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100 Best universities for Data Science in China

Updated: February 29, 2024

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Below is a list of best universities in China ranked based on their research performance in Data Science. A graph of 1.33M citations received by 83.1K academic papers made by 304 universities in China 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. Tsinghua University

For Data Science

2. University of Hong Kong

3. Peking University

4. Hong Kong Polytechnic University

5. Shanghai Jiao Tong University

6. Dalian University of Technology

7. Zhejiang University

8. Wuhan University

9. Huazhong University of Science and Technology

10. Beihang University

11. Hong Kong University of Science and Technology

12. Chinese University of Hong Kong

13. Beijing University of Posts and Telecommunications

14. University of Electronic Science and Technology of China

15. Fudan University

16. Sun Yat - Sen University

17. Harbin Institute of Technology

18. Xi'an Jiaotong University

19. Nanjing University

20. City University of Hong Kong

21. National University of Defense Technology

22. Tongji University

23. South China University of Technology

24. Sichuan University

25. University of Science and Technology of China

26. University of Macau

27. Northwestern Polytechnical University

28. Beijing Institute of Technology

29. Southeast University

30. Central South University

31. Jinan University

32. Beijing Jiaotong University

33. Shenzhen University

34. Hefei University of Technology

35. Beijing Normal University

36. East China Normal University

37. Soochow University

38. Renmin University of China

39. Jilin University

40. Xidian University

41. Tianjin University

42. Beijing University of Technology

43. Nanjing University of Science and Technology

44. University of Science and Technology Beijing

45. Hong Kong Baptist University

46. Nanjing University of Posts and Telecommunications

47. Shanghai University

48. Xiamen University

49. Peking Union Medical College

50. Shandong University

51. Northeastern University, China

52. Hohai University

53. Nankai University

54. Hunan University

55. Wuhan University of Technology

56. Nanjing Normal University

57. Fuzhou University

58. Chongqing University

59. Southwest Jiaotong University

60. North China Electric Power University

61. Nanjing University of Aeronautics and Astronautics

62. Central China Normal University

63. Nanjing University of Information Science and Technology

64. Zhejiang University of Technology

65. University of Shanghai for Science and Technology

66. East China University of Science and Technology

67. South China Agricultural University

68. Hangzhou Normal University

69. Capital Medical University

70. Xi'an Jiaotong-Liverpool University

71. Yunnan University

72. Guangzhou University

73. China University of Mining and Technology

74. South China Normal University

75. Guangdong University of Technology

76. Zhongnan University of Economics and Law

77. Zhejiang University of Science and Technology

78. chongqing university of posts and telecommunications.

79. Shandong University of Science and Technology

80. Southwest University

81. Harbin Engineering University

82. Macau University of Science and Technology

83. Northeast Normal University

84. Yangzhou University

85. Southern Medical University

86. Shanghai University of Engineering Science

87. Hangzhou Dianzi University

88. Donghua University

89. China Agricultural University

90. University of Nottingham Ningbo, China

91. Jiangsu University

92. China University of Petroleum - Beijing

93. Lanzhou University

94. Beijing University of Chemical Technology

95. Qingdao University

96. Southwestern University of Finance and Economics

97. Ocean University of China

98. Dalian Maritime University

99. Henan University

100. Guangdong University of Foreign Studies

The best cities to study Data Science in China based on the number of universities and their ranks are Beijing , Hong Kong , Shanghai , and Dalian .

Computer Science subfields in China

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Introduction

• Data Science and Big Data Technology
• Computer Science and Engineering
• Financial Engineering
• M.Sc. in Data Science
• M.Sc. in Financial Engineering
• M.Sc. in Artificial Intelligence and Robotics
• M.Sc. in Bioinformatics
• M.Phil.-Ph.D. Programme in Data Science
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M.Phil. - Ph.D. Programme in Data Science - Introduction

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CUHK-Shenzhen has designed the programme for exceptionally motivated and mathematically talented students who wish to pursue a higher degree in the interdisciplinary research areas of Data Science, Operations Research and Management, Statistics, Computer Science, Optimization and Machine Learning . Admitted students will have opportunities to work in Shenzhen Research Institute of Big Data or Shenzhen Institute of Artificial Intelligence and Robotics for Society on machine or artificial intelligence. SDS aims to produce the best trained MPhil-PhD students who will be capable of becoming faculty members at top research universities or working in leading research and development labs in industry.

Students admitted were majored in computer science, mathematics and applied mathematics, statistics, information science, and industrial engineering, etc., from well-known universities like UC-Berkeley, New York University, the University of Hong Kong, Hong Kong University of Science and Technology, Tsinghua University, National University of Singapore, Southeast University.

Contact: 0755-2351-7011 (Monday to Friday 9:00-11:30, 14:00-17:30, except holidays) 

Email: [email protected]

Office: Room 421, Dao Yuan Building, 2001 Longxiang Road, Longgang District, Shenzhen

sds.cuhk.edu.cn

PhD in Big Data Science and Technology

Shenzhen university ( ).

The PhD in Big Data Science and Technology at Shenzhen University (SZU) is a 3 years long program for international students, taught in English.

📖 Introduction

Apply online now at Shenzhen University, Shenzhen, China for 2022 intake. Admissions for international students, scholarships, and more.

🏫 About Shenzhen University (SZU)

About Shenzhen University

Since its foundation 35 years ago, Shenzhen University has closely followed the pace of Shenzhen Special Economic Zone and has been committed to reform and rapid development. The University adheres to the motto of "self-reliance, self-discipline, self-improvement" and strives to become "the Special Economic Zone University, the Window University, and the Experimental University."

The University is committed to high-quality social service in the pursuit of excellence in teaching, learning, and research. It is a comprehensive university which offers a wide range of undergraduate and postgraduate programs and provides leading-edge facilities and excellent services to students, faculty, and staff.

As a unique institution of higher learning in the Shenzhen Special Economic Zone, the University promotes a distinctive and innovative campus culture. Shenzhen University has an enrollment of 35,455 full-time students, which include 28,674 undergraduates, 6,433 postgraduates, 348 doctoral students. And besides, there are 1,744 part-time postgraduates, 21,022 adult education students, and 837 international students. Shenzhen University is a comprehensive university with a variety of disciplines, including philosophy, literature, economics, law, education, science, engineering, management, medicine, history, and arts, eleven disciplines in total. 

Shenzhen University Scholarships

As the first public funding foundation specially established for assisting overseas students, Shenzhen Universiade International Scholarship Foundation is established by Shenzhen with sums of money for the purpose of promoting University spirits and boosting the cultural and educational exchange of international youth. You can choose from partial and full-time scholarships such as the Guangdong Provincial Government Scholarship, Shenzhen Universiade International Scholarship, and Government Scholarships.

Campus Life and Facilities

There is a university hospital, a post office, small shops, book booths and a securely guarded international students’ dormitory building that is elegantly and harmoniously embedded in the colorful woods and lawns by the Wenshan Lake. 

If you are a sports enthusiast, you will appreciate that the university has the best sports facilities in all the universities in China, such as two gymnasiums, two swimming pools, table tennis, and bowling center, football fields, basketball courts, tennis courts, a golf driving range and a large standard stadium, which supply full functions.  

🏠 Accommodation

On-campus House

There are two international students’ dormitory buildings on campus, at a favorable rate: twin room with bathroom, shower, color-TV, refrigerator, telephone, Internet access and a single room with bathroom, shower, color-TV, refrigerator, telephone, Internet access. Besides, the dormitory building has a meeting room, a reading room, cafe, laundry, and microwave ovens.

Off-campus House

Those who do not like to live on campus may choose to rent apartments with reasonable charge in the nearby communities: Chuanghua Apartment or Feicui Mingzhu Apartment which takes only 20 minutes walking to Shenzhen University.

• You will need to book the accommodation after you have been accepted.
• You can choose to live on campus or off campus in private accommodation.
• We have an article about how to find accommodation off campus here .

Application Fee:

Tuition fee:

40,000 CNY per year

120,000 CNY in total

Insurance is 800 CNY .

❓ ✅ ❌ Entry Requirements

The minimum age is 18 and the maximum age is 40.

Minimum education level: Master's

All students from all countries are eligible to apply to this program.

📬 Admissions Process

3 steps to apply to a chinese university.

Choose Programs

Apply Online

Enroll in China

Please choose the programs here , "You are advised to select 2-3 programs to increase your chances of getting accepted.

Required Documents:

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• Your Passport Copy

Preparing documents:

You can start your application now and send the application documents during your application. Some documents you can send later if you don’t have them right away. Some more info about preparing application documents is here

Application process:

Applying Online is simple in just a few steps. More information is available here .

The first steps are to choose the programs, pay the application fee and upload the application documents.

Once submitted to China Admissions, we will review your application within 2-3 days and proceed to the university or ask you for further clarification

After it has been processed to the university you will receive your unique application ID from each university.

The university may contact you directly for further questions.

We will then follow up each week with the university for updates. As soon as there is any update we will let you know. If you have made other plans, decide to withdraw / change address at any time please let us know.

After you have been accepted you will receive your admissions letter electronically and asked to pay the non-refundable deposit to the university.

Once you have paid the deposit the university will issue you the admissions letter and visa form to your home country.

❓ Have a Question?

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📝 Shenzhen University (SZU) Reviews

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QS World University Rankings by Subject: Data Science 2023

Discover which universities around the world are the best for your chosen subject with the QS World University Rankings by Subject 2023. 

The QS World University Rankings by Subject 2023 cover a total of 54 disciplines, grouped into five broad subject areas.

The QS World University Rankings by Subject are compiled annually to help prospective students identify the leading universities in a particular subject. Research citations, along with the results of major global surveys of employers and academics, are used to rank universities. More information about this year’s methodology is available.

This year’s rankings include three new subjects: data science , history of art , and marketing . All 54 tables included in this year’s QS World University Rankings by Subject can be accessed by clicking the links below. Let us know what you think of the results on Sina Weibo .

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The overall research goal of Institute of Operations Research and Data Science is to make a profound impact at national/international level and to improve the prosperity of the nation through innovative applications of Operations Research and Data Science methods.

Faculty members are actively engaged in a variety of research projects that have been making important contributions to both the theory and practice of operations research and data science. The areas of research concentration include optimization methods, combinatorial optimization, queuing, behavioral operations research, stochastic programming, stochastic model, fuzzy theory, uncertainty theory, network design, logistics and supply chain, simulation, and intelligent transportation systems. These research directions reflect the interests and creativity of the faculty members and students.

Select journals we publish in

 Annals of Operations Research   Computational Optimization and Applications   Computers & Industrial Engineering   Computers & Operations Research   Cybernetics and System Analysis   European Journal of Operations Research   Flexible Services and Manufacturing Journal   IEEE Transactions on Intelligent Transportation Systems   IEEE Intelligent Systems   IIE Transactions   International Journal of Computational Science and Engineering   International Journal of Production and Economics   International Journal of Production Research   International Journal of Systems Science   Journal of Global Optimization   Journal of Optimization Theory and Applications   Journal of Operations Research Society   Manufacturing and Service Operations Management   Mathematics of Operations Research   Naval Research Logistics   Operations Research   Operations Research Letters   Queueing Systems

Research Projects

Projects funded by National Science Foundation of china

Distinguished Young Scholar     -Operations Research and Management Science

Key projects     -Research on supply chain theory and method based on behavioral operations research

Regular and young investigator projects     - Research on periodic review inventory management for multiple stochastic demand classes     -Coordination of Order Pricing and Scheduling under MTO, funded by NSF China,     -Decision Making on Crude Purchasing and Transportation under Uncertainty, National Science Foundation of China,     -Parallel Service Management Study on Chain Retailing Industry     -Building resilience against supply risks in supply chain management: a stochastic programming approach     -Study on Optimization Problems Related to Protein Folding     -Research on a new mode of production management     -Discrete Choice Based Recommender Systems  

Projects funded by Ministry of Education

  -Integration of Facility Location and Sizing, funded by SRF for ROCS, SEM, China    -Study on Minimization Methods for Non-convex Functions    -Choice-based airline schedule design 

Projects funded by domestic companies

 -China Railway Container Transpiration Corporation: A Study on Container Management Model    -China Railway Tielong Container Logistics Corporation: Service Capacity and Policy Study on Tielong Cold Chain Logistics    -Sinotrans Air Transportation Development Co. Ltd.: Local networks design in Beijing hub and its regional networks    -Sinotrans Air Transportation Development Co. Ltd.: Re-design the sorting center in Beijing hub    -COSCONET: Logistic planning in tobacco industry    -Sinopec Maoming Company: Risk control and optimization on Crude Purchasing and Transportation, September 2009-December 2010.    -Sinopec Maoming Company: Decision Making Model for Human Resource and Training Management    -Institute of Automation, Chinese Academy of Science: Information Fusion, Integration and Computational Experiments on Urban Traffic;    -the Institute of Beijing Metro Design: Emergency resource allocation for Beijing metro system.    -Beijing Mass Transit Railway Operation Corporation, Design and Research Institute: Passenger Management at Transfer Stations of Urban Rail Transit     -IBM Research – China: Carbon emission evaluation indicator system and decision support system for the China Railways    -Beijing Wuzi University: Warehouse and sorting information system design.    -Shandong Lvbang Tech. Company: Maintenance Network Planning for Gas Stations     -Study on Apartment Assignment Plan in Students Dormitory Management of Tsinghua University from the year of 2009 to 2011    -Study on Representative Data Selection, Statistical Modeling, Optimization and Algorithm Evaluation for Validations    -Study on Optimization Problems Related to Protein Folding    -Study on Minimization Methods for Non-convex Functions    -Study on Apartment Assignment Plan in Students Dormitory Management of Tsinghua University from the year of 2009 to 2011    -2009 Projects of Editing Excellent Textbooks for  Beijing High Education Including Operations Research (Mathematical Programming)    -Study on Representative Data Selection, Statistical Modeling, Optimization and Algorithm Evaluation for Validations 

Funded by Foreign Companies

  -General Mills Operations, LLC (USA): Dynamic inventory management    -Mitsubishi Heavy Industry Co. (Japan): Development of optimization solver for constrained traveling salesman and vehicle routing problems    -Mitsubishi Heavy Industry Co. (Japan): Production plan modifying system: supply chain disruption management    -Mitsubishi Heavy Industry Co. (Japan): Study on inventory management with shareable parts for multiple stochastic demand classes    -Mitsubishi Heavy Industry Co. (Japan): Warehouse planning and inventory management    -Mitsubishi Heavy Industry Co. (Japan): Automatic routing of piping layout    -Mitsubishi Heavy Industry Co.(Japan): Spatial Scheduling in Large-scale Manufacturing System    -Rotterdam School of Management, Erasmus University (The Netherlands): A simulation study on supply chain visibility in ocean container transportation (EU FP-7, INTEGRITY) 

Collaborations

Government and Research Institutes

  -National Development and Reform Commission    -State-owned Assets Supervision and Administration Commission of the State Council (SASAC)    -Ministry of Industry and Information Technology of the People's Republic China (MIIT)    -Princeton University    -North Carolina State University    -Ohio State University at Columbus    -University of California, Berkeley    -Missouri University of Science and Technology    -Eindhoven University of Technology    -Rotterdam School of Management, Erasmus University    -National Tsinghua University    -IBM China Research Laboratory    -Institute of Automation, Chinese Academy of Science    -Academy of Mathematics and Systems Science, Chinese Academy of Sciences    -Beijing Mass Transit Railway Operation Corporation, Design and Research Institute 

Domestic Corporations

  -China Railway Tielong Container Logistics Co., Ltd    -China Railway Container Transpiration Corporation    -Sinotrans Air Transportation Development Co., Ltd.    -Sinopec Maoming Company    -COSCONET (Beijing) Co. Ltd. 

International Corporations

  -Boeing    -General Motor    -General Mills Operations, LLC (USA)    -Mitsubishi Heavy Industry Co. (Japan) 

Group Awards

 2009 Projects of Editing Excellent Textbooks for Beijing High Education Including Operations Research (Mathematical Programming)   2007 IIE Innovations in Curriculum Award   2002 Secondary Award of China Mechanical Engineering Association

 Huang, H., Operations Research: Mathematical Programming, Tsinghua Press, 2011.   Zhao, X. and S. Huang, Inventory Management, Tsinghua Press, 2008.   Huang, H. and J. Han, Mathematical Programming, Tsinghua Press, 2006 

Department of Industrial Engineering, Tsinghua University Phone： 010-62772989 Fax：010-62794399 E-mail：[email protected] Address：Shunde Building, Tsinghua University, Beijing 100084

Copyright © 2014-2024 Department of Industrial Engineering, Tsinghua University

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NYU Shanghai offers various opportunities for students to pursue advanced doctoral study and research culminating in the award of a PhD degree from New York University. These opportunities are made possible through unique collaborations with other schools and departments of NYU, including the NYU Graduate School of Arts and Science, the NYU Tandon School of Engineering, and the NYU Wagner Graduate School of Public Service. Currently, we are recruiting for outstanding PhD candidates in the following subjects: Biology, Chemistry, Computer Science, Data Science, Electrical Engineering, Mathematics, Neural Science, Physics, Public Administration, Sociology, and Transportation Planning and Engineering.

Students accepted into these programs normally begin by spending their first year in New York City for foundational PhD coursework, completed alongside other NYU PhD students in their discipline, before transitioning full-time to research groups led by NYU Shanghai faculty.

All students are supported through a generous financial aid program - the NYU Shanghai Doctoral Fellowship, which includes scholarships covering NYU tuition and fees, annual stipends, travel funds, and enrollment in an international health insurance plan.

In these programs, students receive a world-class NYU doctoral education, along with a truly distinctive global experience across multiple NYU campuses. In Shanghai, students benefit from NYU Shanghai’s cutting-edge research environment, where new world-class platforms for discovery and exploration, training, and international collaboration are being pioneered, all while living in one of the world’s most incomparable cities.

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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)

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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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Center for Security and Emerging Technology

China is Fast Outpacing U.S. STEM PhD Growth

Remco Zwetsloot

Jack Corrigan

Emily S. Weinstein

Dahlia Peterson

Diana Gehlhaus

Ryan Fedasiuk

Since the mid-2000s, China has consistently graduated more STEM PhDs than the United States, a key indicator of a country’s future competitiveness in STEM fields. This paper explores the data on STEM PhD graduation rates and projects their growth over the next five years, during which the gap between China and the United States is expected to increase significantly.

Executive Summary

This paper compares the STEM PhD pipelines of the United States and China. We find that China has consistently produced more STEM doctorates than the United States since the mid-2000s, and that the gap between the two countries will likely grow wider in the next five years. Based on current enrollment patterns, we project that by 2025 Chinese universities will produce more than 77,000 STEM PhD graduates per year compared to approximately 40,000 in the United States. If international students are excluded from the U.S. count, Chinese STEM PhD graduates would outnumber their U.S. counterparts more than three-to-one.

Our findings also suggest the quality of doctoral education in China has risen in recent years, and that much of China’s current PhD growth comes from high-quality universities. Approximately 45 percent of Chinese PhDs graduate from Double First Class (A) universities—the country’s most elite educational institutions (see Appendix D)—and about 80 percent of graduates come from universities administered by the central government. While it is possible that the growing supply of STEM PhDs in China exceeds current labor market demand, the quality and quantity of a country’s doctoral graduates is an important indicator of its future competitiveness, and China’s capacity to produce skilled PhD-level STEM experts appears to be growing rapidly.

Our analysis focuses on students who obtained a research-oriented doctoral degree in STEM disciplines. For the United States, this includes data on seven academic fields: life sciences, geosciences, mathematics and statistics, computer science, physical sciences, engineering, and medical sciences. For China, we include four academic fields tracked by its Ministry of Education: science, engineering, agriculture, and medicine. Historical trends and predictions can vary depending on the exact field categorization (e.g., whether the social sciences and/or the health sciences are included in graduate counts), but in all cases, Chinese PhD graduates are expected to clearly outnumber U.S. graduates by 2025.

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Cheung Receives NIH Grant to Research Water Contaminants and Human Health

Kei-Hoi Cheung, PhD , professor of biomedical informatics and data science, has been awarded a grant by the National Institute of Environmental Health Sciences (NIEHS) to research environmental health data and drinking water contamination using AI methods.

The U.S. Environmental Protection Agency (EPA) defines emerging contaminants , or contaminants of emerging concern, as “chemicals that have not previously been detected in water, or that are being detected at significantly different levels than expected.” These potential pollutants include pharmaceuticals, microplastics, and endocrine disrupting chemicals caused by industrial land use and agricultural runoff. Researchers and government agencies warn that these chemicals may pose adverse health and ecological effects.

Only a fraction of these contaminants have been extensively evaluated, but Cheung’s project aims to address this. The study will explore how new data and metadata standards can be used to harmonize diverse environmental health information. Integrating a variety of data types in this way could help other researchers investigate drinking water contaminants and their associated impact on human health. To extract and integrate these data types, Cheung’s team will deploy artificial intelligence (AI) techniques like natural language processing (NLP) and machine learning. They also plan to build an environmental exposure knowledge graph, and engage with users to evaluate the impact of their project.

“There is a great desire by the data science, exposure science, and epidemiology communities to use data and metadata standards to accelerate environmental research workflow, gain new knowledge, and increase data reuse,” said Cheung, who is also a professor of biostatistics at the Yale School of Public . “Bringing this desire to fruition requires a set of community-driven standards for describing environmental exposures and linking them to human health and disease-related data.”

Cheung's co-investigators at Yale include Nicole Deziel, PhD, MHS , associate professor of epidemiology, Vasilis Vasiliou, PhD , Susan Dwight Bliss Professor of Epidemiology, and Hua Xu, PhD, FACMI , Robert T. McCluskey Professor of Biomedical Informatics and Data Science. Mark Musen, professor of biomedical informatics at Stanford University, is also a co-investigator.

The grant will award $600,000 annually for the next five years.

Featured in this article

• Kei-Hoi Cheung, PhD Professor of Biomedical Informatics & Data Science; Professor, Biostatistics
• Nicole Deziel, PhD, MHS Associate Professor of Epidemiology (Environmental Health Sciences); Co-Director, Yale Center for Perinatal, Pediatric and Environmental Epidemiology (CPPEE)
• Vasilis Vasiliou, PhD Department Chair and Susan Dwight Bliss Professor of Epidemiology (Environmental Health Sciences) and of Ophthalmology and Visual Science and of Environment; Director, Yale Superfund Research Center; Affiliated Faculty, Yale Cancer Center; Affiliated Faculty, Yale Institute for Global Health; Co-Director, Environmental Health Sciences Track, Executive MPH
• Hua Xu, PhD Robert T. McCluskey Professor of Biomedical Informatics and Data Science; Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science; Assistant Dean for Biomedical Informatics, Yale School of Medicine

The Master of Science in Data Science (MSDS) is a professional master’s degree program. The residential MSDS full-time program begins with the second summer session, continues into the Fall term, and concludes at the end of the following Spring term (11 months in total). The online MSDS part-time program may be started in the Fall or Spring and can be completed in five semesters. 

Prerequisite Requirements

An applicant must hold a bachelor’s degree conferred by an accredited college or university. Proof of degree conferral issued by the undergraduate institution is required before an admitted applicant may start the program. Degree conferral is defined as official certification and confirmation of the date of degree completion from the undergraduate institution’s Student Records or Registrar office and is typically signified by receipt of a diploma or degree certificate and/or an official final transcript.

Regardless of the applicant’s undergraduate degree or major, completion of the following prerequisite courses are required:

• Single variable calculus
• Linear algebra or matrix algebra
• An introductory statistics course
• An introductory programming course

Prerequisite knowledge may be obtained through summer session courses at UVa, courses at another institution, online, or other forms of instruction as approved by the Office of Admission.

Prospective students must complete all parts of the application and submit it by the stated deadlines on the program website. Upon matriculation to the graduate program, the applicant must provide official transcripts of their baccalaureate record and any graduate-level work completed, and all official reports for test scores on their application.

Applicants whose first language is not English must complete the Test of English as a Foreign Language (TOEFL), which is administered by ETS, or the International English Language Testing System (IELTS). The minimum TOEFL (iBT) score requirement is 100 (including minimum section scores of 22 in speaking, 22 in writing, 23 in reading and 23 in listening). The minimum IELTS score requirement is 7.0 (including minimum section scores of 6.5). This requirement is typically waived for applicants who will have received a baccalaureate degree or its international equivalent entirely from a college or university in which English is the primary language of instruction.

Masters Degree

The degree of Master of Science will be conferred upon the holder of an approved baccalaureate degree who has fulfilled within the designated time limit all requirements set forth in the Graduate Record.

Academic Requirements

MSDS students must complete a minimum of 32 graded credits, which include 26 graded credits in core MSDS courses and 6 graded graduate-level elective credits with a grade of B- or higher. 

The requirements of the master’s degree must be completed in the original modality of study to which the student applied. Students are not permitted to deviate from their original modality of study unless for extenuating circumstances or compelling reasons. Approval is conditional on School resources such as enrollment limits. Petitions are granted upon approval of each MSDS program director and the Associate Dean for Faculty & Academic Affairs.

A student’s elective courses are selected in consultation with the program manager and program director. Additional discipline-specific requirements for the master’s degree are noted in the entries for the specific program.

Time Limitation

All requirements for the master’s degree must be completed within five years from the first term of enrollment.

MSDS Core Requirements

Course Requirements for the MSDS Program:

Computer Science

• CS 5012 - Foundations of Computer Science Credits: 3
• DS 5100 - Programming for Data Science Credits: 3

Linear Models

• STAT 6021 - Linear Models for Data Science Credits: 3

Practice and Application

• DS 6001 - Practice and Application of Data Science Credits: 3

Ethics of Big Data  

• DS 6002 - Ethics of Big Data I Credits: 2

Capstone (3 credits required)

• (   DS 6011 - Data Science Capstone Project Work I Credits: 1
• DS 6013 - Data Science Capstone Project Work II Credits: 1 to 3 )
• DS 6015 - Data Science Capstone Credits: 1 to 3

Data Mining and Machine Learning

• DS 6030 - Statistical Learning Credits: 3
• DS 6050 - Deep Learning Credits: 3

Bayesian Machine Learning

• DS 6040 - Bayesian Machine Learning Credits: 3

Electives (Residential Modality)

Students must select two courses (six credits minimum) of elective coursework in the spring term. Selection of elective courses is done in consultation with the program director. There are a variety of electives available, including (but not limited) to those listed below. Elective courses must be at the 5000-level or higher to count for elective credit. 

• CS 6160 - Theory of Computation Credits: 3
• CS 6444 - Introduction to Parallel Computing Credits: 3
• CS 6501 - Special Topics in Computer Science Credits: 3

      (Topics approved by SDS)

• CS 6750 - Database Systems Credits: 3
• DS 5001 - Exploratory Text Analytics Credits: 3
• DS 5110 - Big Data Systems Credits: 3
• DS 5111 - Data Engineering Credits: 3
• ECON 7720 - Econometrics II Credits: 4
• ECON 8720 - Time Series Econometrics Credits: 3
• EVSC 7070 - Advanced Use of Geographical Information Systems Credits: 3
• GCOM 7240 - Advanced Quantitative Analysis Credits: 3
• PHS 7310 - Clinical Trials Methodology Credits: 3
• PSYC 5720 - Fundamentals of Item Response Theory Credits: 3
• PSYC 7760 - Introduction to Applied Multivariate Methods Credits: 3
• SARC 5400 - Data Visualization Credits: 3
• STAT 6250 - Longitudinal Data Analysis Credits: 3
• STAT 6260 - Categorical Data Analysis Credits: 3
• SYS 6023 - Cognitive Systems Engineering Credits: 3
• SYS 6050 - Risk Analysis Credits: 3
• SYS 6582 - Selected Topics in Systems Engineering Credits: 1 to 3 (Topics approved by SDS) 
• SYS 7001 - System and Decision Sciences Credits: 3

Electives (Online Modality)

Students must select two courses (six credits minimum) of elective coursework. There are a variety of possible electives available, including (but not limited) to those listed below. Elective courses must be at the 5000-level or higher to count for elective credit in the program unless further approval is obtained.

• DS 5400 - Business Analytics for Data Science Credits: 3
• DS 5559 - New Course in Data Science Credits: 1 to 4

Requirements for the MSDS/MBA Combination (Formerly “Dual”) Degree Program

The School of Data Science offers a combination (formerly termed “dual”) degree program with the University of Virginia Darden Graduate School of Business Administration, in which the student may obtain the MS in Data Science (MSDS) degree and the Master of Business Administration (MBA) degree in two years, instead of the three years that would be required if each were undertaken separately.  All MSDS/MBA students complete a minimum of 60 credits for the MBA and a minimum of 32 credits for the MSDS. Students who complete all coursework to the satisfaction of each respective school and meet the graduation requirements of each school will be awarded two degrees: the Master of Science in Data Science and the Master of Business Administration.

Core Computer Science

Core Statistics

Core: Practice and Application of Data Science I & II

Core: Ethics of Data

Core Capstone Work

Core Systems & Information Engineering

Core Bayesian Machine Learning

Darden Required Courses  grade of B- or higher required

• GBUS 7351 - Decision Analysis Credits: 1.5
• GBUS 7352 - Decision Analysis - Part II Credits: 1.5

Elective: 5000-level or higher, at least 3 credits

Transfer Credits

The graduate programs normally require students to complete all requirements of the program during their period of enrollment in the program at the University of Virginia. Students must receive prior approval for transfer credit from the program director; if the course was taken prior to enrollment in the M.S. in Data Science, the student should make the formal request using the Transfer Credit Request form.

Students who enter the University of Virginia specifically as non-degree seeking School of Data Science students may apply up to 6 credits to the M.S. in Data Science program.  The 6 credits are limited to courses within the M.S. in Data Science curriculum.

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Slice of life: master of science in business analytics.

Fusing data science with business acumen for global impact in San Francisco and Silicon Valley

• May 15, 2024

The UC Davis Master of Science in Business Analytics develops high-performance professionals who can create business value from data and models. Our program blends data science skills with business knowledge and organizational savvy. You will build competencies in analytics, data, business, and practice. You'll learn to ask the right questions to deploy technical skills, data science tools and the managerial savvy to lead organizational change. You'll thrive in the global innovation hub of San Francisco and Silicon Valley, networking with trailblazers pushing the envelope on business analytics and data-driven decision-making.

Maanvee Mehrotra

Coming to school in San Francisco, it opens a lot of doors. It opens the hesitation of being a first-gen, immigrant, STEM woman. I belong here. I belong in the tech hub. And I'm in those spaces where I see people and women who are just making waves in the tech industry, the data industry. Personally, I want to have a career in data. For me, that is the most important thing.

Keyi (Eddy) Yu

MSBA program is basically the study of data analytics to solve business problems. and I have three years of working experience in the financial industry. Now, I think it's time to combine both together and pursue a future career in the data analytics field. We're not just learning about how to code, how to solve business problems. We are also focused on solving the problems ethically, regardless of which field you are working in.

Abdullah Kazi

Ethics in data science, ethics in data, and ethics in analysis. This is key because when you're working with data, you want to make sure that the data you grab is unbiased. Otherwise, it will lead to a biased analysis. I wanted to learn skills that I didn't know as an engineer. I wanted to go into analysis. I wanted to go into public speaking. So one thing MSBA prides itself on is the concept of data storytelling. How do you condense large amounts of technical information into a small chunk that you can present to your non-technical audience? That's how you kind of get recognition in the business world. MSBA program is located in San Francisco. You go north, you'll hit biotech area. You'll hit DNA-way. You go down south, you'll hit the finance district. So you see, everything is in San Francisco. There's actually a lot of resources that the MSBA program offers. And so one of them is the career services portal. And this is strictly for the MSBA students. Here we have access to resumé help. We have access to the best career staff in the world.

Prasad Naik

The Graduate School of Management is perhaps the only boutique school that you will get in the entire world. I know names of my students. Students know my name. We interact during the program. We interact after the program. Twenty years out, we still are in touch. That only happens when you have a small boutique program like the Graduate School of Management. I taught them advanced statistics and time series analysis. I bring my research into the program because I love time series and dynamics.

The professors who are teaching the MSBA program are doing research in my interest fields. And I chose UC Davis because of that.

Daniela Mallard Juarez

I chose UC Davis because it's one of the most recognized universities in the U.S., definitely in California. And I also really like the MSBA program it offers. It feels just like a community that is available to you to help you in any way. We are roughly 100 students from 11 different countries. It really combines and complements each other's skills and talents. People are united with this one common goal of academic excellence. And just like building up our skills in analytics. We always go back to these core values that the UC Davis community has, and making lifelong friends here.

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CfP: 2024-2025 Bloomberg Data Science Ph.D. Fellowship

Bloomberg is happy to announce an exciting funding opportunity for Ph.D. students. The seventh edition of the Bloomberg Data Science Ph.D. Fellowship Program invites Ph.D. students working in broadly-construed data science to apply for fellowships.

Our fellowship program, launched in 2018, provides the opportunity for outstanding Ph.D. candidates to be funded for up to three years of their Ph.D. studies to work on their research proposal. The recipients will collaborate and be supported by our Data Science community throughout this time and will complete 14-week summer internships with Bloomberg for the duration of their fellowships. Previous recipients of the fellowship are presented on the website.

Applications for the 2024-2025 academic year must be submitted by May 30, 2024. Fellowship recipients will be announced by July 15, 2024.

Full details about the fellowship, specific topics of interest for this year and application process can be found at: https://www.bloomberg.com/company/values/tech-at-bloomberg/data-science/...

We would appreciate it if you can share this opportunity with interested parties.

Please direct all questions and future communications to rdml [at] bloomberg.net .

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MS in Data Science

MSDS students choose among the many introductory graduate courses offered to students in the PhD program. These courses cover areas of computer science, optimization, linear algebra and statistics for students that have not had prior exposure to this required course work. Master’s students are fully integrated in the academic activities of the department alongside the PhD students.

Students must complete the required 5 core courses, 4 electives, and a final project to complete the program. There are also three foundational courses that students can test out. For the students who test out of foundational courses, the minimum number of courses taken in the program is 9. For the students who take all foundational courses, it is 12. These foundational courses can be taken in the summer before the program starts. Finally, students will be able to engage in a variety of opportunities across the Data Science Institute research programs and partnerships during their residency in the program.

The Curriculum

Foundational courses:.

Interested students will have the opportunity to test out of each of the 3 foundation courses below. Each of the courses will be offered in the late summer and offered online before the start of the fall quarter.

• Computational Foundations for Data Science
• Mathematical Foundations for Data Science
• Statistical Foundations for Data Science

Core Courses:

• Introduction to Data Science
• Systems for Data and Computers/Data Design
• Data Interaction
• Introduction to ML and AI  or Foundations of Machine Learning and AI Part I
• Responsible Use of Data and Algorithms

Four graduate-level electives can be selected from a wide variety of courses in Data Science, Computer Science, Statistics and across the University.

The online application portal will begin accepting applications for Fall 2024 admission in early Fall 2023. To ensure full consideration, applicants should apply by the deadline. The program may accept applications after the deadline if the cohort is not filled.