursing, public affairs, science, and social sciences and humanities graduate programs. Medical school and engineering rankings are not being released at this time.

A notable methodology change includes a new salary indicator based on profession in the business rankings.

Additionally, for the first time in four years, there are new rankings for a blend of doctoral and master's programs in audiology, occupational therapy, physical therapy, pharmacy, nurse midwifery and speech-language pathology. Graduate programs in nurse anesthesia and social work are also ranked for the first time since 2016 and 2022, respectively. Those and other specialty rankings are based on reputation ratings from scholars at other surveyed schools.

Read each program's specific methodology for the most detailed explanations of all the changes. The rankings are one source of information among many that prospective college students can use to inform their college decision. Below is a summary of the top-ranked schools in four major graduate program areas:

Best Law Schools

Best business schools, best nursing schools, best education schools.

Among the top 10 law schools . Yale Law School in Connecticut and California-based  Stanford Law School shared the top spot again. The  University of Chicago Law School in Illinois maintained its No. 3 rank, followed by a four-way tie at No. 4: Duke University School of Law in North Carolina, Harvard Law School in Massachusetts, the University of Pennsylvania Carey Law School and the University of Virginia School of Law .

Columbia Law School in New York ranked No. 8 again, while there was a three-way tie for No. 9: New York University School of Law , Northwestern University's Pritzker School of Law in Illinois and the University of Michigan—Ann Arbor Law School .

Looking beyond the top 10, multiple law schools moved up in the rankings. William & Mary Law School in Virginia, for instance, jumped nine spots from a tie at No. 45 to a five-way tie at No. 36.

U.S. News also ranked 13 law specialties: business/corporate, clinical training, constitutional, contracts/commercial, criminal, dispute resolution, environmental, health care, intellectual property, international, legal writing, tax and trial advocacy. (You can filter by specialty on the  main ranking page .)

Meanwhile, in the  part-time law school rankings – which consists of law schools with at least 20 part-time students enrolled in fall 2022 and fall 2023 – the top three stayed the same. The  Georgetown University Law Center in Washington, D.C., is once again at the top while D.C.-based  George Washington University Law School , now No. 3, switched places with the  Fordham University School of Law in New York City, which claimed second place.

Previously ranked at No. 3 and No. 6 respectively, the University of Pennsylvania's Wharton School and Stanford Graduate School of Business took the top spot in this year's full-time MBA program rankings . Northwestern's Kellogg School of Management and the University of Chicago's Booth School of Business moved down from their previous places in the top two to tie at No. 3.

While the top 10 mostly consists of the same schools as last year, both the Haas School of Business at the University of California, Berkeley and the University of Virginia's Darden School of Business joined those ranks this year. UC Berkeley rose from a three-way tie at No. 11 to a three-way tie at No. 7, while UVA moved up four spots from No. 14 to a tie at No. 10.

Farther down the full-time MBA rankings, there were some big changes. For example, Pitt's Joseph M. Katz Graduate School of Business soared 39 spots from a tie at No. 86 to a tie at No. 47.

Meanwhile, the very top of the part-time MBA rankings looks similar to last year, with the same schools in the top 5: UChicago, UC Berkeley, Northwestern, NYU's Leonard N. Stern School of Business and the Anderson School of Management at the University of California—Los Angeles. But UChicago took the No. 1 spot from UC Berkeley this year.

Moving up from No. 2, Johns Hopkins University School of Nursing in Maryland tied with Emory University's Nell Hodgson Woodruff School of Nursing in Georgia to claim the top spot in this year's nursing master's program rankings. Duke University School of Nursing in North Carolina climbed up by one to claim the third spot.

Johns Hopkins ranked No. 1, as it did last year, in the Doctor of Nursing Practice program rankings. George Mason University School of Nursing in Virginia – which reported more graduates and resources per faculty – soared from a four-way tie at No. 39 to take the No. 2 spot. Duke tied with the University of Washington School of Nursing to round out the top three.

Duke also ranked No. 1 in all of the ranked nursing master's nursing practice specialties, including administration, family, both acute and primary care adult gerontology, and mental health.

Once again, Teachers College, Columbia University in New York was No. 1 in the graduate education schools rankings. This year, however, it tied with the University of Wisconsin—Madison School of Education , which climbed two spots.

The University of Michigan—Ann Arbor's School of Education dropped from the top position to tie with the UCLA School of Education and Information Studies at No. 3. UCLA was previously tied at No. 7.

U.S. News also ranks nine education specialties, with the College of Education at Michigan State University claiming the top spot in the following categories: curriculum and instruction, educational administration, elementary teacher education, higher education administration and secondary teacher education.

Searching for a grad school of education? Access our  complete rankings  of Best Graduate Schools.

Grad Degree Jobs With $100K+ Salaries

Tags: students , graduate schools , medical school , business school , law school , education graduate school , engineering graduate school , MBAs , nursing programs

You May Also Like

Law schools that are hardest to get into.

Sarah Wood April 9, 2024

Ask Law School Admissions Officers This

Gabriel Kuris April 9, 2024

Grad School Housing Options

Anayat Durrani April 9, 2024

MBA Scholarships

Sammy Allen April 4, 2024

Special Master's Programs and Med School

Renee Marinelli, M.D. April 2, 2024

15 Famous Fulbright Scholars

Cole Claybourn April 1, 2024

When to Expect Law School Decisions

Gabriel Kuris April 1, 2024

How to Decide if an MBA Is Worth it

Sarah Wood March 27, 2024

Choosing A Major for Med School

Andrew Bauld March 26, 2024

Handling a Law School Rejection Letter

Gabriel Kuris March 25, 2024

Rawls College of Business

Master of science in data science.

Ranked the No. 3 best online, non-MBA program in the nation in 2022 , the Rawls College Master's in Data Science (MSDS) program provides graduates with the technical expertise needed to lead in the digital frontier. Through our 36-hour, STEM-designated program, learn how to manage, analyze and understand complex data to make strategic decisions. Upon graduation, you will have the skills and knowledge needed to be an agile data scientist capable of making impactful decisions across a variety of business settings and industries.

program highlights

Flexible format, on-campus or online classes.

Options to complete coursework on campus or online allow you to choose the course modality that best fits your personal needs.

One or Two-Year Options

Complete your degree in as little as a year, or take fewer classes per semester by selecting the online, two-year option.

Optional Practical Training (OPT) Eligible

International students may qualify to work in the U.S. for up to three years after receiving their degrees.

Cutting-Edge Curriculum

We prioritize the real-world application of knowledge and skills to best support students who want to accelerate their careers in data science, business analytics, business intelligence and big data fields. Through our comprehensive curriculum, you will learn how to use advanced technologies and statistical methods to manipulate data and translate findings into actionable organizational strategies.

Classes include foundational building blocks for today's data scientists:

Statistics for Data Science

Scripting Languages

Database Concepts

Data Technology Environments

Big Data Strategy

Business Intelligence

Multivariate Analysis

Time Series Analysis

Simulation & Optimization

Machine Learning

Decision Theory and Business Analytics

Big Data Security

program format

The MSDS program requires 36-credit hours, consisting of specialized data science courses. This is a lock-step program, requiring students to take classes in a specific order, as concepts build on each other. The program begins in the summer, and summer courses are four to five weeks in length. Fall and spring courses are seven to eight weeks in length. Coursework for the one-year program can be completed on campus or online. The two-year program is available online only.

Working professionals experience their core courses together, creating a stimulating cohort-based learning environment. During your tenure in the program, you will build relationships with peers, emanating from diverse backgrounds and industries, resulting in a larger professional network upon graduation.

View sample degree programs »

Our MSDS faculty members include engaged technology practitioners who utilize real-world experience to inspire their instruction. Their areas of expertise include computer-aided decision making, information requirements determination, operations management, health care analytics, information economics and more.

Meet our faculty »

class profile

At Rawls College, we believe diversity drives opportunities for collaboration and learning. Together, we benefit from the many perspectives, skills and experiences our students from all over the world bring to our learning environment.

supporting your success

Rawls Career Management Center

Whether you are looking to switch careers or advance on your current path, the Rawls Career Management Center (CMC) is dedicated to supporting your success. The staff in the CMC can help you explore professions and industries, learn strategic career advancement techniques, and connect you with top employers.

Techsan Connection

The Techsan Connection is a free, online platform for Texas Tech alumni. Through the platform, alumni can apply to jobs, reconnect with fellow classmates, network with industry professionals and volunteer to mentor current students.

The admission process is the first step toward earning your degree. We will work closely with you to ensure your application process is personal, simple and successful.

Application Requirements

While no prior work experience is required, applicants must have a bachelor's degree. Most applicants have an education or work background in computer science, management information systems, science, engineering, or similar fields. Basic knowledge of computer programming software such as R, SQL, and Python will be beneficial throughout the program's coursework. Additionally, applicants will benefit from prior completion of coursework in calculus, statistics and probability.

Unofficial Transcripts

Applicants must submit unofficial transcripts from any degree-awarding college or university, as well as any post-secondary institution attended. 

Applicants must submit a detailed current resume, indicating professional work experience—including start and end dates (month and year) for each position held. Provide accomplishments and skills acquired, including managerial experience.

GMAT Scores

The summer 2023 intake will not require a GRE or GMAT for application, but submission of scores are encouraged. 

We don't have a set minimum or maximum requirement for test scores. We review students holistically taking the application in its entirety into consideration.

English Proficiency for International Students

All international applicants must provide proof of English proficiency before their applications can be considered for admission. Only your most recent measure of English proficiency is considered for admission purposes. This test is waived only for graduates of U.S. universities or universities in English proficiency-exempt countries. Applicants who have completed at least two consecutive years at a college or university in the U.S. or in an English proficiency-exempt country are also exempt from the English proficiency requirement.

Application Deadlines

Summer Entry: May 1

International students are encouraged to apply at least six months in advance when possible.

student resources

• Prospective Students
• Current Students

Program Questions

[email protected] 806.742.3184

Cy Cawthron 806.834.1069

Events@Rawls

Rawls college graduate programs - open house event.

3:00 PM - 5:00 PM

Where: Rawls College of Business

Professional MBA Weekend Classes

Saturday, June 1, 2024 - Sun , June 2, 2024 (all day)

Saturday, June 29, 2024 - Sun , June 30, 2024 (all day)

Where: Center for Business Communications Room 139

Contact TTU

• Like Rawls College of Business on Facebook Like Rawls College of Business on Facebook
• Follow Rawls College of Business on X (twitter) Follow Rawls College of Business on X (twitter)
• Subscribe to Rawls College of Business on YouTube Subscribe to Rawls College of Business on YouTube
• Follow Rawls College of Business on Flickr Follow Rawls College of Business on Flickr
• Follow Rawls College of Business on Instagram Follow Rawls College of Business on Instagram
• Connect with Rawls College of Business on LinkedIn Connect with Rawls College of Business on LinkedIn

Graduate Programs

Graduate programs in mathematics, statistics and data science - driving the decisions that shape our world.

As we emerge from the COVID-19 pandemic, we can expect mathematics, statistics and data science to play key roles in helping us learn the best lessons and make the best decisions about how to move forward in every part of our society.

The SDSU Department of Mathematics and Statistics offers the M.S. in Mathematics, the M.S. in Statistics, the M.S. in Data Science and the Ph.D. in Computational Science and Statistics . All four programs offer students the opportunity to pursue graduate studies in a collegial environment with high faculty-student interaction and research activity, and are particularly effective at preparing graduates to work in business, industry or government as well as the more traditional area of higher education.

Accelerated masters degrees are available in all three master's degree programs.

Assistantships are available on a competitive basis

Many graduate research and teaching assistantships and fellowships are available (currently 38), though applicants should note that admission to the graduate program does not guarantee that an assistantship or fellowship will be awarded.

Application for admission

Applications may be submitted at any time, but it is recommended that applications for admission for the fall semester should be made by February 1 for master's degree programs and by December 15 for the Ph.D. program. Application and graduate assistantship decisions are generally made by the first week of March. Late applications will be considered for both admission and assistantships, but assistantship availability for such applications is likely to be reduced.

Graduate School Admission Requirements

At the department level, admission decisions are based on applicants' level of achievement in prerequisite undergraduate courses and the extent to which applicants' educational and professional goals align with the department's areas of specialization.GRE scores are not required or considered.

Graduate Program Application for Admission