data science phd france

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Data & Artificial Intelligence

WHY ENROLL IN THIS PROGRAM?

Get ready for a PhD by starting research at an early stage

Be closely associated with the research activities carried out in a world-renowned innovation cluster

Benefit from individual and personalized supervision by a faculty member

• Description
• Associated laboratories

The Data&AI PhD Track is a 5-year integrated Master’s/PhD program that provides a research-intensive training in the multi-disciplinary field of Data sciences. The program is open to outstanding students from a variety of scientific backgrounds who have completed their undergraduate training with highest honors and who are interested in tackling cutting-edge research ranging from machine learning to artificial intelligence, with applications to industry, social science, digital transformation or even physics and telecommunications.

Students apply to one of the following flavors:

Data&AI with a computer science flavor

Data&ai with an applied mathematics and statistics flavor, data&ai for social science, data&ai applied to other fields (telecommunication, physics…).

• prepare for a career in artificial intelligence and data science
• dive into an exciting field of research at the confluence of applied mathematics and computer science
• contribute to a domain that will shape tomorrow's societies

The five-year curriculum of the PhD track trains students in cutting-edge research for them to pursue international careers in prestigious universities or leading companies in the inter-disciplinary domain of AI and data science.

The students benefit from a program that is hand-tailored to their needs and interests. Each student is assigned a tutor -- a member of the IP Paris faculty who helps the student establish the courses to follow in each year. Courses can be chosen both in the domain of applied mathematics and in the domain of computer science, as well as beyond these

Relevant courses are offered, e.g., in the Master's programs “ DataAI ” in Computer Science, in “ DataScience “ in Applied Mathematics, or in the neighboring fields of statistics and social sciences.

Students are required to complete one research internship per year for the first two years of the PhD track. This internship can be carried out in the private or public sector, in France or abroad. It represents a maximum of 30 ECTS/year.

Students can complete a semester or a year in a partner university abroad. The modalities and target universities depend on the master's program that the student is assigned to.

• Information Processing and Communications laboratory (LTCI)
• Computer Science Laboratory (LIX)
• Applied Mathematics Center (CMAP)
• Computer Science and System Engineering Laboratory (U2IS)

Admission requirements

Academic prerequisites.

Completion of a Bachelor in computer science or mathematics, at Institut Polytechnique de Paris or equivalent in France or abroad.

Students who have completed the first year of an equivalent program may exceptionally be directly admitted to the second year (4-year PhD program).

Students who already have a master’s degree are invited to apply directly for a PhD in 3 years .

Language prerequisites

A certificate of proficiency in English (level B2) is required (TOEIC, IELTS, TOEFL, Cambridge ESOL), except for native speakers and students who previously studied in English.

How to apply

Applications are exclusively online. You will be required to provide the following documents:

• Transcript 
• Two academic references (added online directly by your referees)  
• CV/resume 
• Statement of purpose

You will receive an answer in your candidate space within 2 months following the closing date of the application session. 

Fees and scholarships

Estimated fees for 2022-2023 are subject to increase

• Regular fees: 243€
• Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
• Special cases: please refer to the "Cost of studies" section of the FAQs

Admission dates

Coordinator.

Louis Jachiet

General enquiry

[email protected]

77 Best universities for Data Science in France

Updated: February 29, 2024

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Below is a list of best universities in France ranked based on their research performance in Data Science. A graph of 417K citations received by 16.9K academic papers made by 77 universities in France 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. Claude Bernard University Lyon 1

For Data Science

2. Pierre and Marie Curie University

3. Paul Sabatier University - Toulouse III

4. University of Montpellier

5. Paris-Sud University

6. Grenoble Alpes University

7. University of Aix-Marseilles

8. University of Lorraine

9. University of Strasbourg

10. University of Bordeaux

11. University of Picardie Jules Verne

12. University of Paris 1 Pantheon-Sorbonne

13. University of Lille

14. National Graduate School of Engineering, Paris

15. Polytechnic School

16. Paris Dauphine University

17. University of Nice-Sophia Antipolis

18. Normal Superior School of Lyon

19. University of Rheims Champagne-Ardenne

20. TELECOM ParisTech

21. University of Rouen Normandie

22. Grenoble Institute of Technology

23. University of Nantes

24. National Institute for Applied Sciences, Lyon

25. Eurecom

26. Paris Descartes University

27. University of Burgundy

28. Paris Diderot University

29. National Polytechnic Institute of Toulouse

30. Versailles Saint-Quentin-en-Yvelines University

31. Paris Institute of Technology for Life, Food and Environmental Sciences

32. Paris Institute of Political Studies

33. School for Advanced Studies in the Social Sciences

34. University of Technology of Compiegne

35. Francois Rabelais University

36. University of Franche-Comte

37. SKEMA Business School

38. National Institute for Applied Sciences, Rouen

39. Toulouse Business School

40. ESSEC Business School Paris

41. University of Western Brittany

42. Paris-Est Creteil Val-de-Marne University

43. University of La Rochelle

44. Normal Superior School

45. University of Savoy Mont Blanc

46. University of Caen Normandy

47. University of Poitiers

48. National Engineering School of Mechanical and Aeronautical Engineering

49. National Advanced School of Engineering

50. University of Toulon

51. Catholic University of Lyon

52. University of Paris 8

53. National School of Bridges and Roads

54. Central School of Nantes

55. Montpellier SupAgro

56. University of Technology of Troyes

57. University of Pau and Pays de l'Adour

58. University of Angers

59. Paris West University Nanterre La Defense

60. HEC School of Management

61. EMLYON Business School

62. University of Orleans

63. University of Clermont Auvergne

64. University of Evry-Val d'Essonne

65. Polytechnic University of Hauts-de-France

66. New Sorbonne University - Paris III

67. CentraleSupelec

68. EHESP School of Public Health

69. Audencia Nantes School of Management

70. Grenoble Graduate School of Business

71. National Institute for Applied Sciences, Strasbourg

72. Kedge Business School

73. Jean Monnet University

74. University of Southern Brittany

75. Central School of Lille

76. University of Avignon and the Vaucluse

77. Paul Valery University, Montpellier 3

The best cities to study Data Science in France based on the number of universities and their ranks are Villeurbanne , Paris , Toulouse , and Montpellier .

Computer Science subfields in France

Support PSL

Data Science Program

The production and collection of massive amounts of data, combined with advances in storage, analysis and processing capacities, opened up unprecedented scientific perspectives in many disciplines, such as life sciences, cognitive sciences, astrophysics and quantitative sociology. The Data Science cross-disciplinary program covers the PSL education in AI and at the interfaces of other scientific disciplines. Thanks to the scientific richness of PSL in AI and other scientific disciplines and its involvement in the PRAIRIE institute , the Data Science cross-disciplinary Program offers a training of excellence in AI for students with any background.

[New in 2023] 

• To enable all master's and PhD students at PSL University to learn about or specialize in AI and data science, whatever their field of expertise, the DATA program is launching a brand-new certifying minor from the start of the 2023 academic year.
• The DATA program at Université PSL also offers training for academics in data science and artificial intelligence to all PSL researchers, PhD students and postdoc, adapted to their disciplines and levels (beginners, advanced, expert).

For students majoring in other subjects

If you are following a master's degree in another subject than mathematics and computer science, and if you want to include AI in your curriculum, the university-wide Data Science program includes a two-step training:

• Two-week intensive preparation in order to learn the basics of AI and to equip you with enough background to follow several MASH and IASD Masters' courses
• Intensive weeks on AI at the interfaces

Two preparatory weeks are offered. These are precontions for the certificate (unless the student demonstrates comparable skills as part of a previous course of study).

•  Week 1 – Fundamentals of Mathematics and Computer Science (3 ECTS) | The basics of mathematics and computer science for data science. The week can be followed asynchronously.
• Week 2 - Machine Learning and Database (3 ECTS) | This takes place at the end of August/beginning of September on the PariSanté Campus.

The 2024 program for these 2 weeks will be published soon.

Intensive DATA weeks are PSL weeks providing scientific and technical immersion in AI, at the interface with another discipline. 2 ECTS are awarded at the end of each week.

Details of the program can be found on the PSL weeks website .

PSL weeks from 27 November 2023 to 1st December 2024:

• AI for Economics and Finance
• Explainability and Interpretability in Artificial Neural Networks
• Ethique et Intelligence Artificielle
• NLP for Social Sciences
• Neuro and Bio-robotics: senses and perception

PSL weeks from 4 March 2024 to 8 March 2024:

• Digital Humanities meet Artificial Intelligence
• Machine learning for physics and engineering
• Green Artificial Intelligence
• Machine learning in Genomics
• Statistical Physics and Machine Learning
• Data mining and modeling for behavioral sciences and beyond

COFUND: Artificial Intelligence for the Sciences (AI4theSciences)

Artificial Intelligence for the Sciences (AI4theSciences) is a doctoral program run by Université PSL. 26 Ph.D. contracts at the interfaces of artificial intelligence or big data processing are offered. AI4theSciences is supported and jointly funded by the Horizon 2020-Marie Skłodowska-Curie Actions-COFUND European program.

This unique and structured project invites an international audience. It aims to create a research community from multiple PSL laboratories and schools, along with a dozen private and public partners. AI4theSciences is one of the PSL initiatives that contribute to strengthening PSL research on a key challenge of our century. It benefits from a particularly useful and evolving environment for the doctoral students in the program and, more broadly, for the PSL research community.

All disciplines are eligible for co-funding (physics, chemistry, history, economics, etc.) as long as the doctoral dissertation includes artificial intelligence or big data processing techniques. In addition to the training courses specific to each doctoral grant, a program of training courses and specific events will be created: first courses on AI/big data, seminars, conferences, etc. Each doctoral student will benefit from dual supervision composed of a PSL dissertation adviser specialized in their discipline and a co-adviser specialized in artificial intelligence or big data techniques (possibly from an independent laboratory outside or a private partner).

Initiated in 2020, AI4theSciences is planned to run for six years.

Alexandre Allauzen (ESPCI Paris - PSL)

[email protected]

(For PSL students)

More information

Data science & AI for academics

(For PSL researchers, Phd students & postdoc)

France’s Top Graduate School for Statistics and Data Science

Located on the Ker Lann campus, just outside Rennes, ENSAI educates Data Scientists – for the private and public sector – capable of giving meaning to data.

We Train Data Experts

Our Data Scientists are qualified experts capable of collecting, treating, and modeling data, making it possible to derive meaning and inform decision making . ENSAI offers specializations in Risk Management, Biostatistics, Industry, Quantitative Marketing, Big Data, and Official Statistics.

Our DNA: Statistical Modeling

ENSAI graduates have advanced skills in Statistical Modeling as well as complementary skills in Computer Science and Quantitative Economics . They are unanimously recognized for the quality of the innovative scientific and operational training they receive at ENSAI and their ability to meet the needs of businesses and administrations across a wide array of sectors.

Recognized Research

ENSAI’s professors are nearly all members of the Center for Research in Economics and Statistics (CREST) research laboratory, a joint research center which includes researchers from ENSAE and the Department of Economics of Polytechnique Paris, both members of the prestigious Institut Polytechnique de Paris . The CREST lab fosters a dynamic research environment combining both fundamental and applied research in Statistics, Economics, and Computer Science which continually informs and influences ENSAI’s curriculum.

Open to the World

ENSAI has a network of academic and business partners around the globe. The school has 28 exchange partnerships in 13 European countries, as well as other exchange and double degree agreements with universities in Europe, the United States, Africa, and Asia. Additionally, 23% of ENSAI’s students come from abroad.

Not Just a Number

ENSAI graduates around 100 Data Scientists and nearly 50 Official Statisticians annually. Maintaining manageable class sizes allows the school to make sure students get the personal attention and advice they need to ensure that they succeed in their studies and begin their careers successfully.

  “Today, ENSAI educates some of the best data experts out there. Whether students go on to become Official Statisticians, Data Analysts, or Data Scientists, opportunities abound. In a field which is in constant evolution, our graduates can count on the solid foundations they have acquired at ENSAI: know-how in handling and modeling data to make sense of it. Be it in Finance, Health, Industry, Marketing, IT, Territorial Development, or Public Policy Evaluation, ENSAI graduates’ expertise will be essential in meeting the scientific, economic, and societal challenges of tomorrow.”

Ronan LE SAOUT Director of ENSAI

Part of the French Ministry of Economy and Finance, with administrative ties to France’s National Statistical Institute ( INSEE), ENSAI and its sister school ENSAE are part of the Group of National Schools of Economics and Statistics (GENES). 

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Doctoral training at ENSAE Paris is organized within two doctoral schools:

• The multidisciplinary doctoral school of the Institut Polytechnique de Paris, of which ENSAE is one of the five member schools along with the École Polytechnique, ENSTA Paris, Télécom Paris and Télécom SudParis), co-accredited with HEC Paris.
• The Hadamard Mathematics doctoral school, co-accredited with the Institut Polytechnique de Paris and the Université Paris-Saclay.

These doctoral schools federate a group of research teams, the first within the schools and research units of IP Paris, the second including the schools, universities and research units of the University of Paris-Saclay in mathematics. They offer future doctoral students scientific supervision at the highest international level as well as preparation for professional integration.

The doctoral school of the Institut Polytechnique de Paris

The grouping of the École polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris, Télécom SudParis within the Institut Polytechnique de Paris makes it possible to offer doctoral training of a very high level, reflecting the excellence of the founding schools that have chosen to join forces.

Internationally recognized as a degree of excellence and a marker of a very high level of expertise, the doctorate opens the door to highly qualified jobs in the public and private sectors. In addition to access to the academic world, it opens up major long-term career opportunities, based on the specific skills attained during the doctorate and widely recognized in the socio-economic world for highly qualified jobs.

The IP Paris doctoral school offers a very rich training program, reflecting its founding schools, ranging from fundamental to applied research. It is very open to the international scene, with 45% of its doctoral students coming from abroad, thus demonstrating its attractiveness on a global level.

Through its excellence and ambition, the IP Paris Doctoral School program prepares its students for outstanding scientific careers in both the academic and industrial sectors, in France and abroad.

The IP Paris doctoral school currently welcomes a thousand doctoral students, supervised by more than 800 teacher-researchers (550 of whom are qualified to direct research) in 30 research laboratories.

Two paths are open to obtain the title of doctor from the Institut Polytechnique de Paris:

• The classic three-year doctoral path in many fields. For more information
• The PhD Track in 5 years, for holders of a Bachelor's degree who already wish to go on to a PhD at the end of their Master's degree. For more information

Host laboratories and grants

CREST, which hosts doctoral students in economics, statistics, finance and insurance, and sociology, has some twenty research grants, usually for two years, to help students (graduates of ENSAE Paris, ENSAI, or other French or foreign higher education institutions) prepare a doctoral thesis.

• ENLIGHTEN THE FUTURE

Doctoral Studies

With its 21 doctoral schools, Université Paris Cité offers many doctoral students the opportunity to train through research in all major disciplinary fields. At the national level, once fully operational, Université Paris Cité will offfer 5% of all PhD degrees in France.

Université Paris Cité is committed to a doctoral policy aimed at research training and training by research. It trains future researchers and teacher-researchers as well as future high-level executives.

Astronomy and Astrophysics Ile-de-France – ED 127 Director : Mr. Thierry FOUCHET Contact : Mrs. Jacqueline PLANCY

Environmental Sciences Ile-de-France – ED 129 Director : Mrs Pascale BOURUET-AUBERTOT Contact : Mrs Laurence AMSILI-TOUCHON

Doctoral School of Computer Science, Telecommunications, Electronics of Paris (EDITE) – ED 130 Director : Mr. Carlos AGON Contact : Mrs Rose NAHAN

Language, Litterature and Imagery : civilisations and humanities – ED 131 Director : Mr. Mathieu DUPLAY Co-director : Mrs Emmanuelle ANDRE Contact : Mrs Robin CHEVALIER

Cognition, Brain, Behaviour (ED3C) – ED 158 Director : Mr Alain TREMBLEAU Deputy director UPCité   :Mrs Thérèse COLLINS Contact : Mrs Hélène JOUANNE

Cognition, Behaviour, Human behaviour (3CH) – ED 261 Director : Mrs Karine DORE-MAZARS Contact : Mrs Lucie ALEX

Legal, political sciences, economics and management – ED 262 Director  : Mrs Anémone CARTIER-BRESSON Contact : Mrs Josie YEYE

Mathematical science Paris Centre – ED 386 Director : M. Elisha FALBEL Co-director  : M. Pierre-Henri CHAUDOUARD Contact : Mrs Amina HARITI

Physical Chemistry and Analytical chemistry – ED 388 Director : Mrs Alexa COURTY Contact : Mrs Konnavadee SOOBRAYEN

Pierre Louis Doctoral School of Public Health in Paris : Epidemiology and Biomedical Information Sciences – ED 393 Director  : Mr. Pierre-Yves BOËLLE Contact : Mrs Koltoum BEN SAID

Research in Psychoanalysis – ED 450 Director : Mrs Mi-Kyung YI Co-director : Mr Thamy AYOUCH Contact : Mr Ali BRADOR

Frontiers of Innovation in Research and Education (FIRE) – ED 474 Director : Mrs Muriel MAMBRINI-DOUDET  Co-directeur David TARESTE Contact : Mrs Elodie KASLIKOWSKI

Earth and Environmental Sciences and Physics of the Universe – ED 560 Director : Mr. Fabien CASSE Contacts : Mrs Alissa MARTEAU

Hematology, Oncogenesis, and Biotherapies – ED 561 Director  : Mr. Raphaël ITZYKSON Contacts : Mr Maxime DA CUNHA / Mrs Aurélie BULTELLE

Bio Sorbonne Paris Cité – ED 562 Director : Mrs Caroline LE VAN KIM – Co-Director : Mrs Chantal DESDOUETS Contacts : Mr Louis DUVAL-KISTER

Drug Toxicology, Chemistry and Imaging (MTCI) – ED 563 Director  : Mrs Marie-Christine LALLEMAND Contact : Mrs Elisabeth HOMBRADOS

Physics in Ile de France – ED 564 Director  : Mr Frédéric CHEVY Co-director : Mr Philippe LAFARGE Contact : Mrs Monia MESTAR

Sports, Motricity and Humain mobility sciences (SSMMH) – ED 566 Director  : Mrs Isabelle SIEGLER Co-director : Mr. Bernard ANDRIEU Contact : Mrs Marie-Pierre RICHOUX

Language Sciences – ED 622 Director : Mrs Caterina DONATI Contact : Mrs Chafia AIT-HELAL

Knowledge, Science, Education – ED 623 Co-Director : Mr. Fabrice VANDEBROUCK Co-Director : Mrs Anne BARRERE Contact : Mrs Agathe TRAN

Social Sciences – ED 624

Department 1 Director : Mrs Véronique PETIT Contact : Mr. Jérôme BROCHERIOU

Department 2

Director : Mr Antoine REBERIOUX Contact : Mrs Sarah RAHMANI

More information :

Doctoral School website for more information The following content is in French French higher education system chart

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The Cifre doctorate

The Cifre ( Conventions Industrielles de Formation par la Recherche ) system allows French companies, local authorities or associations to entrust a doctoral candidate with an assignment in the framework of a research collaboration with an academic research laboratory affiliated to a doctoral school. 

Published on 16/06/2020 - Updated on 5/05/2022

If you want to obtain a doctorate that will allow you to evolve naturally in two environments with distinct requirements and to build a bridge between the academic and business worlds, the Cifre system is for you. You will be supervised by two supervisors: a thesis director, researcher or lecturer from a research unit at Sorbonne University and a scientific manager or a contact within the company. You will have a fixed-term employment contract of 3 years or even an open-ended contract (CDI).

The assignment that the company will entrust to the doctoral candidate will constitute the doctoral research project. This project will have been defined jointly by the company and the research unit and will have been validated by the doctoral school. It will fall within the framework of a collaboration agreement signed between the university and the company, an agreement which will mainly frame the sharing of intellectual property and the use of the results. 

The ANRT (National Agency for Research and Technology), which manages the CIFRE scheme on behalf of the Ministry of Higher Education, Research and Innovation, provides applicants with offers from companies and proposals for laboratory partnerships to help doctoral candidates to set up their CIFRE project.

It is also the ANRT that examines the applications. 

• Information on the CIFRE system - ANRT website
• Contact the Doctoral College's Corporate Relations Officer
• Download the CIFRE presentation brochure

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M2 Data Science

• Places available 20
• Language(s) of instruction English
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• Entry requirements

This master allows students to gain strong theoretical and applied skills to be able to successfully manage today's mass of data of the real world. Specifically, students will be able to gain specialized skills in handling modern database systems and the algorithms used for data analysis at huge scales. Indeed, the main focus will be on how to handle data and knowledge that is large, but also heterogeneous and in some cases unreliable, via algorithms and systems spanning the areas of data science and engineering, data mining, and machine learning.

This programme is offered as an introductory course (hosting capacity: 15) and as apprenticeship training (work-study programme) (hosting capacity: 25)

• Introductory course:

recruitment session from 01/05/2020 to 01/07/2020

• Apprenticeship training (work-study):

recruitment session from 01/04/2020 to 01/07/2020

This programme is offered as an introductory course (hosting capacity: 15) and in work-study programme (hosting capacity: 25)

Holders of a DataScience or Artificial Intelligence M1 from Université Paris-Saclay or an equivalent qualification for students from outside Paris-Saclay. For the work-study track, students must have obtained the Data Science M1 via a work-study programme. Contracts with the companies last for two years. Prospective students are expected to have completed a minimum of 4 years of tertiary studies in Computer Science, either via an M1 in Paris-Saclay or via an equivalent diploma for foreign students.

Acquire the theoretical foundations belonging to various types of data science approaches.

Design and develop systems for the management of big data and heterogeneous data.

Build, evaluate and interpret analysis and learning models taking into account the nature of the data.

Have a good command of the learning methodology, from raw data (from scratch) to evaluation.

Data scientist/engineer, designer/developer of the architecture of big data management and analysis, applications manager, applications integrator, data architect, R&D engineer.

This study course enables students to easily join the industrial world in companies that develop innovative software, in start-ups or in the R&D departments of large companies. It also paves the way to a doctoral programme, preparing students to write a thesis, either by joining a research organisation (public or private) or an R&D department in a company This study path targets professions such as Data Analyst, Database administrator, Big Data manager. Big Data, Application Manager, Application Integrator, Data Architect, Big Data Application Designer/Developer, Research and Development Engineer.

Laboratoire de recherche en informatique Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur Laboratoire Spécification et Vérification.

Le parcours-type Data Science peut être validé selon deux voies de formation : - La voie par alternance (ISD) - La voie initiale (DS)

Toutes les UEs listées ci-dessous devront être validées au cours du parcours (M1 / M2): il est requis d'acquerir 60 ECTS par niveau, pour un total de 120 ECTS à l'issue des deux années.

** Pour valider la voie initiale DS (M1 & M2), les étudiants devront valider toutes les UE intitulées [DS]. À ces UE s'ajoutent : - [AI] TC1 : Machine Learning - [AI] TC2 : Optimization - [AI] TC6 : Datacomp 2 - Visual Analytics (UE du M2 BDMA)

Pour atteindre 120 crédit ECTS, chaque étudiant devra compléter son parcours avec 4 UE dont l'intitulé est Soft skills - xxxx, un TER-Stage (en M1), un stage long (au second semestre du M2) ainsi qu'un libre choix de cours d'autres parcours-types pour compléter les 120 crédits ECTS.

** Pour valider la voie par alternance, les étudiants devront suivre toutes les UE [ISD] pour un total de 120 ECTS. Les cours dans cette voie sont dispensés en français.

Motivation letter.

All transcripts of the years / semesters validated since the high school diploma at the date of application.

Certificate of English level.

Curriculum Vitae.

Selection sheet completed.

VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

Supporting documents : - Residence permit stating the country of residence of the first country - Or receipt of request stating the country of first asylum - Or document from the UNHCR granting refugee status - Or receipt of refugee status request delivered in France - Or residence permit stating the refugee status delivered in France - Or document stating subsidiary protection in France or abroad - Or document stating temporary protection in France or abroad.

Find out more about the registration fees

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

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UVA launches the nation's first school of data science

The University of Virginia today opened a new building that will house the nation’s first School of Data Science , bankrolled by business school graduate Jafffray Woodriff. As a kid, he was obsessed with statistics and the ability to make predictions – applying his understanding to sports.

"At the age of 18, after 7 years of studying baseball, I had a profound moment of practicality," he told a crowd of about 200 people who had gathered for the grand opening. "I abandoned baseball statistics and switched my focus to the financial markets, and luckily I had the courage and optimism to bet on myself heavily, over and over, as I plunged into ever deeper debt in my first few years.”

While still an undergraduate at UVA’s school of business, he stayed up for 40 hours, drinking Pepsi and working with 20 computers to develop the trading system used to launch his company – Quantitative Investment Management. It made him a multi-millionaire, and he donated $120 million to UVA for the new School of Data Science.

Governor Glenn Youngkin was also on hand as the university opened the four-story building. He told students that studying there would be their ticket to jobs in many fields.

“Think about the new reams of opportunity for us to glean insights and to seek truth. Think about the opportunity to develop innovative ways to innovate and assess, to determine new medical treatments, new drug discovery.”

He noted on this very day he had joined executives with Google to announce their billion-dollar investment in infrastructure here along with a $75 million fund to support education in artificial intelligence.

Seated with a top executive from Capital One, which gave $2 million for the new school, Youngkin said collaboration with corporations was an essential part of the future for data science. The new building, which has been called a school without walls, features a cushy student lounge dubbed the Corporate Commons.

Thinking loftier thoughts, UVA President Jim Ryan had three hopes for the school’s future.

“The first relates to the whole school without walls things. I confess I was a little surprised and somewhat disappointed when I came here yesterday for a tour and realized it was just a metaphor," he joked. "There are actually walls here – though I have come to appreciate the practical needs for walls.”

He said the school should serve as a home base for the community, the Commonwealth and beyond. And, finally, he urged students and faculty to act ethically, responsibly and with an eye toward how data and data science ccan help and not hurt our communities.

Driving Innovations in Biostatistics with Denise Scholtens, PhD

“I'm continually surprised by new data types. I think that we will see the emergence of a whole new kind of technology that we probably can't even envision five years from now…When I think about where the field has come over the past 20 years, it's just phenomenal.”  —  Denise Scholtens, PhD  

• Director, Northwestern University Data Analysis and Coordinating Center (NUDACC)  
• Chief of Biostatistics in the Department of Preventive Medicine  
• Professor of Preventive Medicine in the Division of Biostatistics and of Neurological Surgery  
• Member of Northwestern University Clinical and Translational Sciences Institute (NUCATS)  
• Member of the Robert H. Lurie Comprehensive Cancer Center  

Episode Notes 

Since arriving at Feinberg in 2004, Scholtens has played a central role in the dramatic expansion of biostatistics at the medical school. Now the Director of NUDACC, Scholtens brings her expertise and leadership to large-scale, multicenter studies that can lead to clinical and public health practice decision-making.    

• After discovering her love of statistics as a high school math teacher, Scholtens studied bioinformatics in a PhD program before arriving at Feinberg in 2004.  
• Feinberg’s commitment to biostatistics has grown substantially in recent decades. Scholtens was only one of five biostatisticians when she arrived. Now she is part of a division with almost 50 people.  
• She says being a good biostatistician requires curiosity about other people’s work, knowing what questions to ask and tenacity to understand subtitles of so much data.   
• At NUDACC, Scholtens and her colleagues specialize in large-scale, multicenter prospective studies and clinical trials that lead to clinical or public health practice decision-making. They operate at the executive level and oversee all aspects of the study design.  
• Currently, Scholtens is involved with the launch of a large study, along with The Ohio State University, that received a $14 million grant to look at the effectiveness of aspirin in the prevention of hypertensive disorders in pregnancy.  
• Scholtens first started her work in data coordinating through the Hyperglycemia Adverse Pregnancy Outcome (HAPO) study, which looked at 25,000 pregnant individuals. This led to a continued interest in fetal and maternal health.   
• When it comes to supportive working environments, Scholtens celebrates the culture at Feinberg, and especially her division in biostatistics, for being collaborative as well as genuinely supportive of each other’s projects. She attributes this to strong leadership which established a culture with these guiding principles.   

Additional Reading  

• Read more about the ASPIRIN trial and other projects taking place at NUDACC   
• Discover a study linking mothers’ obesity-related genes to babies’ birth weight, which Scholtens worked in through the HAPO study   
• Browse all of Scholtens recent publications 

Recorded on February 21, 2024.

Continuing Medical Education Credit

Physicians who listen to this podcast may claim continuing medical education credit after listening to an episode of this program..

Target Audience

Academic/Research, Multiple specialties

Learning Objectives

At the conclusion of this activity, participants will be able to:

• Identify the research interests and initiatives of Feinberg faculty.
• Discuss new updates in clinical and translational research.

Accreditation Statement

The Northwestern University Feinberg School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Credit Designation Statement

The Northwestern University Feinberg School of Medicine designates this Enduring Material for a maximum of 0.50  AMA PRA Category 1 Credit(s)™.  Physicians should claim only the credit commensurate with the extent of their participation in the activity.

American Board of Surgery Continuous Certification Program

Successful completion of this CME activity enables the learner to earn credit toward the CME requirement(s) of the American Board of Surgery’s Continuous Certification program. It is the CME activity provider's responsibility to submit learner completion information to ACCME for the purpose of granting ABS credit.

All the relevant financial relationships for these individuals have been mitigated.

Disclosure Statement

Denise Scholtens, PhD, has nothing to disclose.  Course director, Robert Rosa, MD, has nothing to disclose. Planning committee member, Erin Spain, has nothing to disclose.  FSM’s CME Leadership, Review Committee, and Staff have no relevant financial relationships with ineligible companies to disclose.

Read the Full Transcript

[00:00:00] Erin Spain, MS: This is Breakthroughs, a podcast from Northwestern University Feinberg School of Medicine. I'm Erin Spain, host of the show. Northwestern University Feinberg School of Medicine is home to a team of premier faculty and staff biostatisticians, who are the driving force of data analytic innovation and excellence here. Today, we are talking with Dr. Denise Scholtens, a leader in biostatistics at Northwestern, about the growing importance of the field, and how she leverages her skills to collaborate on several projects in Maternal and Fetal Health. She is the Director of the Northwestern University Data Analysis and Coordinating Center, NUDACC, and Chief of Biostatistics in the Department of Preventive Medicine, as well as Professor of Preventive Medicine and Neurological Surgery. Welcome to the show.  

[00:01:02] Denise Scholtens, PhD: Thank you so much.  

[00:01:02] Erin Spain, MS: So you have said in the past that you were drawn to this field of biostatistics because you're interested in both math and medicine, but not interested in becoming a clinician. Tell me about your path into the field and to Northwestern.  

[00:01:17] Denise Scholtens, PhD: You're right. I have always been interested in both math and medicine. I knew I did not want to be involved in clinical care. Originally, fresh out of college, I was a math major and I taught high school math for a couple of years. I really enjoyed that, loved the kids, loved the teaching parts of things. Interestingly enough, my department chair at the time assigned me to teach probability and statistics to high school seniors. I had never taken a statistics course before, so I was about a week ahead of them in our classes and found that I just really enjoyed the discipline. So as much as I loved teaching, I did decide to go ahead and invest in this particular new area that I had found and I really enjoyed. So I wanted to figure out how I could engage in the field of statistics. Decided to see, you know, exactly how studying statistics could be applied to medicine. At the time, Google was brand new. So I literally typed in the two words math and medicine to see what would come up. And the discipline of biostatistics is what Google generated. And so here I am, I applied to grad school and it's been a great fit for me.  

[00:02:23] Erin Spain, MS: Oh, that's fantastic. So you went on to get a PhD, and then you came to Northwestern in 2004. And so tell me a little bit about the field then and how it's changed so dramatically since.  

[00:02:36] Denise Scholtens, PhD: So yes, I started here at Northwestern in 2004, just a few months after I had defended my thesis. At the time there was really an emerging field of study called bioinformatics. So I wrote my thesis in the space of genomics data analysis with what at the time was a brand new technology, microarrays. This was the first way we could measure gene transcription at a high throughput level. So I did my thesis work in that space. I studied at an institution with a lot of strengths and very classical statistics. So things that we think of in biostatistics like clinical trial design, observational study analysis, things like that. So I had really classic biostatistics training and then complimented that with sort of these emerging methods with these high dimensional data types. So I came to Northwestern here and I sort of felt like I lived in two worlds. I had sort of classic biostat clinical trials, which were certainly, you know, happening here. And, that work was thriving here at Northwestern, but I had this kind of new skillset, and I just didn't quite know how to bring the two together. That was obviously a long time ago, 20 years ago. Now we think of personalized medicine and genomic indicators for treatment and, you know, there's a whole variety of omics data variations on the theme that are closely integrated with clinical and population level health research. So there's no longer any confusion for me about how those two things come together. You know, they're two disciplines that very nicely complement each other. But yeah, I think that does speak to how the field has changed, you know, these sort of classic biostatistics methods are really nicely blended with a lot of high dimensional data types. And it's been fun to be a part of that.  

[00:04:17] Erin Spain, MS: There were only a handful of folks like you at Northwestern at the time. Tell me about now and the demand for folks with your skill set.  

[00:04:26] Denise Scholtens, PhD: When I came to Northwestern, I was one of a very small handful of biostatistics faculty. There were five of us. We were not even called a division of biostatistics. We were just here as the Department of Preventive Medicine. And a lot of the work we did was really very tightly integrated with the epidemiologists here in our department and we still do a lot of that for sure. There was also some work going on with the Cancer Center here at Northwestern. But yeah, a pretty small group of us, who has sort of a selected set of collaborations. You know, I contrast that now to our current division of biostatistics where we are over 20s, pushing 25, depending on exactly how you want to count. Hoping to bring a couple of new faculty on board this calendar year. We have a staff of about 25 statistical analysts. And database managers and programmers. So you know, when I came there were five faculty members and I think two master's level staff. We are now pushing, you know, pushing 50 people in our division here so it's a really thriving group.  

[00:05:26] Erin Spain, MS: in your opinion, what makes a good biostatistician? Do you have to have a little bit of a tough skin to be in this field?  

Denise Scholtens, PhD: I do think it's a unique person who wants to be a biostatistician. There are a variety of traits that can lead to success in this space. First of all, I think it's helpful to be wildly curious about somebody else's work. To be an excellent collaborative biostatistician, you have to be able to learn the language of another discipline. So some other clinical specialty or public health application. Another trait that makes a biostatistician successful is to be able to ask the right questions about data that will be collected or already have been collected. So understanding the subtleties there, the study design components that lead to why we have the data that we have. You know, a lot of our data, you could think of it in a simple flat file, right? Like a Microsoft Excel file with rows and columns. That certainly happens a lot, but there are a lot of incredibly innovative data types out there: wearables technology, imaging data, all kinds of high dimensional data. So I think a tenacity to understand all of the subtleties of those data and to be able to ask the right questions. And then I think for a biostatistician at a medical school like ours, being able to blend those two things, so understanding what the data are and what you have to work with and what you're heading toward, but then also facilitating the translation of those analytic findings for the audience that really wants to understand them. So for the clinicians, for the patients, for participants and the population that the findings would apply to.   

Erin Spain, MS: It must feel good, though, in those situations where you are able to help uncover something to improve a study or a trial.  

[00:07:07] Denise Scholtens, PhD: It really does. This is a job that's easy to get out of bed for in the morning. There's a lot of really good things that happen here. It's exciting to know that the work we do could impact clinical practice, could impact public health practice. I think in any job, you know, you can sometimes get bogged down by the amount of work or the difficulty of the work or the back and forth with team members. There's just sort of all of the day to day grind, but to be able to take a step back and remember the actual people who are affected by our own little niche in this world. It's an incredibly helpful and motivating practice that I often keep to remember exactly why I'm doing what I'm doing and who I'm doing it for.  

[00:07:50] Erin Spain, MS: Well, and another important part of your work is that you are a leader. You are leading the center, NUDACC, that you mentioned, Northwestern University Data Analysis and Coordinating Center. Now, this has been open for about five years. Tell me about the center and why it's so crucial to the future of the field.  

[00:08:08] Denise Scholtens, PhD: We specialize at NUDACC in large scale, multicenter prospective studies. So these are the clinical trials or the observational studies that often, most conclusively, lead to clinical or public health practice decision making. We focus specifically on multicenter work. Because it requires a lot of central coordination and we've specifically built up our NUDACC capacity to handle these multi center investigations where we have a centralized database, we have centralized and streamlined data quality assurance pipelines. We can help with central team leadership and organization for large scale networks. So we have specifically focused on those areas. There's a whole lot of project management and regulatory expertise that we have to complement our data analytics strengths as well. I think my favorite part of participating in these studies is we get involved at the very beginning. We are involved in executive level planning of these studies. We oversee all components of study design. We are intimately involved in the development of the data capture systems. And in the QA of it. We do all of this work on the front end so that we get all of the fun at the end with the statistics and can analyze data that we know are scientifically sound, are well collected, and can lead to, you know, really helpful scientific conclusions.  

[00:09:33] Erin Spain, MS: Tell me about that synergy between the clinicians and the other investigators that you're working with on these projects.  

[00:09:41] Denise Scholtens, PhD: It is always exciting, often entertaining. Huge range of scientific opinion and expertise and points of view, all of which are very valid and very well informed. All of the discussion that could go into designing and launching a study, it's just phenomenally interesting and trying to navigate all of that and help bring teams to consensus in terms of what is scientifically most relevant, what's going to be most impactful, what is possible given the logistical strengths. Taking all of these well informed, valid, scientific points of view and being a part of the team that helps integrate them all toward a cohesive study design and a well executed study. That's a unique part of the challenge that we face here at NUDACC, but an incredibly rewarding one. It's also such an honor and a gift to be able to work with such a uniformly gifted set of individuals. Just the clinical researchers who devote themselves to these kinds of studies are incredibly generous, incredibly thoughtful and have such care for their patients and the individuals that they serve, that to be able to sit with them and think about the next steps for a great study is a really unique privilege.  

[00:10:51] Erin Spain, MS: How unique is a center like this at a medical school?  

[00:10:55] Denise Scholtens, PhD: It's fairly unique to have a center like this at a medical school. Most of the premier medical research institutions do have some level of data coordinating center capacity. We're certainly working toward trying to be one of the nation's best, absolutely, and build up our capacity for doing so. I'm actually currently a part of a group of data coordinating centers where it's sort of a grassroots effort right now to organize ourselves and come up with, you know, some unified statements around the gaps that we see in our work, the challenges that we face strategizing together to improve our own work and to potentially contribute to each other's work. I think maybe the early beginnings of a new professional organization for data coordinating centers. We have a meeting coming up of about, I think it's 12 to 15 different institutions, academic research institutions, specifically medical schools that have centers like ours to try to talk through our common pain points and also celebrate our common victories.  

[00:11:51] Erin Spain, MS: I want to shift gears a little bit to talk about some of your research collaborations, many of which focus on maternal and fetal health and pregnancy. You're now involved with a study with folks at the Ohio State University that received a 14 million grant looking at the effectiveness of aspirin in the prevention of hypertensive disorders in pregnancy. Tell me about this work.  

[00:12:14] Denise Scholtens, PhD: Yes, this is called the aspirin study. I suppose not a very creative name, but a very appropriate one. What we'll be doing in this study is looking at two different doses of aspirin for trying to prevent maternal hypertensive disorders of pregnancy in women who are considered at high risk for these disorders. This is a huge study. Our goal is to enroll 10,742 participants. This will take place at 11 different centers across the nation. And yes, we at NUDACC will serve as the data coordinating center here, and we are partnering with the Ohio State University who will house the clinical coordinating center. So this study is designed to look at two different doses to see which is more effective at preventing hypertensive disorders of pregnancy. So that would include gestational hypertension and preeclampsia. What's really unique about this study and the reason that it is so large is that it is specifically funded to look at what's called a heterogeneity of treatment effect. What that is is a difference in the effectiveness of aspirin in preventing maternal hypertensive disorders, according to different subgroups of women. We'll specifically have sufficient statistical power to test for differences in treatment effectiveness. And we have some high priority subgroups that we'll be looking at. One is a self-identified race. There's been a noted disparity in maternal hypertensive disorders, for individuals who self identify according to different races. And so we will be powered to see if aspirin has comparable effectiveness and hopefully even better effectiveness for the groups who really need it, to bring those rates closer to equity which is, you know, certainly something we would very strongly desire to see. We'll also be able to look at subgroups of women according to obesity, according to maternal age at pregnancy, according to the start time of aspirin when aspirin use is initiated during pregnancy. So that's why the trial is so huge. For a statistician, the statisticians out there who might be listening, this is powered on a statistical interaction term, which doesn't happen very often. So it's exciting that the trial is funded in that way.  

[00:14:27] Erin Spain, MS: Tell me a little bit more about this and how your specific skills are going to be utilized in this study.  

[00:14:32] Denise Scholtens, PhD: Well, there are three biostatistics faculty here at Northwestern involved in this. So we're definitely dividing and conquering. Right now, we're planning this study and starting to stand it up. So we're developing our statistical analysis plans. We're developing the database. We are developing our randomization modules. So this is the piece of the study where participants are randomized to which dose of aspirin they're going to receive. Because of all of the subgroups that we're planning to study, we need to make especially sure that the assignments of which dose of aspirin are balanced within and across all of those subgroups. So we're going to be using some adaptive randomization techniques to ensure that that balance is there. So there's some fun statistical and computer programming innovation that will be applied to accomplish those things. So right now, there are usually two phases of a study that are really busy for us. That's starting to study up and that's where we are. And so yes, it is very busy for us right now. And then at the end, you know, in five years or so, once recruitment is over, then we analyze all the data,  

[00:15:36] Erin Spain, MS: Are there any guidelines out there right now about the use of aspirin in pregnancy. What do you hope that this could accomplish?  

 Prescribing aspirin use for the prevention of hypertension during pregnancy is not uncommon at all. That is actually fairly routinely done, but that it's not outcomes based in terms of which dosage is most effective. So 81 milligrams versus 162 milligrams. That's what we will be evaluating. And my understanding is that clinicians prescribe whatever they think is better, and I'm sure those opinions are very well informed but there is very little outcome based evidence for this in this particular population that we'll be studying. So that would be the goal here, would be to hopefully very conclusively say, depending on the rates of the hypertensive disorders that we see in our study, which of the two doses of aspirin is more effective. Importantly, we will also be tracking any side effects of taking aspirin. And so that's also very much often a part of the evaluation of You know, taking a, taking a drug, right, is how safe is it? So we'll be tracking that very closely as well. Another unique part of this study is that we will be looking at factors that help explain aspirin adherence. So we are going to recommend that participants take their dose of aspirin daily. We don't necessarily expect that's always going to happen, so we are going to measure how much of their prescribed dose they are actually taking and then look at, you know, factors that contribute to that. So be they, you know, social determinants of health or a variety of other things that we'll investigate to try to understand aspirin adherence, and then also model the way in which that adherence could have affected outcomes.  

Erin Spain, MS: This is not the first study that you've worked on involving maternal and fetal health. Tell me about your interest in this particular area, this particular field, and some of the other work that you've done.  

[00:17:31] Denise Scholtens, PhD: So I actually first got my start in data coordinating work through the HAPO study. HAPO stands for Hyperglycemia Adverse Pregnancy Outcome. That study was started here at Northwestern before I arrived. Actually recruitment to the study occurred between 2000 and 2006. Northwestern served as the central coordinating center for that study. It was an international study of 25,000 pregnant individuals who were recruited and then outcomes were evaluated both in moms and newborns. When I was about mid career here, all the babies that were born as a part of HAPO were early teenagers. And so we conducted a follow up study on the HAPO cohort. So that's really when I got involved. It was my first introduction to being a part of a coordinating center. As I got into it, though, I saw the beauty of digging into all of these details for a huge study like this and then saw these incredible resources that were accumulated through the conduct of such a large study. So the data from the study itself is, was of course, a huge resource. But then also we have all of these different samples that sit in a biorepository, right? So like usually blood sample collection is a big part of a study like this. So all these really fun ancillary studies could spin off of the HAPO study. So we did some genomics work. We did some metabolomics work. We've integrated the two and what's called integrated omics. So, you know, my work in this space really started in the HAPO study. And I have tremendously enjoyed integrating these high dimensional data types that have come from these really rich data resources that have all, you know, resulted because of this huge multicenter longitudinal study. So I kind of accidentally fell into the space of maternal and fetal health, to be honest. But I just became phenomenally interested in it and it's been a great place.  

[00:19:24] Erin Spain, MS: Would you say that this is also a population that hasn't always been studied very much in biomedical science?  

[00:19:32] Denise Scholtens, PhD: I think that that is true, for sure. There are some unique vulnerabilities, right, for a pregnant individual and for the fetus, right, and in that situation. You know, the vast majority of what we do is really only pertaining to the pregnant participant but, you know, there are certainly fetal outcomes, newborn outcomes. And so, I think conducting research in this particular population is a unique opportunity and there are components of it that need to be treated with special care given sort of this unique phase of human development and this unique phase of life.  

[00:20:03] Erin Spain, MS: So, as data generation just really continues to explode, and technology is advancing so fast, faster than ever, where do you see this field evolving, the field of biostatistics, where do you see it going in the next five to ten years?  

[00:20:19] Denise Scholtens, PhD: That's a great question. I think all I can really tell you is that I'm continually surprised by new data types. I think that we will see an emergence of a whole new kind of technology that we probably can't even envision five years from now. And I think that the fun part about being a biostatistician is seeing what's happening and then trying to wrap your mind around the possibilities and the actual nature of the data that are collected. You know, I think back to 2004 and this whole high throughput space just felt so big. You know, we could look at gene transcription across the genome using one technology. And we could only look at one dimension of it. Right now it just seems so basic. When I think about where the field has come over the past 20 years, it's just phenomenal. I think we're seeing a similar emergence of the scale and the type of data in the imaging space and in the wearable space, with EHR data, just. You know, all these different technologies for capturing, capturing things that we just never even conceived of before. I do hope that we continue to emphasize making meaningful and translatable conclusions from these data. So actionable conclusions that can impact the way that we care for others around us. I do hope that remains a guiding principle in all that we do.  

[00:21:39] Erin Spain, MS: Why is Northwestern Medicine and Northwestern Feinberg School of Medicine such a supportive environment to pursue this type of work?  

[00:21:47] Denise Scholtens, PhD: That's a wonderful question and one, honestly, that faculty candidates often ask me. When we bring faculty candidates in to visit here at Northwestern, they immediately pick up on the fact that we are a collaborative group of individuals who are for each other. Who want to see each other succeed, who are happy to share the things that we know and support each other's work, and support each other's research, and help strategize around the things that we want to accomplish. There is a strong culture here, at least in my department and in my division that I've really loved that continues to persist around really genuinely collaborating and genuinely sharing lessons learned and genuinely supporting each other as we move toward common goals. We've had some really strong, generous leadership who has helped us to get there and has helped create a culture where those are the guiding principles. In my leadership role is certainly something that I strive to maintain. Really hope that's true. I'm sure I don't do it perfectly but that's absolutely something I want to see accomplished here in the division and in NUDACC for sure.  

[00:22:50] Erin Spain, MS: Well, thank you so much for coming on the show and telling us about your path here to Northwestern and all of the exciting work that we can look forward to in the coming years.  

[00:22:59] Denise Scholtens, PhD: Thank you so much for having me. I've really enjoyed this.  

[00:23:01] Erin Spain, MS: You can listen to shows from the Northwestern Medicine Podcast Network to hear more about the latest developments in medical research, health care, and medical education. Leaders from across specialties speak to topics ranging from basic science to global health to simulation education. Learn more at feinberg. northwestern.edu/podcasts.  

Data Tables

These tables present detailed data on the demographic characteristics, educational history, sources of financial support, and postgraduation plans of doctorate recipients. The Survey of Earned Doctorates (SED) data tables were reorganized and renumbered in 2021; see table B-1 in the " Technical Notes " for a crosswalk comparing the current tables with those prior to 2021. Explore SED data further via the interactive data tool and the Restricted Data Analysis System . Kelly Kang Survey Manager, SED NCSES

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Trends in research doctorate recipient characteristics

Trends in postgraduation commitments of research doctorate recipients, field and demographic characteristics of research doctorate recipients, financial support and education-related debt of research doctorate recipients, educational and background characteristics of research doctorate recipients, postgraduation commitments and salaries of research doctorate recipients, doctorate institutions, locations, and countries of origins of research doctorate recipients, statistical profiles of research doctorate recipients, postgraduation plans of research doctorate recipients.

Community Data Fellow Stephania Tello Zamudio helps broaden internet access for Illinois residents

After an intensive winter of data pipeline building, running analyses, and team consultations, the winter cohort of DSI Community Data Fellows gathered to present their collaborative research with various non-profits and social impact organizations. For graduate student Stephania Tello Zamudio (MSCAPP ‘24), this wasn’t a new experience.

“Last quarter I was working with the Internet Equity Initiative and directly with Alexis Schrubbe, the director of the Initiative,” said Zamudio. “We were working on a project looking at Internet access in public libraries.” This work led naturally into the following quarter.

Zamudio spent the winter quarter focused on a new project: cleaning and organizing a ‘challenge test’ collected through the BEAD Challenge with DSI’s Internet Equity Initiative (IEI). As part of a broader infrastructure investment, Illinois is slated to receive $1 billion in federal funding dedicated to enhancing the state’s internet infrastructure under the Broadband Equity Access and Deployment (BEAD) Program. IEI was asked to build the tool used for tracking internet infrastructure, with the goal of highlighting regions lacking adequate broadband internet service. This would help ensure funding goes towards communities with the most need.

This tool allowed users to test the speed of their internet connection, both downloading and uploading speeds, and the user could then share the recorded results with IEI to collate for the state broadband office. Zamudio stepped in after the initial data collection and was focused on turning those responses into actionable datasets.

Over the course of the eight weeks, Zamudio created a process for matching speed test results with the geospatial data of the tests and helped match the results to specific internet service providers (ISPs), allowing for the data collected by IEI to be submitted to the federal government for inclusion in their broadband mapping project.

In particular, she identified community anchor institutions, an important class of broadband serviceable locations that were left off of the Federal Communication Commission (FCC)’s Broadband Map. This work resulted in a list of places to engage, including a specific request from the state library of Illinois: to direct libraries to participate in the BEAD challenge process.

Using the DSI BEAD challenge, users like local libraries could test their internet connection speeds with the simple click of a button. The user then had the option to submit these results, along with information about their geographic location and ISP, to IEI for analysis and reporting back to the state broadband office.

By getting people from across the state to participate in the BEAD challenge, Illinois can focus funds on communities that aren’t adequately served by their ISPs. During the 60-day window of the speed challenge process, over 12,000 speed tests were captured from across the state.

Community Data Fellows: data science with impact

Zamudio first heard about the Community Data Fellows (CDF) through a friend in her program. “I heard about CDF through a friend that participated in the first quarter. He mentioned the fact that he was working directly with organizations around the area and doing real work for them,” said Zamudio. “That was one of the reasons I’m getting this degree, because of my interest in this intersection between data science and the public good.”

Community Data Fellows is a program that pairs local, community-based nonprofit organizations with UChicago graduate students who want to further develop their skills in technology and policy analysis. Zamudio said that working on the project “amplified [her] belief that data science can be a very important tool to improve the lives of communities. The BEAD Challenge project, in particular, served as a bridge between the citizens of Illinois and the government by facilitating access to the public funding they needed.”

Reflecting on Zamudio’s experience

The IEI’s BEAD challenge offered Zamudio a unique opportunity to work across multi-institutional teams. Understanding data as the shared product, and which institutions provide the data sources, as well as the requirements for that data, offered an interesting perspective that she hadn’t previously experienced.

“Steph rose to the occasion to hone new skills in a short period of time in order to see the unseen,” said Alexis Schrubbe, Director of the Internet Equity Initiative. “By doing so, she helped a team of dedicated researchers direct much-needed funding to technologically underserved areas in Illinois during this once-in-a-generation investment in infrastructure.”

Zamudio looks forward to seeing her work improve the internet access of people most in need, based on the BEAD challenge speed test. And while her work on the project has ended with the conclusion of the winter quarter, the skills she developed through working with IEI will carry on.

Zamudio had lots of praise of the community data fellows program, stating that it taught her how to keep code clean across multiple iterations, how to communicate with a data science team, and how to work more collaboratively using Github. It also inspired Zamudio to take a web mapping class.

“My first year in grad school, they taught us how to code,” Zamudio said. “But it isn’t until you start building practical experience that you realize how important it is to learn how to read other people’s code.”

This story was originally published by the Data Science Institute .

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