phd health data analytics

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Health Data Science - PHD

Program Guide

The PhD Program in Health Data Science trains the next generation of data science leaders for applications in public health and medicine. The program advances future leaders in health and biomedical data science by: (i) providing rigorous training in the fundamentals of health and biomedical data science, (ii) fostering innovative thinking for the design, conduct, analysis, and reporting of public health research studies, and (iii) providing practical training through real-world research opportunities at research centers and institutes directed by departmental faculty such as the Biostatistics Center (BSC), the Computational Biology Institute (CBI), and the Biostatistics and Epidemiology Consulting Service (BECS).

The PhD program consists of two concentrations; Biostatistics & Bioinformatics Concentration. Biostatistics is the science of designing, conducting, analyzing, and reporting studies aimed at advancing public health and medicine. Bioinformatics is the science of developing and applying computational algorithms and analysis methodologies to big biological data such as genetic sequences. Together they are foundational sciences for public health research and decision-making and essential to educating the next generation of leaders in health and biomedical data science.

The program takes advantage of the rich biostatistical and bioinformatics resources at GW and in the Nation’s Capital. Faculty in the Department of Biostatistics and Bioinformatics are engaged in a diverse research portfolio that includes areas such as diabetes, infectious diseases, mental health, maternal-fetal medicine, cardiovascular disease, emergency medicine, and oncology. Methodological interests of the faculty include the design and analyses of clinical trials including group-sequential and adaptive design, SMART trials, pragmatic trials, multiple testing, and benefit: risk evaluation; machine learning; meta-analyses; missing data; randomization tests, longitudinal data; the use of real-world data including electronic medical records; and research in biostatistics education methodologies. The Washington DC area is a hub for biostatisticians and bioinformaticians in government and industry, providing a rich source of adjunct faculty with relevant experience.  Specifically, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) have considerable human resources in these disciplines, many with world-class reputations. Several leading biostatisticians from the NIH are currently serving on doctoral committees and teach courses in the Milken Institute School of Public Health (GWSPH).

The program features a modernized applied curriculum, unique in its emphasis on cutting-edge data science techniques while retaining the rigor of traditional Biostatistics and Bioinformatics programs. The program prepares students to be independent researchers and effective collaborators in interdisciplinary studies.

APPLICATION DEADLINE: DECEMBER 1

Program Co-Directors:  Keith Crandall  (Bioinformatics Concentration)

Guoqing Diao  (Biostatistics Concentration)

Toshi Hamasaki  (Biostatistics Concentration)

GWSPH Doctoral programs admit students for the Fall term each academic year. Applications will be accepted beginning in August and are due no later than December 1st for the next matriculating cohort beginning in the following Fall term.  Find GWSPH graduate admissions information  here .

All applicants for the Biostatistics Concentration are required to submit current GRE scores (within five years of matriculation date). Applicants for the Bioinformatics Concentration are strongly encouraged to submit a GRE score.

Meeting the minimum requirements does not assure acceptance. Applicants must provide evidence of the completion of their undergraduate and/or graduate work before registration in GWSPH is permitted.

Concentration-Specific Prerequisites

Transfer Credits

Graduate courses taken prior to admission while in non-degree status may not be transferable into GWSPH programs. The PhD program is designed to serve students coming directly from an undergraduate degree. Students completing a master’s degree prior to admission to the PhD degree program may be eligible to transfer up to 24 credits toward the PhD coursework requirements. Depending on how many transfer credits are accepted, at minimum, 48 credits of additional coursework and dissertation research will be required.

PUBH 6080  | Pathways to Public Health (0 credits) PUBH 6421 | Responsible Conduct of Research (1 credit) PUBH 6850   | Introduction to SAS for Public Health Research (1 credit) PUBH 6851   | Introduction to R for Public Health Research (1 credit) PUBH 6852   | Introduction to Python for Public Health Research (1 credit)  PUBH 6860   | Principles of Bioinformatics (3 credits)  PUBH 6886   | Statistical and Machine Learning for Public Health Research (3 credits) PUBH 8099 | PhD Seminar: Cross Cutting Concepts in Public Health (1 credit) NOTE: In 23-24, PUBH 8099 was updated to PUBH 8001 PUBH 8870 | Statistical Inference for Public Health Research I* (3 credits)

CORE TOTAL: 14 CREDITS

SPH Course Descriptions

PUBH 6866   | Principles of Clinical Trials (3 credits)  PUBH 6869   | Principles of  Biostatistical Consulting (1 credit) PUBH 6879 | Propensity Score Methods for Causal Inference in Observational Studies (3 credits) PUBH 6887   | Applied Longitudinal Data Analysis for Public Health Research (3 credits) PUBH 8871   | Statistical Inference for Public Health Research II* (3 credits) PUBH 8875 | Linear Models in Biostatistics* (3 credits)  PUBH 8877   | Generalized Linear Models in Biostatistics* (3 credits) PUBH 8878 | Statistical Genetics (3 credits) PUBH 8879 | An Introduction to Causal Inference for Public Health Research (3 credits) PUBH 8880 | Statistical Computing for Public Health Research (3 credits) STAT 6227   | Survival Analysis (3 credits)

BIOSTATISTICS CONCENTRATION-SPECIFIC TOTAL: 28 CREDITS

* Courses are basis of comprehensive exam for the Biostatistics concentration.

PUBH 6854 | Applied Computing in Health Data Science (3 credits) PUBH 6859 | High Performance Cloud Computing (3 credits)  PUBH 6861   |  Public Health Genomics (3 credits)  PUBH 68 84   |  Bioinformatics  Algorithms and Data Structure (3 credits)  PUBH 8885   | Computational Biology (3 credits) 

BIOINFORMATICS CONCENTRATION-SPECIFIC TOTAL: 15 CREDITS

• Applied Biostatistics Concentration: 12 credits minimum
• Bioinformatics Concentration:  18 credits minimum

Both concentrations:  elective selections must include at least*:

• 3 Credits in Biostatistics 
• 3 Credits in Bioinformatics
• 3 Credits in a Cognate Area

All students are expected to work with their Advisor in the selection of their Elective coursework.

*Pre-approved elective courses are shown in the program guide for each category. 

 BIOSTATISTICS ELECTIVES MINIMUM: 12 CREDITS BIOINFORMATICS ELECTIVES MINIMUM: 18 CREDITS

PRACTICUM/TEACHING RESEARCH GTAP**   |   GradTeachingAsst Certification (This includes UNIV 0250 - Graduate Assistant Certification Course (1 credit) (both concentrations) (0; 1 credit) PUBH 8283 | Doctoral Biostatistics Consulting Practicum (Biostatistics concentration only) (2 credits) PUBH 8413 |  Research Leadership (both concentrations (1 credit)

** This is a requirement for TAs .

DISSERTATION RESEARCH PUBH 8999 |  Dissertation Research (varies by concentration - 12 minimum credits)

BIOSTATISTICS PRACTICUM/RESEARCH: 12-15 CREDITS BIOINFORMATICS PRACTICUM/RESEARCH: 12-24 CREDITS

Professional Enhancement

Students in degree programs must participate in eight hours of Professional Enhancement. These activities may be Public Health-related lectures, seminars, or symposia related to your field of study.

Professional Enhancement activities supplement the rigorous academic curriculum of the SPH degree programs and help prepare students to participate actively in the professional community. You can learn more about opportunities for Professional Enhancement via the Milken Institute School of Public Health Listserv, through departmental communications, or by speaking with your advisor.

Students must submit a completed  Professional Enhancement Form  to the student records department  [email protected] .

Collaborative Institutional Training Initiative (CITI) Training

All students are required to complete the Basic  CITI training module  in Social and Behavioral Research prior to beginning the practicum.  This online training module for Social and Behavioral Researchers will help new students demonstrate and maintain sufficient knowledge of the ethical principles and regulatory requirements for protecting human subjects - key for any public health research.

Academic Integrity Quiz

All Milken Institute School of Public Health students are required to review the University’s Code of Academic Integrity and complete the GW Academic Integrity Activity.  This activity must be completed within 2 weeks of matriculation. Information on GWSPH Academic Integrity requirements can be found  here.

Past Program Guides

Students in the PhD in Health and Biomedical Data Science program should refer to the guide from the year in which they matriculated into the program. For the current program guide, click the "PROGRAM GUIDE" button on the right-hand side of the page.

Program Guide 2023-2024 Program Guide 2022-2023 Program Guide 2021-2022

Lizhao (Agnes) Ge Email:   [email protected] Start year: 2021

Lizhao was born and raised in Zhejiang, China. She came to the United States for undergraduate studies at the University of Iowa, where she obtained a BS in Mathematics and a BBA in Finance, and a minor in Music. She earned a Master of Applied Statistics from the Pennsylvania State University and worked there as a Statistical Consultant after graduation. She joined the Antibacterial Resistance Leadership Group (ARLG) at the George Washington University Biostatistics Center as a biostatistician in 2020 and started her PhD journey in the Health and Biomedical Data Science (Applied Biostatistics track) in 2021. Her research interests are clinical trial designs, and application of the Desirability of Outcome Ranking (DOOR) in biomedical studies.

Yijie He Email:   [email protected] Start year: 2021

Yijie was born in China. Before coming to the George Washington University, he received a BS degree in Bioengineering from University of California San Diego and an MS degree in Biostatistics from Duke University. He is currently a PhD student in Health and Biomedical Data Science, in the Applied Biostatistics track, and he also works at the George Washington University Biostatistics Center as a biostatistician. His current research interests include clinical trials, high-dimensional data, and data science.

Shiyu Shu Email:  [email protected] Start year: 2021

Shiyu (Richard) was born and raised in Dalian, China, and has been studying in the United States for the last 7 years. He obtained a BA in Mathematics and in Economics from Vassar College, during which he spent one semester as an exchange student at St Edmund Hall, Oxford University. He then received a Master of Statistical Practice from Carnegie Mellon University, and worked as a data analyst for a healthcare organization in rural Arizona during the peak of the COVID pandemic. The work experience motivated him to pursue a career in public health, and to continue his PhD studies in the Health and Biomedical Data Science program at GWU. He is currently a biostatistician working in the Diabetes Prevention Program team (DPP) at the Biostatistics Center, under the supervision of Dr. Marinella Temprosa. His current research interests include machine learning/data science, genomics data and survival analysis.

Shanshan Zhang Email:  [email protected] Start year: 2021

Shanshan was born and raised in China. She earned a Bachelor of Medicine and a Master of Science in Cell Biology from China Medical University. When she came to the United States in 2018, she transferred her interests to public health, since a doctor can save individuals, whereas a public health expert can save lives on a population level. She obtained a second graduate degree, an MS in Biostatistics from the George Washington University. Shanshan hopes that she can make contributions to the field of public health, especially in designing and conducting clinical trials during the PhD program, and can work as an outstanding biostatistician in the future.

Recent Publications:

Qiongfang Wu, Leizhen Xia, Lifeng Tian, Shanshan Zhang, Jialyu Huang. Hormonal replacement treatment for frozen-thawed embryo transfer with or without GnRH agonist pretreatment: a retrospective cohort study stratified by times of embryo implantation failures. Accepted by Frontiers in Endocrinology. 5 January 2022

Shanshan Zhang. Biostatistics in Clinical Decision Making: What can We Get from a 2× 2 Contingency Table. E3S Web of Conferences (Vol. 233). EDP Sciences. December 2020

Qiqiang Guo, Shanshan Wang, Shanshan Zhang, Hongde Xu, Xiaoman Li, et al. ATM‐CHK 2‐Beclin 1 Axis Promotes Autophagy to Maintain ROS Homeostasis Under Oxidative Stress. The EMBO Journal, 39(10), e103111. 18 March 2020

PhD in Bioinformatics Students

Mahdi Baghbanzadeh Email:  [email protected] ;  [email protected] Start year: 2021

Mahdi Baghbanzadeh is a Ph.D. student in the health and biomedical data science program at the Milken Institute School of Public Health at the George Washington University. Mahdi received his MS in Mathematical Statistics from Shiraz University in 2012, and his BS in Statistics from Shahid Beheshti University in 2010.  Before joining GWU, he had the experience of 7 years performing in an analytical role ranging from data analyst to senior data scientist in multiple companies. His research interests are applying machine learning algorithms in analyzing omics data, developing tools for studying the genotype-phenotype association studies, and the effects of different medications on a certain disease.

Publications:

Baghbanzadeh, Mostafa; Simeone, F. C.; Bowers, C. M.; Liao, K.-C.; Thuo, M. M.;  Baghbanzadeh, Mahdi ; Miller, M.; Carmichael, T. B.; Whitesides, G. M.*  “Odd-even effects in charge transport across n-alkanethiolate-based SAMs”   Journal of American Chemical Society ,  2014 ,  136 , 16919–16925.

Mahdi Baghbanzadeh , Dewesh Kumar, Sare I. Yavasoglu, Sydney Manning, Ahmad Ali Hanafi-Bojd, Hassan Ghasemzadeh, Ifthekar Sikder, Dilip Kumar, Nisha Murmu, Ubydul Haque*  “Malaria Epidemics in India: Role of Climatic Condition and Control Measures”   Science of the Total Environment ,  2020 ,  712 , 136368.

Peeri, Noah C., Nistha Shrestha, Md Siddikur Rahman, Rafdzah Zaki, Zhengqi Tan, Saana Bibi,  Mahdi Baghbanzadeh , Nasrin Aghamohammadi, Wenyi Zhang, and Ubydul Haque.  " The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? "   International Journal of Epidemiology ,  2020 ,  49 , 717-726.

Md Siddikur Rahman, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Miftahuzzannat Amrin, Sumaira Zafar, Nadia Nahrin Rahman, Sharifa Umma Shirina, Ubydul Haque,  “Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media–based cross-sectional survey” ,  Transactions of The Royal Society of Tropical Medicine and Hygiene ,  2021 ,  115 , 85-93.

Nistha Shrestha, Muhammad Yousaf Shad, Osman Ulvi, Modasser Hossain Khan, Ajlina Karamehic-Muratovic, Uyen-Sa D.T. Nguyen,  Mahdi Baghbanzadeh , Robert Wardrup, Nasrin Aghamohammadi, Diana Cervantes, Kh. Md Nahiduzzaman, Rafdzah Ahmed Zaki, Ubydul Haque,  “ The impact of COVID-19 on globalization” ,  One Health ,  2020 , 100180.

Osman Ulvi, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Ateka Bashir, Jacob Smith, Ubydul Haque, “ Social Media Use and Mental Health: A Global Analysis ”,  Epidemiologia,   2022 , 3 (1), 11-25 .

Ranojoy Chatterjee Email:  [email protected] ;  [email protected] Start year: 2021

Ranojoy is originally from Kolkata, India. He got his B.Tech in Computer Science from WBUT and has an MS in Computer Science from Kansas State University, specializing in recommendation systems using a multi-armed bandit approach. After graduation he worked at Bellwethr, Inc developing a retention engine which was later patented by the company. After his brief stint in industry, he worked as a research specialist in Rahlab to develop machine learning tools for analyzing Covid-19 data. His current research interests are graph neural networks, single cell data and prediction systems in biomedical data science.  

Amritphale, A., Chatterjee, R., Chatterjee, S.  et al.  Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.  Adv Ther   38,  2954–2972 (2021).  https://doi.org/10.1007/s12325-021-01709-7

Chow JH, Rahnavard A, Gomberg-Maitland M, Chatterjee R, et al. Association of Early Aspirin Use With In-Hospital Mortality in Patients With Moderate COVID-19.  JAMA Netw Open.  2022;5(3):e223890. doi:10.1001/jamanetworkopen.2022.3890

Clark Gaylord Email:  [email protected] Start year: 2021

After receiving M.S. degrees in Mathematics and Statistics from the University of Virginia and Virginia Tech, respectively, Clark has had a career in information technology, network security, and research computing. Over the last 20 years, Clark has led the design and operation of many research computing and big data research systems, and is a consulting statistician on several research projects. While at Virginia Tech, Clark taught several courses in Statistics, Data Science, and Networking. A PhD candidate in GW's Health and Biomedical Data Science, Bioinformatics Track, Clark is also Director of Research Technology Services in GW IT.

CAAREN:  https://www.caaren.org/clark-gaylord GW High Performance Computing:  https://www.hpc.arc.gwu.edu/

Erika Hubbard Email:  [email protected] Start year: 2021

Erika was born and raised in Fairfax County, Virginia (NOVA) and earned her BSc in Biomedical Engineering with minor concentrations in Applied Mathematics and Engineering Business from the University of Virginia. Upon graduation she went on to intern and work for AMPEL BioSolutions, LLC in Charlottesville, VA, researching autoimmune and inflammatory diseases, primarily systemic lupus erythematosus (SLE). As a dual member of the systems biology and bioinformatics teams at AMPEL she developed an interest in leveraging genomics data to gain insights into mechanisms of autoimmune disease pathogenesis. She continues to work with AMPEL to study lupus and translate findings into novel clinical tools to further precision medicine. 

Hubbard EL, Pisetsky DS, Lipsky PE. Anti-RNP antibodies are associated with the interferon gene signature but not decreased complement levels in SLE. Ann Rheum Dis [Epub ahead of print: 3 Feb 2022]. doi:  https://doi.org/10.1136/annrheumdis-2021-221662

Hubbard EL, Grammer AC, Lipsky PE. Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus. Curr Opin Rheumatol [Internet]. 2021 Nov 1;33(6):579-85. doi:  https://doi.org/10.1097/bor.0000000000000833   

Daamen AR, Bachali P, Owen KA, Kingsmore KM, Hubbard EL, Labonte AC, et al. Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway. Sci Rep [Internet]. 2021 Mar 29;11(1):7052. doi:  https://doi.org/10.1038/s41598-021-86002-x

Hubbard EL, Catalina MD, Heuer S, Bachali P, Geraci NS, et al. Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis. Sci Rep [Internet]. 2020 Oct 15;10(1):17361. doi:  https://doi.org/10.1038/s41598-020-74391-4

Xinyang Zhang Email:  [email protected] ;  [email protected] Start year: 2021

Xinyang was born and raised in Jiangsu, China. Before coming to George Washington University, she obtained her MS in Data Informatics at the University of Southern California, Los Angeles. For now, she started her Ph.D. journey in Health and biomedical data science (Applied Bioinformatics track) and works for the Computational Biology Institute (CBI) as a Research Assistant. Her research interest focuses on microbiome analysis, omics data for the COVID-19, and reference-grade pathogen sequences database construction.  

Health Sciences Informatics, PhD

School of medicine.

The Ph.D. in Health Sciences Informatics offers the opportunity to participate in ground-breaking research projects in clinical informatics and data science at one of the world’s finest biomedical research institutions. In keeping with the traditions of the Johns Hopkins University and the Johns Hopkins Hospital, the Ph.D. program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health informatics, health IT, and data science. Resources include a highly collaborative clinical faculty committed to research at the patient, provider, and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.    

Areas of research:

• Standard Terminologies
• Precision Medicine Analytics
• Population Health Analytics
• Clinical Decision Support
• Translational Bioinformatics
• Health Information Exchange (HIE)
• Multi-Center Real World Data
• Telemedicine

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program.

Admission Criteria

Applicants with the following types of degrees and qualifications will be considered:

• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies and evidence of scholarly accomplishment; or
• In exceptional circumstances, BA or BS, with relevant technical and quantitative competencies, with some combination of scholarly accomplishment and/or professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.)

Relevant fields include: medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical science, bioengineering and pharmaceutical sciences, and computer and information science. An undergraduate minor or major in information or computer science is highly desirable. Professional work experience in one of these fields is also highly desirable. 

The application is made available online through Johns Hopkins School of Medicine's website . Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15 of the following year. Applications will not be reviewed until they are complete and we have all supporting letters and documentation.

• Curriculum Vitae (including list of peer-reviewed publications and scientific presentations)
• Three Letters of Recommendation
• Statement of Purpose
• Official Transcripts from undergraduate and any graduate studies
• Certification of terminal degree
• You are also encouraged to submit a portfolio of published research, writing samples, and/or samples of website or system development

Please track submission of supporting documentation through the SLATE admissions portal.

If you have questions about your qualifications for this program, please contact [email protected]

Program Requirements

The PhD curriculum will be highly customized based on the student's background and needs. Specific courses and milestones will be developed in partnership with the student's advisor and the PhD Program Director.

The proposed curriculum is founded on four high-level principles:

• Achieving a balance between theory and research, and between breadth and depth of knowledge
• Creating a curriculum around student needs, background, and goals
• Teaching and research excellence
• Modeling professional behavior locally and nationally.

Individualized curriculum plans will be developed to build proficiencies in the following areas:

• Foundations of biomedical informatics: e.g., lifecycle of information systems, decision support
• Information and computer science: e.g., software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); ethnographic methods.
• Implementation sciences: methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods), health economics, evidence-based practice, safety, quality.
• Specific informatics domains: clinical informatics, public health informatics, analytics
• Practical experience: experience in informatics research, experience with health information technology.

Basic Requirements

• "Core" courses
• Student Seminar & Grand Rounds
• Selective and Elective courses
• Mentored Research (in Year 1)
• Qualifying Exam (in Year 2)
• Proposal Defense (in Year 2 or 3)
• Dissertation (Years 2-4)
• Final Dissertation Defense (Year 4)
• Research Ethics

Master of Science in Health Data Science

Leverage your skills in statistics, computer science & software engineering and begin your career in the booming field of health data science

The Master of Science (SM) in Health Data Science is designed to provide rigorous quantitative training and essential statistical and computing skills needed to manage and analyze health science data to address important questions in public health and biomedical sciences.

The 16-month program blends strong statistical and computational training to solve emerging problems in public health and the biomedical sciences. This training will enable students to manage and analyze massive, noisy data sets and learn how to interpret their findings. The program will provide training in three principal pillars of health data science: statistics, computing, and health sciences.

Students in the program will learn to:

• Wrangle and transform data to perform meaningful analyses
• Visualize and interpret data and effectively communicate results and findings
• Apply statistical methods to draw scientific conclusions from data
• Utilize statistical models and machine learning
• Apply methods for big data to reveal patterns, trends, and associations
• Employ high-performance scientific computing and software engineering
• Collaborate with a team on a semester-long, data driven research project

The SM in Health Data Science is designed to be a terminal professional degree, giving students essential skills for the job market. At the same time, it provides a strong foundation for students interested in obtaining a PhD in biostatistics or other quantitative or computational science with an emphasis in data science and its applications in health science.

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• Academic Programs

PhD in Health Sciences Informatics Program

The PhD is a fully funded campus based program only.

Directed by Hadi Kharrazi, MD, PhD, the program offers the opportunity to participate in ground breaking research projects in clinical informatics at one of the world’s finest medical schools. In keeping with the tradition of the Johns Hopkins University and the Johns Hopkins Hospital, the program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health IT. Division resources include a highly collaborative clinical faculty committed to research at the patient, provider and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.

Areas of research:

• Clinical Decision Support
• Global Health Informatics
• Health Information Exchange (HIE)
• Human Computer Interaction
• Multi-Center Real World Data
• Patient Quality & Safety
• Population Health Analytics
• Precision Medicine Analytics
• Standard Terminologies
• Telemedicine
• Translational Bioinformatics

Vivien Thomas Scholars Initiative

As diverse PhD students at Johns Hopkins, Vivien Thomas scholars will receive the academic and financial support needed to ensure their success, including up to six years of full tuition support, a stipend, health insurance and other benefits, along with significant mentorship, research, professional development and community-building opportunities.

Click here to read more.

Application Requirements for the PhD in Health Sciences Informatics

Applicants with the following degrees and qualifications will be considered:

• BA or BS, or
• BA or BS, and a minimum of five years professional experience in a relevant field, or
• MA, MLS, MD or other PhD, with no further requirements.

"Relevant fields" include medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical basic science, bioengineering and pharmaceutical sciences and computer and information science. An undergraduate minor or major in information or computer science is highly desirable.

The Application Process

Applications for the class entering in academic year 2025-2026 will be accepted starting in September 1, 2024 through December 15, 2024. (The application is made available through the Johns Hopkins School of Medicine here. )

Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15, 2024 . Applications will not be reviewed until they are complete and we have all supporting letters and documentation. 

• Curriculum vitae
• Three letters of recommendation
• Official transcript of school record
• Certification of terminal degree
• Statement of Purpose
• You may also submit a portfolio of published research, or samples of website or system development to support your application if you wish.

This program does not require the GRE.

Important Transcript Information

It is the policy of the School of Medicine Registrar that new students have a complete set of original transcripts on file prior to matriculation showing the degree awarded and date. An official transcript is one that is addressed to the Office of Graduate Student Affairs and sent directly from the granting institution to Johns Hopkins University School of Medicine, Office of Graduate Student Affairs, 1830 East Monument Street, Ste. 620, Baltimore, MD 21287. The transcript envelope must be sealed and stamped on arrival at the OGSA office. Transcripts addressed to the student can not be accepted even if they are sent to the OGSA address above.

Program Description

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program. The following are specific requirements:

• A student should plan and successfully complete a coherent program of study including the core curriculum, Oral Examination, and additional requirements of the Research Master’s program. In addition, doctoral candidates are expected to take at least two more advanced courses. In the first year, two or three research rotations are strongly encouraged. The Master’s requirements, as well as the Oral Examination, should be completed by the end of the second year in the program. Doctoral students routinely will not be receiving a Masters degree on their way to the PhD; particular exceptions will be decided on a case-by-case basis. Doctoral students are generally advanced to PhD candidacy after passing the Oral Examination. A student’s academic advisor has primary responsibility for the adequacy of the program, which is regularly reviewed by the Doctoral Study Committee (DSC) of the Health Sciences Informatics (HSI) program.
• The student must have a minimum of two consecutive semesters (four quarters) of full time enrollment and resident on campus as a graduate student
• To remain in the PhD program, each student must receive no less than an B in core courses, must attain a grade point average (GPA) as outlined above, and must pass a comprehensive exam covering introductory level graduate material in any curriculum category in which he or she fails to attain a GPA of 3.0. The student must fulfill these requirements and apply for admission to candidacy for the PhD by the end of six quarters of study (excluding summers). In addition, reasonable progress in the student’s research activities is expected of all doctoral candidates.
• During the third year of training, generally in the Winter Quarter, each doctoral student is required to present a pre-proposal seminar that describes evolving research plans and allows program faculty to assure that the student is making good progress toward the definition of a doctoral dissertation topic. By the end of nine quarters (excluding summers), each student must orally present a thesis proposal to a dissertation committee that generally includes at least one member of the Graduate Study Committee of the Health Sciences Informatics program. The committee determines whether the student’s general knowledge of the field, and the details of the planned thesis, are sufficient to justify proceeding with the dissertation.
• As part of the training for the PhD, each student is required to be a teaching assistant for two courses approved by the DHSI Executive Committee; one should be completed in the first two years of study.
• The most important requirement for the PhD degree is the dissertation. Prior to the oral dissertation proposal and defense, each student must secure the agreement of a member of the program faculty to act as dissertation advisor. The University Preliminary Oral Exam (UPO) committee must consist of five faculty members, two of whom to be from outside the program, with the chair of the UPO committee coming from outside the program. The Thesis Committee comprises the principal advisor, who must be an active member of the HSI program faculty, and other, approved non HSI faculty members. Thesis committees must meet formally at least annually. Upon completion of the thesis research, each student must then prepare a formal written thesis, based on guidelines provide by the Doctor of Philosophy Board of the University.
• No oral examination is required upon completion of the dissertation. The oral defense of the dissertation proposal satisfies the University oral examination requirement.
• The student is expected to demonstrate the ability to present scholarly material orally and present his or her research in a lecture at a formal seminar, lecture, or scientific conference.
• The dissertation must be accepted by a reading committee composed of the principal dissertation advisor, a member of the program faculty, and a third member chosen from anywhere within the University. All University guidelines for thesis preparation and final graduation must be met.
• The Executive Committee documents that all Divisional or committee requirements have been met.

Program Handbook

Details about our program's policies are provided in our handbook here .

Course Offerings

The proposed curriculum is founded on four high-level principles:

• Balance between theory and research, and between breadth and depth of knowledge: By providing a mix of research and practical experiences and a mix of curricular requirements.
• Student-oriented curriculum design: By creating the curriculum around student needs, background, and goals, and aiming at long-term competence using a combination of broadly-applicable methodological knowledge, and a strong emphasis on self-learning skills.
• Teaching and research excellence: By placing emphasis on student and teaching quality rather than quantity, by concentrating on targeted areas of biomedical informatics, and by close student guidance and supervision.
• Developing leadership: By modeling professional behavior locally and nationally.

The Health Sciences Informatics Doctoral Curriculum integrates knowledge and skills from:

• Foundations of biomedical informatics: Includes the lifecycle of information systems, decision support.
• Information and computer science: E.g. computer organization, computability, complexity, operating systems, networks, compilers and formal languages, data bases, software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: Includes research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability).
• Implementation sciences: Methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods.) Health economics, evidence-based practice, safety, quality.
• Specific informatics domains: Clinical informatics, public health informatics.
• Practical experience: Experience in informatics research, experience with health information technology.

To achieve in-depth learning of the above knowledge and skills we adopt a student-oriented curriculum design, whereby we identify “teaching or learning processes,” that is, structured activities geared towards learning (i.e., courses/projects/assignments, seminars, examinations, defenses, theses, teaching requirements, directed study, research, service, internships). These processes were selected, adapted, or created in order to meet a set of pre-specified learning objectives that were identified by the faculty as being important for graduates to master.

The requirements are:

• 35 quarter credits/17.5 semester credits Core Courses (9 courses + research seminar 8 quarters)
• 48 quarter credits/24 semester credits Electives (may include optional practicum/research)
• 6 quarter credits/3 semester credits ME 250.855 practicum/ research rotation
• 36 quarter credits/18 semester credits ME 250.854 Mentored Research
• 125 TOTAL quarter credits/62.5 semester credits

Students are required to be trained in HIPAA and IRB submission, and to take the Course of Research Ethics.

IRB Compliance Training:

https://www.hopkinsmedicine.org/institutional_review_board/training_req…

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Health Data Science

• Entry year 2024
• Duration Full time 3 - 4 years, Part time 4 - 7 years

The PhD in Health Data Science provides research training in developing applied informatic and analytic approaches to data within health-related subjects such as medicine and the biomedical, biotechnological, and bioengineering sciences.

You will join the programme with a supervisory panel composed of academics working in health data science more broadly. Throughout the programme, and particularly during your first year, you will be encouraged to engage in training opportunities at Lancaster and elsewhere to develop both your research skills and subject-specific knowledge and abilities. Throughout your studies, you will focus on novel scientific research, developing best practice in interpreting and communicating new scientific methods and findings.

Your department

• Lancaster Medical School Faculty of Health and Medicine
• Telephone +44 (0)1524 592032

Entry requirements

Academic requirements.

2:1 Hons degree (UK or equivalent) in a relevant subject.

We may also consider non-standard applicants, please contact us for information.

If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.

Additional Requirements

As part of your application you will also need to provide a viable research proposal. Guidance for writing a research proposal can be found on our writing a research proposal webpage.

English Language Requirements

We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.

We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 5.5 in each element of the test. We also consider other English language qualifications .

If your score is below our requirements, you may be eligible for one of our pre-sessional English language programmes .

Contact: Admissions Team +44 (0) 1524 592032 or email [email protected]

Fees and funding

The tuition fee for students with home fee status is set in line with the standard fee stipend provided by the UK Research Councils. The fee stipend for 2024/25 has not been set. For reference, the fee stipend for 2023/24 was full-time £4,712.

The international fee for new entrants in 2024/25 is full-time £26,490.

Depending on the nature of the research project, an additional programme cost may be charged. This additional fee will contribute towards the costs incurred on specific research projects. These costs could include purchasing specialist consumables, equipment access charges, fieldwork expenses and payments for transcription/translation services.  Normally any additional charge will not exceed a maximum of £9,720 but this could be increased in exceptional circumstances.

Applicants will be notified of any specific additional programme cost when the offer of a place is made.

General fees and funding information

There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.

Specific additional costs for studying at Lancaster are listed below.

College fees

Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small College Membership Fee  which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.

For students starting in 2024, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2025 have not yet been set.

Computer equipment and internet access

To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated  IT support helpdesk  is available in the event of any problems.

The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.

For most taught postgraduate applications there is a non-refundable application fee of £40. We cannot consider applications until this fee has been paid, as advised on our online secure payment system. There is no application fee for postgraduate research applications.

For some of our courses you will need to pay a deposit to accept your offer and secure your place. We will let you know in your offer letter if a deposit is required and you will be given a deadline date when this is due to be paid.

The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your  fee status .

If you are studying on a programme of more than one year’s duration, tuition fees are reviewed annually and are not fixed for the duration of your studies. Read more about  fees in subsequent years .

Scholarships and bursaries

You may be eligible for the following funding opportunities, depending on your fee status and course. You will be automatically considered for our main scholarships and bursaries when you apply, so there's nothing extra that you need to do.

Unfortunately no scholarships and bursaries match your selection, but there are more listed on scholarships and bursaries page.

If you're considering postgraduate research you should look at our funded PhD opportunities .

We also have other, more specialised scholarships and bursaries - such as those for students from specific countries.

Browse Lancaster University's scholarships and bursaries .

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Important Information

The information on this site relates primarily to 2024/2025 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.

The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.

More information on limits to the University’s liability can be found in our legal information .

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We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. View our Charter and other policies .

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Office of Academic Clinical Affairs

Institute for Health Informatics

• Faculty & Staff
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• Oral History Project
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• Join the IHI
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• Admissions Overview
• Tuition & Funding
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• PhD Overview
• Clinical Informatics Track
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• Clinical Translational Science Institute - Biomedical Informatics (CTSI-BMI) Group
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• Health Informatics Invited Speaker Series

February 15 Deadline - APPLY NOW

The Data Science and Informatics for Learning Health Systems track builds on the highly regarded data science program offered jointly by the School of Engineering, School of Public Health, and School of Statistics. It requires students to fulfill the requirements of the Masters in Data Science program and use their elective courses to gain exposure to health sciences and health care in the form of a suite of required foundational courses. The MS capstone project will address a research question related to health sciences or healthcare. Specialization to the health care field intensifies at the PhD level by offering additional courses focusing on advanced analytics and its applications to healthcare. The thesis research will naturally relate to health science or healthcare.

Students who pursue the Data Science and Informatics for Learning Health Systems track are expected to earn the University’s Data Science MS degree en route to completing the PhD. Credits earned in the University’s Data Science MS program may be used to fulfill required courses or elective credits in the Data Science Informatics PhD subject to approval by PhD advisor. Students who have an MS in Data Science from a comparable program may be exempt from this requirement in whole or in part, subject to program review and approval.

Gyorgy Simon, PhD Assistant Professor Department of Medicine, Core Faculty 612-626-8329 [email protected]

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Structured PhD Program in Health Data Sciences

The PhD Program in Health Data Sciences at the Charité is hosted in English and aimed at qualified young scientists interested in:

• deepening their methodological knowledge in the fields of biostatistics, epidemiology, public health, meta-research, population health science and medical informatics.
• further expanding their competence in research and teaching.

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• PhD in Health Data Sciences

PhD Program in Health Data Sciences

Upon successful completion of the program, students will be awarded the academic degree of "Doctor of Philosophy" (PhD).

We are no longer accepting applications for entrance into our October 2024 cohort.

The deadline to submit applications for entrance into the October 2024 HDS PhD cohort was February 29, 2024 (23:59 CET).

The application window for our October 2025 HDS PhD Cohort will open in the beginning of 2025. Please check back in the autumn of 2024 for further details. 

Center for Health Data Science

The Center for Health Data Science leverages data in combination with knowledge across disciplines and places, with the ultimate goal of addressing quality of life and other public health priorities. CHDS enhances interdisciplinary public health research, teaching and practice through leveraging and developing data science methods in conjunction with public health knowledge, frameworks and action as well as with other disciplines such as computer science, urban planning and sociology. CHDS values and promotes pluralistic knowledge discovery and action, such as through cross-border student and faculty exchanges based on long-term relationships, and by working directly with practitioners on the ground to help address community needs.

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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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Public Health Data Science

The MS in Biostatistics Public Health Data Science Track (MS/PHDS) is designed for students interested in careers as biostatisticians applying statistical methods in health-related research settings. The MS/PHDS Track provides core training in biostatistical theory, methods, and applications, but adds a distinct emphasis on modern approaches to statistical learning, reproducible and transparent code, and data management. It is an appropriate program for students who intend to conclude their studies with the MS degree as well as those who want to pursue a PhD in biostatistics

All MS/PHDS candidates begin their studies in the fall semester. The length of the MS/PHDS program varies with the background, training, and experience of the candidate, but the usual period needed to complete the 36 credit MS/PHDS degree is two years (four semesters). In addition to fulfilling their course work, all MS/PHDS students also complete a one-term practicum and capstone experience.

Competencies

Through a curriculum of 36 credit hours of course work, a practicum, and the capstone experience, the MS/PHDS track provides students with the skills necessary for a career as a public health data scientist and a rigorous grounding in traditional biostatistics.

In addition to achieving the MS in Biostatistics core competencies, students in the PHDS Track gain the following specific competencies in the areas of public health and collaborative research, the foundations of applied data science, teaching biostatistics and biostatistical research. Upon satisfactory completion of the MS/PHDS, graduates will be able to:

Public Health and Collaborative Research

• Formulate and prepare a written statistical plan for analysis of public health research data that clearly reflects the research hypotheses of the proposal in a manner that resonates with both co-investigators and peer reviewers;
• Prepare written summaries of quantitative analyses for journal publication, presentations at scientific meetings, grant applications, and review by regulatory agencies;

Foundations of Applied Data Science

• Develop expertise in one or more statistical software and database management packages (often R and SQL, among others) routinely used by data science professionals;
• Implement a reproducible workflow for data analysis projects, including robust project organization, transparent data management, and reproducible analysis results;
• Develop and execute analysis strategies that use traditional statistical tools or modern approaches to statistical learning, depending on the nature of the scientific questions of interest;
• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning;

Teaching Biostatistics

• Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, statistical learning, and data analytic techniques to public health students enrolled in introductory level graduate public health courses; and

Biostatistical Research

• Apply probabilistic, statistical, and data scientific reasoning to structure thinking and solve a wide range of problems in public health.

Course Requirements

MS/PHDS graduates are expected to master the mathematical and biostatistical concepts and techniques presented in the curriculum’s required courses. Each student's program is designed on an individual basis in consultation with a faculty advisor taking into consideration the student's prior educational experience.

Students who have mastered an academic area through previous training may have the corresponding course requirement waived. Some students, such as those with undergraduate majors in statistics or mathematics, may apply to have several courses waived. Students wishing to waive one or more courses must request approval in writing from their advisors and the Director of Academic Programs. These students must still complete a minimum of 36 points to earn the MS/PHDS degree.

Required Courses

Below is the required course work. Students consult their faculty advisors before registering for classes to plan their programs based on their individual background, goals, and the appropriate sequencing of courses. Waiver of any required courses (with prior written approval of their faculty advisor and the Director of Academic Programs) enables students to take other, higher level classes.

*Students who have strong math background and/or have taken basic machine learning methods, can substitute the P8106 Data Science II with P9120 Topics in Statistical Learning and Data Mining I. 

Students choose four or more courses from the list below or from alternatives approved by their academic advisors.

Sample Timeline

Below is a sample timeline for MS/PHDS candidates. Note that course schedules change from year to year, so that class days/times in future years will differ from the sample schedule below; you must check the current course schedule for each year on the course directory page .

Practicum Requirement

One term of practical experience is required of all students, providing educational opportunities that are different from and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the practicum experience. Students will be required to make a poster presentation at the department’s Annual Practicum Poster Symposium which is held in early May.

Capstone Experience

A formal, culminating experience for the MS degree is required for graduation. The capstone consulting seminar is designed to enable students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/collaborator, which will comprise the major portion of their future professional practice.  

As part of the seminar, students are required to attend several sessions of the Biostatistics Consulting Service (BCS). The Consultation Service offers advice on data analysis and appropriate methods of data presentation for publications, and provides design recommendations for public health and clinical research, including preparation of grant proposals. Biostatistics faculty and research staff members conduct all consultation sessions with students observing, modeling, and participating in the consultations.

In the capstone seminar, students present their experience and the statistical issues that emerged in their consultations, developing statistical report writing and presentation skills essential to their professional practice in biomedical and public health research projects.

Paul McCullough Director of Academic Programs Department of Biostatistics Columbia University [email protected]

Imperial College London Imperial College London

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• Postgraduate taught

Health Data Analytics and Machine Learning

Develop skills in using cutting-edge quantitative methods to fully exploit complex data.

Develop skills in using cutting-edge quantitative methods to fully exploit complex health data.

Further your understanding of the statistical and machine learning models used to analyse and integrate complex and high-dimensional blocks of health data.

Apply your knowledge using real data sets on an extended and real-world research projects.

Course key facts

Qualification, september 2024, £14,900 home, £43,250 overseas, delivered by, school of public health, hammersmith, minimum entry standard, 2:1 in mathematics, statistics, epidemiology or biology, or a medical degree, course overview.

Broaden your expertise in analysing health data on this Master's course. 

You will gain expertise in developing, applying and interpreting results from cutting-edge statistical and machine learning approaches for analysing and integrating complex sets of data that are emerging in the health field.

Delivered by international experts in the field sharing real-world expertise and experiences,, this course will equip you with both a sound theoretical and practical understanding of the needs and utility of health data analytic tools.

You will  address real and yet unresolved scientific questions through a variety of individual and group projects and you will produce work that adheres to international publication quality standards.

The programme has a strong technical component, and will provide you with the complementary training essential for a career in health data sciences.

You will be integrated and will contribute to the fast-emerging multidisciplinary and multicultural health data analytics community within Imperial and beyond.

This page is updated regularly to reflect the latest version of the curriculum. However, this information is subject to change.

Find out more about potential course changes .

Please note:  it may not always be possible to take specific combinations of modules due to timetabling conflicts. For confirmation, please check with the relevant department.

Core modules

• Research project

You’ll take all of these core modules.

Introduction to Statistical Thinking and Data Analysis

Understand the importance of statistical thinking in epidemiology, randomised trials and public health and learn how to critically evaluate the results of standard statistical analyses.

Principles and Methods of Epidemiology

Become familiar with core concepts of epidemiology and acquire the skills necessary to describe, analyse, interpret and appraise epidemiological studies.

Molecular Epidemiology

Engage with the emerging field of molecular epidemiology and analyse the recent advances in biotechnology that have helped revolutionise epidemiologic studies.

Translational Data Science Part 1 and 2

Use cutting edge methods in real life to analyse large data sets and publish a piece of research. You’ll then further your expertise in the methodology used in translational data science.

Clinical Data Management

Explore the complexity and specifics of clinical/medical data and learn how to build and query state-of-the-art databases.

Machine Learning

Uncover the principles of machine learning, their key methodological concepts, and the main tools their implementation relies on.

Computational Epidemiology

Advance your understanding of computational epidemiology and learn about novel approaches for interpretable data analysis and integration.

Bayesian Modelling for Spatial and Spatio-temporal Data

Build your understanding of the concepts of Bayesian modelling and inference, and the statistical methods used in analysing spatial and spatio-temporal data.

You’ll carry out an extensive individual research project during the third term (4 months full-time research.

During this project, you’ll apply advanced techniques you have been taught to real data sets.

You’ll be supervised through the process of conceiving, delivering and assessing a high standard scientific publication, with your work assessed by a paper-style report and oral examination.

Teaching and assessment

Balance of teaching and learning.

• Lectures, practical and seminars
• Independent study

Terms 1 and 2

• 20% Lectures, practical and seminars
• 80% Independent study
• 0% Research project
• 0% Lectures, practical and seminars
• 0% Independent study
• 100% Research project

Teaching and learning methods

Balance of assessment.

• Written examinations
• 20% Coursework
• 35% Practical
• 45% Written examinations

Assessment methods

Entry requirements.

We consider all applicants on an individual basis, welcoming students from all over the world.

• Minimum academic requirement
• English language requirement
• International qualifications

2:1  in mathematics, statistics, epidemiology or biology, or a medical degree.

All candidates must demonstrate a minimum level of English language proficiency for admission to Imperial.

For admission to this course, you must achieve the  higher university requirement  in the appropriate English language qualification. For details of the minimum grades required to achieve this requirement, please see the  English language requirements .

We also accept a wide variety of international qualifications.

The academic requirement above is for applicants who hold or who are working towards a UK qualification.

For guidance see our accepted qualifications  though please note that the standards listed are the  minimum for entry to Imperial , and  not specifically this Department .

If you have any questions about admissions and the standard required for the qualification you hold or are currently studying then please contact the relevant admissions team .

How to apply

Apply online.

You can submit one application form per year of entry. You can choose up to two courses.

Application fee

There is no application fee for MRes courses, Postgraduate Certificates, Postgraduate Diplomas, or courses such as PhDs and EngDs.

If you are applying for a taught Master’s course, you will need to pay an application fee before submitting your application.

The fee applies per application and not per course.

• £80 for all taught Master's applications, excluding those to the Imperial College Business School.
• £100 for all MSc applications to the Imperial College Business School.
• £150 for all MBA applications to the Imperial College Business School.

If you are facing financial hardship and are unable to pay the application fee, we encourage you to apply for our application fee waiver.

Read full details about the application fee and waiver

Application process

Find out more about  how to apply for a Master's course , including references and personal statements.

ATAS certificate

An ATAS certificate  is not  required for students applying for this course.

Tuition fees

Overseas fee, inflationary increases.

You should expect and budget for your fees to increase each year.

Your fee is based on the year you enter the university, not your year of study. This means that if you repeat a year or resume your studies after an interruption, your fees will only increase by the amount linked to inflation.

Find out more about our  tuition fees payment terms , including how inflationary increases are applied to your tuition fees in subsequent years of study.

Which fee you pay

Whether you pay the Home or Overseas fee depends on your fee status. This is assessed based on UK Government legislation and includes things like where you live and your nationality or residency status. Find out  how we assess your fee status .

Postgraduate Master's Loan

If you're a UK national, or EU national with settled or pre-settled status under the EU Settlement Scheme, you may be able to apply for a  Postgraduate Master’s Loan  from the UK government, if you meet certain criteria.

The government has not yet published the loan amount for students starting courses in Autumn 2024. As a guide, the maximum value of the loan was £12,167 for courses starting on or after 1 August 2023. 

The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.

Scholarships

Sph masters scholarship, who it's for.

• All students who make an application to the School of Public Health.

The Dean’s Master’s Scholarships

Value per award.

• £10,000
• All students applying to study a Faculty of Medicine Master’s programme beginning in October 2024

How will studying at Imperial help my career?

Analyse high throughput medical and epidemiological data in depth using a strong methodological framework.

Our graduates often pursue further study in master's programs or doctoral research.

You’ll be well-positioned to become an expert analyst in industry or join large data companies.

Aside from medicine, you'll also be highly sought after in alternative fields.

Common career paths include education, public administration, R&D, and technical consultancy.

Further links

Contact the department.

• Email:  [email protected]

Course Directors: Professor Marc Chadeau-Hyam  and  Professor Paul Elliot Course Organiser:  Dr Sabrina Andrade Rodrigues

Visit the  School of Public Health  website.

Register your interest

Stay up to date on news, events, scholarship opportunities and information related to this course.

Events, tasters and talks

Meet us and find out more about studying at Imperial.

Find an event

Terms and conditions

There are some important pieces of information you should be aware of when applying to Imperial. These include key information about your tuition fees, funding, visas, accommodation and more.

Read our terms and conditions

You can find further information about your course, including degree classifications, regulations, progression and awards in the programme specification for your course.

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Certificate in Health Data Analytics

Tackle New Challenges in the Health Care Industry

Designed for working professionals, the Certificate in Health Data Analytics program at the University of Michigan-Flint provides a flexible learning experience, empowering you to advance your career while improving the healthcare system.  Our 13-credit graduate certificate program offers an interdisciplinary approach, supporting you as you grow your expertise in the computer science and health domains. With a convenient hybrid format and a completion time within three semesters , you can work while earning your certificate and benefit from applying what you learn in the classroom in your day-to-day role.

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On This Page

• Program Highlights
• Curriculum 
• Career Outlook
• Application Process

Why Earn Your Health Data Analytics Certificate at UM-Flint?

Learn in a flexible hybrid program format.

To maximize flexibility, you can complete this certificate in just three semesters through a mix of online or traditional in-person classes. We offer computer science courses via distance learning in a cyber classroom. Additionally, we automatically record face-to-face classes, allowing you to access them online at any time, so you have the option to attend in-person or online. Whatever your scheduling needs may be, you can pursue a graduate education that cultivates career growth at UM-Flint.

Promotes In-Demand Skill Development

Through our integrative approach, you develop advanced skills in data analysis, data reporting, performance improvement, and related health care measurements you can use to inform organizational decisions. With these transferable skills, you establish yourself as a competent professional and valuable contributor to any healthcare facility.

Customize Your Education and Earn Your Master’s Degree

Students who earn their Certificate in Health Data Analytics and wish to continue working to obtain their master’s degree can apply their credits accrued during their certificate toward their Master of Science in Health Services Administration , which allows you to stack two or three certificates of your choice. This added customization level helps you get closer to achieving your academic and professional goals. 

Health Data Analytics Graduate Certificate Curriculum

Our Certificate in Health Data Analytics curriculum merges cutting-edge computer science courses with the medical field to ensure you gain a firm understanding of how you can harness the power of data to support patient care and the overall efficiency of health care facilities. 

You enroll in advanced courses examining data mining, health information management for administrators, and biostatistics, which deepen your knowledge of data analysis, data warehousing and its lifecycle, technological trends in the medical field, and more.

Additionally, study informatics or health informatics to learn about information systems, the latest patient care technology, and determining factors in integrating information technology. 

Students choose from two program options:

• Option 1: For students with approved backgrounds in computer programming
• Option 2: For all other students

Review the Health Data Analytics certificate curriculum and courses .

Academic Advising

Need guidance as you work toward your Health Data Analytics Certificate? UM-Flint’s expert academic advisors are ready to help! For more information, email Dr. Reza Amini at [email protected] to discuss your class selection, degree plan development, and more.

Career Outlook for Health Data Analytics Graduates

As the health care industry increasingly relies on significant data gains, employers seek out health care professionals with in-depth knowledge of data sources and types and the systems that drive them. Those who match the industry’s pace and strengthen their technical knowledge and skills become competitive, qualified candidates. 

Health professionals with technical training are skilled in collecting and analyzing patient and health information. They can turn health care data into meaningful insights to improve patients’ health, which includes creating more effective diagnoses and treatments and transforming the speed and efficiency of processes within agencies and organizations. The Bureau of Labor Statistics estimates that the demand for data scientists will grow 35 percent each year, creating 168,900 career openings. Furthermore, data analysts have the potential to earn a stable income. On average, their annual salary ranges between $74,000 to $113,000 , depending on their place of employment, level of education, and experience.

Admission Requirements (GRE Not Required)

For entrance into the health data analytics certificate program, you must meet the following criteria:

• A BS in a health-related field -or-
• A BS or BBA in a business-related field -or-
• A BS or BA in another field with at least two years of health sector work experience
• Minimum overall GPA of 3.0 on a 4.0 scale

*Students in the final semester of a UM-Flint health care baccalaureate program may receive an override to begin the graduate certificate while completing the baccalaureate degree by filling out this form . Submit the form to [email protected] .

State Authorization for Online Students

In recent years, the federal government has emphasized the need for universities and colleges to comply with the distance education laws of each state. If you are an out-of-state student intending to enroll in an online program, please visit the State Authorization page to verify the status of UM-Flint with your state.

Applying to the Certificate in Health Data Analytics Program

At UM-Flint, we make the application process straightforward. To apply for our Certificate in Health Data Analytics program, please submit the following materials:

• Application for Graduate Admission
• $55 application fee (non-refundable)
• Official transcripts from all colleges and universities attended. Please read our full  transcript policy  for more information.
• For any degree completed at a non-US institution, transcripts must be submitted for an internal credential review.  Read the following  for instructions on how to submit your transcripts for review.
• If English is not your native language, and if you are not from an exempt country , you must demonstrate English proficiency .
• Statement of Purpose: Describe your objectives for graduate study and reasons for selecting this program. You may submit statements online during the application process or email them to [email protected]
• Résumé or curriculum vitae
• A minimum of two letters of recommendation . Recommendations should be from individuals familiar with your work in academic or professional contexts who can comment on your critical thinking skills, ability to undertake independent projects, and capacity for collaborating with colleagues. We send electronic recommendation requests as a part of the online application process.
• Students from abroad must submit additional documentation .

Email additional application materials to [email protected] or deliver them to the Office of Graduate Programs .

This program is a certificate program. Admitted students cannot obtain a student (F-1) visa to pursue this degree. Other nonimmigrant visa holders currently in the United States please contact the Center for Global Engagement at [email protected] .

Application Deadlines

Prospective students interested in the Health Data Analytics program must submit all application materials to the Office of Graduate Programs by 5 p.m. on the application deadline. This program offers rolling admission with monthly application reviews. To be considered for admission, please submit all application materials on or before:

• Fall Early Deadline: May 1
• Fall Final Deadline: August 1 
• Winter: December 1

*Applicants must have a complete application by the early deadline to guarantee eligibility for scholarships, grants, and research assistantships .

Estimated Tuition and Cost

UM-Flint strives to make graduate education accessible and affordable. That’s why we ensure students receive competitive tuition rates and helpful financial aid resources to cover the costs of their tuition and other expenses.

Learn more about UM-Flint’s tuition and financial aid .

Be a Part of Innovating the Health Care System

Increase your influence as a health care provider or an administrator by earning your Certificate in Health Data Analytics from the University of Michigan-Flint. Through our rigorous program, you gain valuable technical and quantitative skills, empowering you to lead with a data-driven strategy and craft innovative solutions. 

Are you excited to take the next step? Request information , or start your UM-Flint application today !

Big Data Analytics (PhD)

Program at a glance.

• In State Tuition
• Out of State Tuition

Learn more about the cost to attend UCF.

Big Data Analytics will train researchers with a statistics background to analyze massive, structured or unstructured data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.

The program will provide a strong foundation in the major methodologies associated with Big Data Analytics such as predictive analytics, data mining, text analytics and statistical analysis with an interdisciplinary component that combines the strength of statistics and computer science. It will focus on statistical computing, statistical data mining and their application to business, social, and health problems complemented with ongoing industrial collaborations. The scope of this program is specialized to prepare data scientists and data analysts who will work with very large data sets using both conventional and newly developed statistical methods.

The Ph.D. in Big Data Analytics requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 42 credit hours of courses, 15 credit hours of restricted elective coursework, and 15 credit hours of dissertation research.

Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree

Program Tracks/Options

• Statistics Track

Application Deadlines

• International

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Program Prerequisites

Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra , COP 3503C - Computer Science II. These pre-required courses are basic undergraduate courses from the Math and Computer Science departments. Students without background in COP 3503C can still apply for admission but they will need to take that course sometime after admission in the PhD program. COP 3503C serves as pre-requisite for COP 5711, which is required for the qualifying exam.

Degree Requirements

• All Ph.D. students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.

Required Courses

• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA5703 - Data Mining Methodology I (3)
• STA6106 - Statistical Computing I (3)
• STA6236 - Regression Analysis (3)
• STA6238 - Logistic Regression (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6329 - Statistical Applications of Matrix Algebra (3)
• STA6704 - Data Mining Methodology II (3)
• STA7722 - Statistical Learning Theory (3)
• STA7734 - Statistical Asymptotic Theory in Big Data (3)
• STA6714 - Data Preparation (3)
• CNT5805 - Network Science (3)
• COP5711 - Parallel and Distributed Database Systems (3)

Restricted Electives (at least 9 credit hours must be STA coursework)

• Other courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.
• STA6107 - Statistical Computing II (3)
• STA6226 - Sampling Theory and Applications (3)
• STA6237 - Nonlinear Regression (3)
• STA6246 - Linear Models (3)
• STA6346 - Advanced Statistical Inference I (3)
• STA6347 - Advanced Statistical Inference II (3)
• STA6507 - Nonparametric Statistics (3)
• STA6662 - Statistical Methods for Industrial Practice (3)
• STA6705 - Data Mining Methodology III (3)
• STA6707 - Multivariate Statistical Methods (3)
• STA6709 - Spatial Statistics (3)
• STA6857 - Applied Time Series Analysis (3)
• STA7239 - Dimension Reduction in Regression (3)
• STA7719 - Survival Analysis (3)
• STA7935 - Current Topics in Big Data Analytics (3)
• CAP5610 - Machine Learning (3)
• CAP6307 - Text Mining I (3)
• CAP6315 - Social Media and Network Analysis (3)
• CAP6318 - Computational Analysis of Social Complexity (3)
• CAP6737 - Interactive Data Visualization (3)
• COP5537 - Network Optimization (3)
• COP6526 - Parallel and Cloud Computation (3)
• COP6616 - Multicore Programming (3)
• COT6417 - Algorithms on Strings and Sequences (3)
• COT6505 - Computational Methods/Analysis I (3)
• ECM6308 - Current Topics in Parallel Processing (3)
• EEL5825 - Machine Learning and Pattern Recognition (3)
• EEL6760 - Data Intensive Computing (3)
• FIL6146 - Screenplay Refinement (3)
• ESI6247 - Experimental Design and Taguchi Methods (3)
• ESI6358 - Decision Analysis (3)
• ESI6418 - Linear Programming and Extensions (3)
• ESI6609 - Industrial Engineering Analytics for Healthcare (3)
• ESI6891 - IEMS Research Methods (3)
• STA5825 - Stochastic Processes and Applied Probability Theory (3)
• STA7348 - Bayesian Modeling and Computation (3)
• COP6731 - Advanced Database Systems (3)

Dissertation

• Earn at least 15 credits from the following types of courses: STA 7980 - Dissertation Research The student must select a dissertation adviser by the end of the first year. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours. The dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.

Examinations

• After passing candidacy, students will enroll into dissertation hours (STA7980) with their dissertation advisor. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor.

Qualifying Examination

• The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) once a year. The courses required to prepare for the examination are STA 5703, STA 6704, CNT 5805, STA 6326, STA 6327 and COP 5711. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their third year and are expected to have completed the exam by the start of their fourth year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program. It is strongly recommended that the student select a dissertation adviser by the completion of 18 credit hours of course work, and it is strongly recommended that the student works with the dissertation adviser to form a dissertation committee within two semesters of passing the Qualifying Examination.

Candidacy Examination

• The candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program

Admission to Candidacy

• The following are required to be admitted to candidacy and enroll in dissertation hours. Completion of all coursework, except for dissertation hours Successful completion of the qualifying examination Successful completion of the candidacy examination including a written proposal and oral defense The dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars Submittal of an approved program of study

Masters Along the Way

• PhD Students can obtain their Master's degree in Statistics & Data Science - Data Science Track along the way to their PhD degree. To satisfy the requirements for the MS degree, the student must complete the requirement for the MS degree. The student has the option of choosing between thesis option or non-thesis option.

Independent Learning

• As will all graduate programs, independent learning is an important component of the Big Data Analytics doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.

Grand Total Credits: 72

Application requirements, financial information.

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

Master of Science in Healthcare Data Analytics

Leverage Data to Drive Healthcare Improvement

Data analytics and data science plays a valuable role in improving healthcare and reducing costs by enabling organizations to leverage data to tackle complex problems and make more informed decisions. Data analytics can be used to identify trends and patterns in patient care, improve patient outcomes, reduce costs, and optimize resource allocation.

Harness the potential of data for informed decision-making in healthcare, positioning yourself as a leader in the evolving landscape. You will learn the fundamentals of data analytics including python, data visualization and the technical aspects of understanding data, in addition to creating models to tell a useful story - driving outcomes and change in healthcare settings.

100% Online

Earn your degree in a flexible 12-24 months

Access to MGH Datasets

Utilize real-word data in your class exercises and projects.

Core Faculty

affiliated with Mass General Brigham

• How to Apply
• Interprofessional experience
• Costs & Funding

Our degree will prepare you to play a valuable role in data science and analytics in the healthcare sector with a strong foundation in the following areas:

Data analytics fundamentals : Learn about data analysis tools and techniques, such as python programming, data visualization, and statistical analysis, as well as understanding how to clean, organize, and manipulate data sets.

Technical aspects of understanding data : This may include learning about different types of data, such as structured and unstructured data, and how to identify and handle missing or incomplete data.

Creating models to tell a useful story: Learn about different types of models, such as regression, classification, and clustering, and how to select and apply the appropriate model for a given problem. And, be able to communicate the results of your analysis effectively to different stakeholders.

Driving outcomes and change in healthcare settings : This involves learning about the challenges and opportunities presented by data analytics in the healthcare sector, as well as understanding how to apply data analytics to address specific problems and improve patient care. Learn about the ethical and legal considerations related to the use of healthcare data.

Overview of the Application Process

MGH Institute’s Master of Science in Healthcare Data Analytics program welcomes applications with a baccalaureate degree who seek to become leaders in data analytics.

Application Deadline

Now accepting applications for programs starting: Fall 2024  and Spring 2025

We accept applications on a rolling admissions basis. If you have any questions, please email us at Admissions [at] mghihp.edu (Admissions[at]mghihp[dot]edu) .

Application and Fee All applicants are required to submit a completed online application. There is no application fee for this program.

Prerequisites

All applicants must have completed a Bachelor’s degree from a regionally accredited U.S. college or university. Applicants that have earned a degree from a non-US institution are required to submit a course-by-course credential evaluation, see “Transcript” section. 

Prerequisite Course

There are no prerequisite courses required to apply for this program.  

Applicants are encouraged to have taken a Statistics course and/or have had experience, or familiarity, with computer programming languages, such as R. Your familiarity could have been gained in formal coursework, or through work experience/independent study, but is not required to apply to the program. Please contact the Data Analytics program if you have any questions related to this.

Learn more about our online prereqs or select an MGH Institute course below to view its description.

• Introductory Statistics (3 credits) (Please be advised this course uses a different statistical software package than the one used/taught in the program.)

TOEFL/ IELTS The language of instruction and clinical education at the MGH Institute is English and a high level of proficiency in both written and spoken English is required.  Applicants who have not completed either an undergraduate or graduate program where English is the language of instruction must demonstrate English Language proficiency as part of your application to the MGH Institute of Health Professions.  If you have questions about the language requirements, please contact the Office of Admissions.

• Applicants who are citizens of Australia, Canada (except Quebec), Great Britain, Ireland, South Africa, New Zealand, Guyana, an Anglophone country of Africa, or an English-speaking country of the Caribbean are not required to submit TOEFL or IELTS scores.
• Applicants who are candidates for graduation from an accredited degree-granting program in the United States or at an English-speaking school in one of the countries listed above are also not required to submit TOEFL or IELTS scores. Acceptance to the IHP will be contingent upon successful completion of this degree prior to matriculation.

Please note that in some circumstances, demonstrating English language proficiency may be required by the academic program even if you are a citizen of a country in which the (or one of the) national language(s) is English. Decisions about the need for TOEFL or IELTS scores are at the discretion of the academic program to which you are applying in coordination with the department of OES.

The IHP accepts either the TOEFL (Test of English as a Foreign Language) or the IELTS (International English Language Testing System). The test must have been taken within two years of the application deadline and official score reports are required. The minimum TOEFL (internet-based) score accepted is 89 and the minimum IELTS score accepted is 6.5.  

• To forward your TOEFL score please contact the Educational Testing Service (ETS) .  The MGH Institute of Health Professions code is 3513.
• For IELTS, a Test Report Form may be mailed to MGH Institute of Health Professions and score information will be verified by the IHP directly. You may designate up to 5 schools to receive Test Report Forms at the time you register for the test.  To request additional Test Report Forms, contact your test center.

Please contact the Office of Admissions if you have any questions about the MGH Institute’s English Language requirements. 

Applicants are required to submit a transcript from each college and/or university attended, even if a degree was not received from that institution. Unofficial transcripts will be accepted throughout the application process, and official transcripts will be required of all accepted and enrolled students, prior to matriculation. For transcripts to be considered official they must be in their original signed and sealed envelopes when received.

Unofficial transcripts uploaded after application submission must include, 1) The name of the institution and 2) list the student’s name, and 3) contain a transcript legend. If an unofficial transcript is received without this information, it will not be accepted. Grade reports and copies of diplomas, or screenshots of a document will not be accepted.  

For official transcripts, the Office of Admissions strongly encourages the use of online electronic transcript ordering which can be sent admissions [at] mghihp.edu (directly via email) to admissions. If this is not an option and your institution does not participate in electronic transcript delivery, please request official transcripts to be sent to the mailing address listed below:

Admission Office MGH Institute of Health Professions 36 First Avenue Boston, MA 02129

Foreign Transcripts: Applicants that have earned a degree from a non-US institution are required to submit a course-by-course credential evaluation from one of the following NACES (National Association of Credential Evaluation Services) members: Educational Credential Evaluators, Inc., SpanTran: The Evaluation Company, World Education Services (WES), or the Center for Educational Documentation. If you earned your bachelor's degree outside of the U.S. this credential evaluation must document minimum equivalency of a US baccalaureate degree or higher.

Statement of Intent

All applicants are required to compose an essay that addresses the following: 

• The reason you have applied to this program & MGH Institute of Health Professions
• How your past experiences (academic, personal, and/or extracurricular) have influenced your decision to apply
• Your specific area of interest and any applicable knowledge of statistics and/or programming skills
• Personal characteristics that will contribute to your success
• How completion of this program will assist you in reaching your professional goals.  

Essays should be 12 pt. font, double spaced, and no more than three pages in total. The statement of intent should be uploaded directly to your online application. 

(Optional) Diversity Statement  

All applicants will have the option to submit diversity statement:

​MGH Institute of Health Professions is committed to an inclusive campus climate that welcomes students who will enrich the diversity of thought and perspective, and therefore, enhance the learning experiences for all.

Essays should be typed, double-spaced, and no more than three pages in total. The (optional) diversity statement of intent should be uploaded directly to your online application. Please Answer the following below: 

​In what ways might you personally contribute to improving the experience of the campus as a welcoming and inclusive place to learn?

Recommendation Letters

Applicants are required to provide two recommendation letters. All recommendations are processed through our online application. Please provide contact information for each recommender within your online application.

Recommendation letters should come from individuals who are able to address your academic ability, character and integrity, as well as your potential for graduate professional study. Furthermore, at least one letter should come from an academic reference and one should come from a professional reference.

Resume or CV

Applicants are required to submit a current resume or CV.

Who do I contact for more information about the academic program, curriculum, or course requirements? Please data-analytics [at] mghihp.edu (contact the Program) .

Who do I contact for more information about the application process, my application status, or what documents to submit? You are welcome to email the admission office , or call (617) 726-1304 weekdays between 9 a.m.-5 p.m. Eastern Time.

What is a good way to learn more about the MGH Institute? Attend an admissions event for additional information.

Can I receive Financial Aid for this program? Do not wait until you've been accepted to the program to learn about financial aid options available. Contact our Financial Aid office as soon as you have applied to ensure that you will be able to take advantage of all options available. When you apply for financial aid through the Free Application for Federal Student Aid (FAFSA), you'll need the MGH Institute Federal School Code: G22316.

Since this is not considered a full-time graduate program, you may not be eligible for most traditional financial aid programs. However, you may be eligible for employer tuition reimbursement if this is a graduate program of study in your profession.

What is your mailing address? MGH Institute of Health Professions Office of Enrollment Services 36 1st Ave. Charlestown Navy Yard Boston, MA  02129

Are there other Conditions of Admission? Yes. If applicable, final transcripts and test scores must be submitted to satisfy the conditions of admission .

I’m an international student. Can I receive an F-1 Visa? Please see General Information for Prospective International Students .

The curriculum has been developed in collaboration with senior leaders from within Mass General Brigham and other local healthcare leaders.  

Learn data analytics in an experiential way. All technical courses are taught with a hands-on approach - you will apply tools and methods as you learn them. In addition, in the two-semester Application of Analytics course, you will solve a real-world problem that a client has (a hospital, a clinic, or a healthcare provider) using data and methods learned in the program. 

View Curriculum

Data analysis and visualization: learn programming languages such as Python, as well as how to effectively visualize and communicate data.

Data management and storage: learn about databases and data management systems, as well as understanding how to store and organize large amounts of data.

Healthcare informatics: learn about the use of technology and data in healthcare, including electronic health records (EHRs), clinical decision support systems, and other health information systems.

Statistical analysis: learn about statistical methods and techniques for analyzing and interpreting data, such as regression analysis, hypothesis testing, and machine learning.  

Clinical outcomes: Outcomes related to the effectiveness of healthcare treatments and interventions. Examples may include measures of patient survival, disease management, and symptom improvement.

Patient satisfaction: The degree to which patients are satisfied with their healthcare experience, including factors such as the quality of care, convenience, and communication with healthcare providers.

Cost and efficiency: Measures of the cost of healthcare services and the efficiency with which they are delivered. For example, data analytics may be used to identify opportunities for cost savings or to optimize the use of resources.

Population health: Refers to the overall health status of a population, including factors such as morbidity, mortality, and risk factors for disease. Data analytics can be used to identify trends and patterns in population health and to develop interventions to improve population health outcomes.

In healthcare data analytics, enterprise information systems (EIS) play a crucial role in the collection, storage, and analysis of data. By using EIS, healthcare organizations can more efficiently and effectively manage and analyze large amounts of data from a variety of sources, including patient medical records, claims data, and population health data.

For example:

Clinical decision support: EIS can be used to provide real-time clinical decision support to healthcare providers, helping them to make informed decisions about patient care.

Population health management: EIS can be used to track and analyze population health data, helping healthcare organizations to identify trends and patterns and develop interventions to improve population health outcomes.

Quality improvement: EIS can be used to track and measure key quality indicators, such as patient satisfaction, clinical outcomes, and cost efficiency. This can help healthcare organizations identify areas for improvement and implement changes to drive better outcomes.

Data integration: EIS can be used to integrate data from a variety of sources, allowing healthcare organizations to get a more complete and accurate picture of patient health and care.

Digital stewardship and governance in healthcare data analytics refers to the principles and practices that guide the responsible and ethical use of healthcare data for analytics and decision-making. It involves the development and implementation of policies, procedures, and systems to ensure healthcare data is collected, stored, used, and shared in a manner that protects the privacy and security of patients, and that complies with relevant laws and regulations, such as HIPAA in the US.

It also involves the development and implementation of best practices and standards for the management of healthcare data, such as the use of standardized terminology (e.g., SNOMED) and the adoption of data governance frameworks, such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Finally, it involves the development and implementation of ethical guidelines and principles to ensure that the use of healthcare data for analytics and decision-making is fair, transparent, and accountable, and does not result in discrimination or harm to patients or other stakeholders. This may involve the use of explainable artificial intelligence (XAI) and other methods to provide clear and interpretable explanations for the decisions and actions of AI systems.

This concentration focuses on the skills, knowledge, and attitudes necessary for effective and responsible leadership and practice in the field of healthcare data analytics. This includes the ability to effectively manage and lead teams, communicate effectively with stakeholders, use data-driven decision-making and problem-solving skills, and uphold professional ethical standards.

Some specific skills and knowledge include:

• Understanding the role of healthcare data analytics in healthcare delivery and decision-making
• Familiarity with relevant laws, regulations, and professional standards, such as HIPAA in the US
• Knowledge of data management and governance best practices, such as the FAIR principles
• Understanding of statistical and computational methods and tools used in healthcare data analytics
• Ability to effectively communicate technical and complex information to a non-technical audience
• Strong problem-solving and critical thinking skills

In addition to technical skills and knowledge, leadership and professionalism in healthcare data analytics also involves the development and maintenance of professional ethical standards, including a commitment to transparency, accountability, fairness, and non-discrimination in the use of healthcare data for analytics and decision-making.

A distinctive feature of the program is interprofessional experience, allowing students to collaborate and practice what they learn as they would in the real world - working with varying disciplines and personalities. You will enroll in courses with students from Post-Professional Doctor of Occupational Therapy , Doctor of Speech-Language Pathology , Master of Health Administration , and Health Professions Education . Shared course topics include leading interprofessional teams, diversity equity and inclusion, data analytics, and organization systems leadership. 

The output of software, data, is taking over the way that healthcare operates. A fundamental understanding of how metrics are created, improved, and maintained is a vital function for any healthcare professional working today. 

Combined Data Analytics and Pre-Health Certificate

Designed for aspiring health professionals who want to apply to graduate school and differentiate themselves with technology and data. Receive academic preparation in addition to a linkage transitioning you seamlessly into graduate school. This graduate certificate will appear on your academic transcript, helping future graduate schools know that you’ve completed rigorous prerequisite coursework.

If you'd like to then work toward your masters at the IHP, pre-health certificate students who earn a 3.0 GPA or better in the certificate program are eligible for contingent admission to the IHP Data Analytics masters degree program. 

she8 [at] mgb.org (Email the Program Director) to get started with your individualized certificate plan.

Tuition & Fees

Financial Aid

Tuition Reduction for MGB Employees, Alumni and Affiliates  

Employees across the MGB system can receive a reduction in their tuition of up to 40%.

This is a fully online program with some on-campus learning experiences. Online programs offer the convenience and flexibility of being able to complete coursework from anywhere, as long as you have a stable internet connection. This can be especially useful for working professionals who may not have the time or ability to attend classes in person.

However, it is worth noting that the program includes occasional on-campus learning experiences. These may be required in-person sessions or events, such as workshops or seminars, that take place on campus. It is important to carefully review the program requirements and schedule to ensure you are able to attend these on-campus learning experiences, as they may be a key part of the program.

It is also worth considering the potential benefits of in-person learning experiences, as they can provide an opportunity for face-to-face interaction with faculty and classmates, as well as hands-on learning opportunities that may not be possible in an online setting.

Data analytics in healthcare is a rapidly expanding field that is relevant to both clinical and non-clinical healthcare professionals, as well as to individuals looking to make a career change into the healthcare sector.

Clinical healthcare professionals, such as doctors, nurses, and other healthcare providers, can benefit from learning about data analytics by gaining a better understanding of how data can be used to improve patient care and outcomes. For example, data analytics can be used to identify trends in patient care, identify areas for improvement, and optimize resource allocation.

Non-clinical healthcare professionals, such as administrators, managers, and other support staff, can also benefit from learning about data analytics by gaining a better understanding of how data can be used to improve operational efficiency, reduce costs, and make more informed decisions.

Career changers who are interested in the healthcare sector may also find data analytics to be a valuable area of study, as the demand for skilled professionals in this field is expected to continue to grow in the coming years.

Earning a master’s degree can have an impact on one’s salary. The 2018 Burtch Works study, for example, found that those with a master’s degree in data analytics earn a median base salary of $92,500. Of course, salaries can vary based on job responsibilities, employer, location, etc. 

The MS in Healthcare Data Analytics program consists of 36 credit hours. Students can complete the program on a full- or part-time basis, usually earning a degree within 2 years. 

Upcoming Admissions Events

Upcoming events, speaker series.

Faculty Working in the Field

Benefit from a faculty with strong affiliations to Mass General Brigham, ensuring your education is rooted in real-world expertise and relevance.

Nicole Danaher-Garcia, PhD

Assistant Professor Health Professions Education Healthcare Data Analytics

• [email protected]

Osman G. Tanrikulu, PhD

Senior Data Analyst Institutional Research & Effectiveness Term Lecturer, Healthcare Data Analytics

• [email protected]

Pedram Safari, PhD

Term Lecturer Healthcare Data Analytics

Shuhan He, MD

Program Director Adjunct Assistant Professor Healthcare Data Analytics

• [email protected]

Data Analytics Highlights

Unlock Your Potential

Work-Life Balance and Professional Growth Juggling work and student life? We've got your back. At MGH IHP, we support working professionals like you. Our programs help you excel academically while managing your career. With flexible scheduling options, you can pursue your degree without compromising your professional success. Gain practical work experience through internships, co-op placements, and collaborative projects, building a strong network while learning. 

Realize your personal & professional development goals.

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

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

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

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

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

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

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

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

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

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

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

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

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MIT Sloan Health Systems Initiative

Data Revolution in Healthcare: New Frontiers in Interoperability and Data Exchange With FHIR

In April 2024, HSI presented the final two seminars this academic year that are part of the seminar series, "Sparking the Data Revolution in Healthcare via FHIR”, that HSI cohosted with the  Martin Trust Center for MIT Entrepreneurship . Earlier seminars explored the origin and development of FHIR, which you can read about  here  and  here . There are a number of FHIR accelerators designed to create open-source standards for data sharing, with the goal of national health data interoperability. On April 11,  Dr. Su Chen  spoke about her work as the Program Manager and Clinical Director of one of these accelerators,  CodeX , which focuses on clinical specialties, such as oncology. Chen’s work is on the ground level of creating open-source standards for data sharing using the FHIR API. Specifically, for oncology CodeX developed  mCODE , which stands for minimal Common Oncology Data Elements. These elements, Chen said, are “standardized, computable, clinically applicable and available in electronic health records for cancer patients”. With mCODE, a patient searching for a relevant clinical trial has a 91% increase in potential matches located nearby compared to searching prior to mCODE implementation. mCODE and CodeX are also being used to support the  White House Cancer Moonshot Initiative , which aims to “end cancer as we know it” by underwriting a large cooperative effort among Federal agencies and outside companies. On April 18, 2024, HSI wrapped up the series with  Don Rucker , who has had a multi-decade career in healthcare. Rucker was the  National Coordinator for Health Information Technology (ONC)  and led the finalization and approval of the  21st Century Cures Act . In that position, he had primary responsibility to write and persuade stakeholders to support a regulation that would require electronic health records (EHRs) to have an API application programming interface based on FHIR standards, so a patient could download their data from the electronic health records into an app of their choice “without special effort”. Don explained that he met with every stakeholder (about 200 meetings) and gave about 150 national presentations so that by the time the rule came up, “it seemed inevitable”. EHRs have to meet the requirements of the rule and include the standardized core data set in order to be certified by the ONC.    Rucker explained that the first stakeholder and situation that FHIR considered was how patients could easily move their own data from providers’ IT to an app of their choosing because it had bipartisan support. The agreement made it easier to make these patient data flows a reality. Similarly, Rucker spoke about “prior authorization” as a lever that could be used to make advances in IT interoperability and data flow since it is an issue that upsets just about everyone. He said, “prior auth is the leverage point because in healthcare everyone is in agreement that it’s egregious.” Throughout the academic year, a number of speakers shared their experience with data interoperability. As a group, the seminars traced the story from the invention of FHIR to its implementation in selected situations. While the rate of change in healthcare can be glacial, FHIR is a very significant step toward the longtime goal of nationwide healthcare data interoperability.

With mCODE, a patient searching for a relevant clinical trial has a 91% increase in potential matches located nearby than searching prior to mCODE implementation.

Prior auth is the leverage point because in healthcare everyone is in agreement that it’s egregious.