phd in health data science

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:

• 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

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:

• BA or BS, with relevant technical and quantitative competencies and a record of scientific accomplishment as an undergraduate; 
• BA or BS, with relevant technical and quantitative competencies and a minimum of five years professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.); or
• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies

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.

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 & Credit Distribution

• 15 "core" quarter credits (5 courses)
• 8 quarter credits of Student Seminar & Grand Rounds
• 60 elective quarter credits
• 6 quarter credits practicum/research rotation
• 36 mentored research quarter credits (12 in year 1, 24 in year 2)
• Research Ethics

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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 2023 and 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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Biomedical Data Science Graduate Program Overview

The Biomedical Data Science Training Program is an interdisciplinary graduate and postdoctoral training program, part of the Department of Biomedical Data Science at Stanford University’s School of Medicine.

Our Mission

History of our graduate program, employment in biomedical data science, directions to dbds, contact information, our educational mission.

The mission of DBDS is to train the next generation of research leaders in Biomedical Data Science. Our students gain knowledge of the scholarly informatics literature and the application requirements of specific areas within biology and medicine. They learn to design and implement novel methods that are generalizable to a defined class of problems, focusing on the acquisition, representation, retrieval, and analysis of biomedical information. We also require training in understanding ethical, social, and legal issues and consequences of research. We seek to attract diverse candidates from all backgrounds and experiences.

What is Biomedical Data Science?

Biomedical Data Science is a broad term comprising multiple areas.

• Bioinformatics develops novel methods for problems in basic biology.
• Translational Bioinformatics moves developments in our understanding of disease from basic research to clinical care.
• Clinical Informatics develops methods and tools directly applied to patient care.
• Public Health Informatics works on challenging problems from health systems and populations.
• Imaging Informatics addresses intelligent management, interpretation, and annotation of biomedical images.

Take a look at our current courses. 

Our Graduate Degrees

The graduate training program offers the PhD degree, and three MS degrees (an academic research-oriented degree, a professional distance-learning masters for part-time students, and co-terminal for Stanford undergraduates). We also have post-doctoral fellows, and offer a distance learning certificate.

• Prerequisites . For a graduate degree, the University requires the applicant to have a bachelor’s degree. We do not require any particular major, but we do require that students have strong undergraduate preparation in computer science/software engineering, mathematics (especially calculus, probability and statistics, and linear algebra), and college-level biology. Applicants with limited backgrounds in these areas should fill the deficiencies prior to applying to our program.
• Curriculum . MS and PhD candidates take coursework in four areas: (1) core DBDS classes, (2) an individual plan with electives in computer science, statistics, mathematics, engineering, and allied informatics-related disciplines, (3) required coursework in social, legal, and ethical issues, (4) unrestricted electives. In addition, PhD candidates are required to choose electives in some area of biology or medicine. Degree candidates also learn important didactic skills by serving as teaching assistants in our core courses.
• Funding . We have been continuously funded by  a training grant from the National Library of Medicine since 1984, which provides fellowship support for students who are US citizens and permanent residents. International students bring outside funding or compete for Stanford Graduate Fellowships. Senior graduate students typically receive funding support through their research supervisor.

The History of Our Graduate Program

History at Stanford

The Biomedical Data Science Graduate Program has a long history both at Stanford and internationally, as the first program of its kind. The degree program was initiated in October 1982 as Medical Information Sciences (MIS) and continues to emphasize interdisciplinary education between medicine, computer science, and statistics, offering pre- and postdoctoral degrees and training. The DBDS Program has been supported by a training grant from the National Library of Medicine since 1984, which initially funded only postdoctoral trainees but was broadened to include predoctoral trainees in 1987. The NLM training grant has been renewed every five years since and has provided tuition and stipend support for hundreds of trainees.

Today, the Biomedical Data Science Graduate Program sits in the newly formed Department of Biomedical Data Science and emphasizes methods development and application across the entire spectrum of biology, medicine, and human health.

A Foundation in Medicine and Computer Science

The interaction between Computer Science and other disciplines has produced vibrant areas of research and education at Stanford since the late 1960s; computing activities in the School of Medicine were stimulated even earlier, principally by the Chair of Genetics, Nobel Laureate Joshua Lederberg. Professor Lederberg collaborated with Professor Carl Djerassi (Chemistry) and Professor Edward Feigenbaum (Computer Science) to create what is arguably the first research program that applied the nascent field of artificial intelligence to biomedical problems. Their U.S. Dendral system, which studied the expertise of mass spectroscopists who could interpret an organic compound’s mass spectrum to infer the chemical structure of that compound, is considered the first expert system.

Professor Lederberg’s second key effort was to attract NIH funding for a large medically focused shared computer for the medical school. This computer, known as ACME, was heavily used by Stanford medical researchers, educators, and students until 1973. It brought a computing culture into the environment, which in turn began to attract medical students who had an interest in the intersection of the two fields.  Later ACME gave way to the SUMEX-AIM Computer, also funded by NIH with Lederberg as PI. This resource was the first biomedically focused machine on the ARPANet, which evolved to become today’s Internet.  The SUMEX Computer was a key resource at Stanford for almost 20 years.

Working closely with Stanley Cohen (a Professor of Medicine who later succeeded Lederberg as Chair of Genetics) and Bruce Buchanan (a research scientist in computer science who was a member of the Dendral Project), Edward Shortliffe undertook a combined MD/PhD with the doctoral degree in a self-designed interdisciplinary program. Further discussion with faculty, students, and researchers emphasized the interest and need to formalize this kind of interdisciplinary education, directly leading to the formation of the MIS graduate program.

The Human Genome Project and a Turn at the Turn of the Century

The launch of the Human Genome Project in 1990 and its completion in 2003 seeded substantial interest and need for computing in the biological community. In 2000 Dr. Russ B. Altman succeeded Dr. Shortliffe as Director of the MIS Program and in recognition of a new mission beyond clinical informatics, to fundamental issues of biomedical knowledge, its representation and its application, the program was renamed Biomedical Data Science  Training Program  (DBDS). The term Biomedical Data Science   represents not only the continued development of medical information systems but also the use of sophisticated computation to study medicine at the molecular, cellular, organismal, and population levels.

Biomedical Data Science Today

On September 1, 2023, the Biomedical Informatics (BMI) training program finalized its last step in merging with the Department of Biomedical Data Science (DBDS) and formally changed its name to the Biomedical Data Science Training Program.

Our trainees admitted after September 1, 2023 will earn their Master’s and PhD degrees in Biomedical Data Science.

The mission of our department and the training program remain fully aligned to “advance precision health by leveraging large, complex, multi-scale real-world data through the development and implementation of novel analytical tools and methods.” Aligning the name of the degree program with department name was widely regarded as both logical and appropriate. More importantly, it reflects a shared vision in our research and education missions that serves to pull our integrated work in biomedical informatics, biostatistics and AI/ML under a unified interdisciplinary umbrella.

The DBDS Training Program at Stanford continues to evolve to meet the needs of biomedical computation and application. Under the guidance of the current Director since 2018 and Chair of the Department of Biomedical Data Science, Professor Sylvia Plevritis, and with support from NLM, the DBDS Program continues to innovate in the areas of Healthcare and Clinical Informatics, Translational Bioinformatics, and Clinical Research Informatics. In addition to historical research thrusts in biomedical knowledge representation and the genetic basis of disease, current research explores algorithms for real world biomedical data, multi-modal data and meta-analysis, medical image analysis, responsible clinical decision making, reproducibility, methods for efficient querying and access to big biomedical data, and more.

Prospective students with interest in career directions in Biomedical Data Science should review a list of our Alumni and their current jobs under the People Directory .

If you have a job posting that you would like to send to the DBDS students and post-docs, please email it to dbds-job-openings at lists.stanford.edu for distribution as we deem appropriate for our audience.

DBDS Current Students and Alumni

The  School of Medicine Career Center  offers resources for professional and leadership development, resources for the job hunt ranging from presentation skills, resume preparation, interview skills to job hunt strategy. There is a seminar series from both industry and academia, and a number of industry events: demos, job fairs, industry mixers.

The University’s  Career Development Center  supports undergraduate and graduate career development. They have  Career Fairs .

To add your name to the DBDS jobs email list, send your request to the DBDS student services team .

External Job Listings in Biomedical Data Science

AMIA Job Exchange BayBio’s Job Sites list BioCareer’s Job site Bioinformatics.org’s Jobs site BioinformaticsDirectory listings Genomeweb’s Job listings ISCB Jobs Database Nature’s Jobs list New Scientist Jobs NIH’s job listings Science Career’s Ziprecruiter

Postdoctoral Positions at Stanford

Please see the descriptions for various opportunities in Biomedical Data Science under Postdoctoral Training

Directions to DBDS Program Offices

The DBDS Program Offices are in the Stanford’s Medical School Office Building (MSOB). The street address is: 1265 Welch Road, Stanford, CA 94305.

MSOB is located on the corner of Campus Drive West and Welch Road, between Panama Street and Welch Road. MSOB is a three story white building with redwood window framing. The exact latitude/longitude is 37.431734, -122.179476. See the map, below.

There are two options for parking:

• The parking lot in front of our building, which has an entrance on Welch Road. This lot has a few parking spots with coin metered parking.
• The large parking lot across the street on Welch Road. Entrance to the lot is from Stock Farm Road or Oak Road, but you have to drive within the lot towards the corner of Welch Road and Campus Drive. Payment is through cash, coins, or credit card using an automated permit dispenser. Information:  https://transportation.stanford.edu/parking

For all questions about the program, email: 

[email protected]

Mailing Address: Office Location 

Department of Biomedical Data Science Graduate Training Program

Stanford University School of Medicine

1265 Welch Road, MSOB X-343

Stanford, CA 94305-5464

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Faculty of Interest

Professor of Biomedical Informatics & Data Science; Vice Chair for Education, Biomedical Informatics & Data Science; Professor, Biostatistics

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Associate Professor of Biostatistics, Associate Professor of Ecology and Evolutionary Biology, Associate Professor of Management, and Associate Professor of Statistics and Data Science; Co-director, Public Health Modeling Concentration

Department Chair and Professor of Biostatistics; Affiliated Faculty, Yale Institute for Global Health; Director, Biostatistics and Bioinformatics Shared Resource

Assistant Professor of Biostatistics (Health Informatics)

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Assistant Professor of Biostatistics; Co-Training Director, Health Informatics MS

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• Genetic Speciation
• Host-Pathogen Interactions
• Genetic Phenomena
• Mathematical Concepts
• Phenomena and Processes

Associate Professor of Biostatistics; Associate Professor, Biomedical Informatics & Data Science

Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science; Affiliated Faculty, Yale Institute for Global Health

• Single-Cell Analysis
• Wearable Electronic Devices

Centers and other resources

• Center for Biomedical Data Science
• Center of Excellence in Regulatory Science and Innovation (CERSI)
• Collaborative Center for Statistics in Science (C²S²) C²S² fosters collaborations involving statistical methods and technology in scientific research, for understanding disease etiologies and developing treatment and prevention strategies.
• Crawford Lab
• Hongyu Zhao Lab - Center for Statistical Genomics and Proteomics
• Yale Center for Analytical Sciences (YCAS) YCAS collaborative team provides expertise in the design, conduct, and analysis of health and health care studies, methodological development, and education and training.

Healthcare Data Science (EPSRC CDT)

• Entry requirements
• Funding and costs

College preference

• How to apply

About the course

The Healthcare Data Science (EPSRC Centre for Doctoral Training) is a four-year doctoral cohort-based training programme offering opportunities for doctoral study in computational statistics, machine learning and data engineering within the context of ethically-responsible health research.

This course is jointly run by a range of Oxford departments including the departments of Computer Science, Statistics, Engineering Science, the Nuffield Department of Medicine, and the Nuffield Department of Population Health.

Course structure

The course begins with a training year, which consists of two terms of intensive training in core data science principles and techniques followed by a third term where you will undertake two eight-week research projects in two of your chosen research areas. One of these projects will usually become the basis of your doctoral research, carried out in the following three years.

During the first year, your day will typically comprise of lectures each morning with practical computational exercises each afternoon.

The taught courses covering core subjects such as computational statistics, machine learning, data engineering, ethics and governance, and health research methodology include the following:

• Software Engineering
• Statistical Methods
• Research Methods
• Machine Learning
• Bayesian Statistics
• Medical Imaging
• Biomedical Image Analysis
• Biomedical Time Series Analysis
• Device and Sensor Data
• Infectious Diseases
• Modelling for Policy Making
• Data Governance
• Data Engineering
• Health Data Quality
• Health Data Standards
• Data-driven Innovation.

In each case, you will develop an understanding of relevant concepts and techniques that is not only enough to enable their application and integration but will also serve as a solid foundation should you choose to pursue research in that area.

Each term of taught modules concludes with an extended, team-based two-week data challenge where you will work in small groups with clinicians and domain experts to address questions using large healthcare datasets.

At the start of the second term you will select from a pool of projects. These projects are proposed by Oxford faculty members but you may also contact faculty members to jointly propose projects. There are always more projects than students, and students are typically matched to, at least, their first choice, but it is not possible to guarantee that you will be able to work with a particular member of staff. 

You will undertake two eight-week placements with research groups within the University. These will provide you with experience of working as part of an active group and the opportunity to explore specific areas before writing a proposal for your doctoral research.

At the end of the summer of the first year, you will normally select one of the two projects to become the basis of your DPhil research.

In years two to four you will carry out individual research on a project within the scope of the programme, specifically the development of novel statistical, machine learning or computational methods with application to health or healthcare data. Training will continue in academic reading, writing and presentation skills, ethics, responsible research and innovation, and career development and planning.

While working on your research project, you will have the opportunity to participate in a range of activities including an ethics placement, four-week external data challenge, seminar series and annual CDT retreats.

Supervision

The allocation of graduate supervision for this course is the responsibility of the Medical Sciences Doctoral Training Centre (MSDTC) and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may be found outside the department.

Teaching on taught modules and subsequent research supervision are provided by leading academics from a range of departments at the University. You will benefit from dual supervision for the duration of your research project; at least one of the members of the supervisory team will have a strong background in core data science.

You will be expected to meet your supervisors on a regular basis. These meetings should take place at least once every two weeks, averaged across the year and agreed by both parties, to discuss your progress.

All modules, data challenges and activities during the taught course component involve some aspect of formal assessment, including written reports, problem solving, and group and individual presentations. At the end of year one, you will submit a short DPhil proposal which will be examined orally by the programme directorate to evaluate your progress and the suitability of the project.

All students will be initially admitted to the status of Probationer Research Student (PRS). Within a maximum of six terms as a PRS student you will be expected to apply for transfer of status from Probationer Research Student to DPhil status. Students who are successful at transfer will also be expected to apply for and gain confirmation of DPhil status within ten terms of admission, to show that your work continues to be on track.

Both milestones normally involve an interview with two assessors (other than your supervisor) and therefore provide important experience for the final oral examination.

You will be expected to submit a original thesis after, at most, four years from the date of admission.

To be successfully awarded a DPhil in Healthcare Data Science you will need to defend your thesis orally (viva voce) in front of two appointed examiners. 

Graduate destinations

It is expected that graduates will be well placed to take on leading roles in industry, academia and the public sector, including areas where health and health care data is used to direct policy or make decisions about patient care.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a first-class or strong upper second-class undergraduate degree with honours  

The above qualification should be achieved in one of the following subject areas of disciplines:

• Mathematics
• Engineering Science
• Computer Science; or
• A related field with substantial mathematical background

A master's qualification in one of the above subjects is recommended, but not essential.

For applicants with a degree from the USA, usually the minimum GPA sought is 3.5 out of 4.0. 

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience

• Research or working experience in a relevant field may be an advantage.
• Whilst not required, or expected, publications demonstrating previous research experience in a relevant field and a track record demonstrating an interest in research are likely to advantage your application.

English language proficiency

This course requires proficiency in English at the University's  higher level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's higher level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides  further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are normally held as part of the admissions process and are expected to take place around a month after the application deadline.

Interviews are usually held remotely and are approximately 30 minutes in length. The interview takes the form of a series of questions to assess readiness to study, specifically your foundational mathematical, statistical and computational skills, and your interest in working at the interface between machine learning and health and healthcare data. 

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Initiatives to improve access to graduate study

This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly.

For this course, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process.  Further information about how we use your socio-economic data  can be found in our page about initiatives to improve access to graduate study.

This is also one of the courses participating in the  Academic Futures programme , including the  Black Academic Futures programme . Applicants who are offered a place on this course and meet the eligibility criteria will subsequently be considered for funding through the Academic Futures programme.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

Academic Technology Approval Scheme (ATAS)

Some postgraduate research students in science, engineering and technology subjects will need an Academic Technology Approval Scheme (ATAS) certificate prior to applying for a  Student visa (under the Student Route) . For some courses, the requirement to apply for an ATAS certificate may depend on your research area.

The Healthcare Data Science cohort-based training programme is based in the Oxford's  Big Data Institute  (BDI), a new purpose-built 7,500 square-metre research institute at the heart of the University's biomedical campus. The institute is an analytical hub for multi-disciplinary working at Oxford, connecting world-leading expertise in statistics, computer science, and engineering to data- driven research in medicine and population health.

The institute has dedicated teaching spaces for classes, workshops, group exercises, and presentations, as well as study space for students during their first year. The institute has many large and small meeting rooms, a large café, and an open, furnished atrium, affording space for formal and informal interaction with research groups, other programmes, and partner organisations. You will have access to a secure research computing infrastructure that supports containerised processing, and you will be able to push your own applications to cloud infrastructure provided by partner organisations. There is central support for common applications and services, including a JupyterHub server for Jupyter notebooks.

The institute houses internationally recognised research groups in genomic medicine, medical image analysis, mobile and sensor data, infectious diseases, and large-scale clinical trials. It is also home to the Ethox Centre  and the newly established Wellcome Centre for Ethics and Humanities .

The BDI hosts the clinical informatics and big data activity of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), a substantial programme (£114m) of translational research, delivered by the University in partnership with Oxford University Hospitals (OUH) NHS Foundation Trust (FT). This activity includes the development of a secure data warehousing and analytics infrastructure - a ‘research platform’ - to support the large-scale re-use of routinely- collected clinical data for research purposes.

The platform contains integrated, longitudinal records for two million patients, including data from patient administration, electronic prescribing, laboratory tests, imaging reports, pathology reports, discharge summaries and clinical letters. It also contains historical datasets, including a comprehensive collection of laboratory test data, on a larger patient population, from 1993 to date. Oxford University Hospitals have agreed to provide students with access to the platform, and to extracts of the data, for approved training and research purposes.

The BDI hosts the informatics activity of the UK Biobank, a major national and international resource for health research. The Biobank team are leading the development of tools for the acquisition, processing, analysis, and re-use of data from clinical and online assessments, imaging, sensors, genotyping, and national datasets (including hospital episodes, death, and primary care) for a cohort of 500,000 participants. CDT students will have the opportunity to access the expertise of the team, and to become involved in Biobank-based research.

Oxford is one of six substantive sites for Health Data Research (HDR) UK . The Oxford HDR UK team, based in the BDI, will lead research initiatives on 21st Century Clinical Trials and Enhancing Prospective Cohort Studies. This work will include the development of new methods and tools for phenotyping at scale, including machine learning approaches to the analysis of large, complex clinical datasets.

When you move out to your DPhil research department you will also have access to the facilities provided by that department. You will remain a member of the CDT and will retain access to the Big Data Institute.

Medical Sciences Doctoral Training Centre

The Medical Sciences Doctoral Training Centre (MSDTC) accommodates the interdisciplinary, cross-departmental DPhil programmes in medical sciences.

Most are structured DPhil programmes, which provide students with the opportunity to undertake two or three 'rotation' projects and relevant course work in their first year of each four-year structured programme. The main doctoral project starts in the second year of each programme. Most of our programmes receive external core-funding, and currently from the Wellcome Trust (WT), British Heart Foundation, Cancer Research UK and EPSRC.

The MSDTC also accommodates the NIH Oxford-Cambridge Scholars’ Programme, the DPhil in Cancer Science programme funded by CRUK which welcomes applications from clinicians, basic scientists, and medical undergraduates, and the new DPhil in Inflammatory and Musculoskeletal Disease which is funded by the Kennedy Trust for Rheumatology Research and is open to Oxford University medical students wishing to undertake DPhils in the fields of musculoskeletal disease, inflammation and immunology.

The department also offers an exciting new programme (the DPhil in Advanced Bioscience of Viral Products) run in collaboration with Oxford Biomedica, which aims to deliver the next generation of bioscience leaders to advance research on the underpinning bioscience of viral products for future gene therapies and vaccines.

Each programme has a distinctive intellectual flavour, designed to nurture independent and creative scientists. Students are supported in their development through:

• supervision and mentoring by world-class academics training in a wide range of research techniques
• development of student resilience and maintenance of mental health and wellbeing from the start and throughout each programme.

View all courses   View taught courses View research courses

We expect that the majority of applicants who are offered a place on this course will also be offered a fully-funded scholarship specific to this course, covering course fees for the duration of their course and a living stipend.

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Annual fees for entry in 2024-25

Further details about fee status eligibility can be found on the fee status webpage.

Information about course fees

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

The following colleges accept students on the Healthcare Data Science (EPSRC CDT):

• Brasenose College
• Exeter College
• Hertford College
• Jesus College
• Keble College
• Kellogg College
• Lady Margaret Hall
• Linacre College
• Mansfield College
• Reuben College
• St Anne's College
• St Cross College
• St Edmund Hall
• Wolfson College
• Worcester College

Before you apply

Our  guide to getting started  provides general advice on how to prepare for and start your application. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines and when to apply  in our Application Guide.

Application fee waivers

An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Application fee waivers for eligible associated courses

If you apply to this course and up to two eligible associated courses from our predefined list during the same cycle, you can request an application fee waiver so that you only need to pay one application fee.

The list of eligible associated courses may be updated as new courses are opened. Please check the list regularly, especially if you are applying to a course that has recently opened to accept applications.

Do I need to contact anyone before I apply?

You do not need to make contact with the department before you apply but you are encouraged to visit the relevant departmental webpages to read any further information about your chosen course.

You may wish to make informal enquiries with the HDS team before you apply in order to work out whether this is the right course for you, and the likely availability of funding. You should do so via the contact details provided on this page.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

Proposed field and title of research project

As you will not choose your research area until the end of year one, you do not need to specify a research field, or project title beyond "HDS cohort-based training programme" in your application. You should not use this field to type out a full research proposal. You will be able to upload your research supporting materials separately if they are required (as described below).

Proposed supervisor

As you will not choose your research supervisor until the end of year one, you do not need to specify a supervisor beyond "HDS cohort-based training programme" in your application.

Referees: Three overall, of which at least two must be academic 

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Academic references are preferred, although a maximum of one professional reference is acceptable where you have completed an industrial placement or worked in a full-time position.

Your references will support your intellectual ability, your academic achievement, your motivation and interest in the course and the subject area, and your ability to work both in a group and independently.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Statement of purpose/personal statement: A maximum of 500 words

You should provide a statement of your research interests, in English, describing how your background and research interests relate to the programme. If possible, please ensure that the word count is clearly displayed on the document.

The statement should focus on academic or research-related achievements and interests rather than personal achievements and interests.

This will be assessed for:

• your reasons for applying;
• evidence of motivation for and understanding of the proposed area of study;
• the ability to present a reasoned case in English;
• capacity for sustained and focused work; and
• understanding of problems in the area and ability to construct and defend an argument.

It will be normal for students’ ideas and goals to change in some ways as they undertake their studies, but your personal statement will enable you to demonstrate your current interests and aspirations.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice . You'll find the answers to most common queries in our FAQs.

Application Guide   Apply

ADMISSION STATUS

Closed to applications for entry in 2024-25

Register to be notified via email when the next application cycle opens (for entry in 2025-26)

12:00 midday UK time on:

Friday 1 March 2024 Applications may remain open after this deadline if places are still available - see below

A later deadline shown under 'Admission status' If places are still available,  applications may be accepted after 1 March . The 'Admissions status' (above) will provide notice of any later deadline.

*Three-year average (applications for entry in 2020-21 to 2022-23)

This course was previously known as Health Data Science

Further information and enquiries

This course is offered jointly by the Big Data Institute and the  Medical Sciences Doctoral Training Centre .

• Course page on the institute's website
• Academic and research staff
• Research in the institute
• Medical Sciences Graduate School
• Mathematical, Physical and Life Sciences
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected]

Application-process enquiries

See the application guide

Other courses to consider

You may also wish to consider applying to other courses that are similar or related to this course:

View related courses

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• Accessibility
• EPSRC Centre for Doctoral Training in Health Data Science

EPSRC Centre for Doctoral Training in Healthcare Data Science

Course structure

Research & ethics, student experiences, follow us @hdscdt.

Insights derived from the analysis of large, complex data sets will make significant contributions to the prevention and treatment of disease. The aim of this doctoral training  programme  in Healthcare Data Science is to offer systematic training in statistics, machine learning, and data management. Core to the entire  programme  is to combine this technical training with a foundation in ethics.  

Ethics plays a central role in health data science, and our approach to doctoral training reflects this. Ethics and research responsibility is a vertical theme running through the four years of the  programme . Each of the first two terms begins with a week of training in ethics, responsible research and innovation, and collaborative working. In the first term, this training addresses ethical issues in data science and big data in general. In the second, it focuses upon specific ethics and governance issues in health data science and healthcare delivery.  

This EPSRC Centre for Doctoral Training in Healthcare Data Science  is located in  the Big Data Institute/Oxford Population Health Building at the University of Oxford.

ENTRY REQUIREMENTS

A data science subject degree including Mathematics, Statistics, Engineering Science, Computer Science or a related field with substantial mathematical background. Applicants are recommended to have completed an MSc in one of the above subjects.  

How to apply

We're delighted that this programme has been renewed as the EPSRC CDT in Healthcare Data Science for another five cohorts starting from October 2024. Please see the admissions page on the University website for information about entry this autumn.

This course is taking part in a continuing pilot programme to improve the assessment procedure for graduate applications, to ensure that all candidates are evaluated fairly. For this course, the socio-economic data you provide in the application form will be used to contextualise the shortlisting and decision-making processes where it has been provided. Please carefully read the instructions concerning submission of your CV/résumé, statement of purpose, transcript and letters of support from referees in the  How to apply  section of this page as well as the  full details about this pilot .

It is important that you follow these new steps for your application to be considered.  Please use the standardised CV template provided and do not upload your own personalised version as these will not be reviewed by the Directorate.

Please ignore the section that states referees should anonymise their references, this applied to other courses on the pilot scheme but not ours.

We suggest considering Reuben College  or Kellogg College as the CDT has forged partnerships with these colleges. You are of course free to select any college on your application form but the CDT encourages you to consider one of these two listed colleges.

RESEARCH ENVIRONMENT

The Centre is hosted within the Big Data Institute (BDI)/Oxford Population Health (OxPop) Building, a purpose- built building  at the heart of the University of Oxford's biomedical campus. The Big Data Institute is an analytical hub for multi-disciplinary working at Oxford, connecting world-leading expertise in statistics, computer science, and engineering to data-driven research in clinical medicine and population health. It is also home to the  Ethox  Centre , a world-leading  centre  for clinical and research ethics, and the   Oxford Centre for Ethics and Humanities . 

The building hosts the clinical informatics and big data activity of the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC), a substantial  programme  of translational research, delivered by the University in partnership with Oxford University Hospitals (OUH) NHS Foundation Trust .  CDT students will have the opportunity to contribute to the work at the BRC, to access the expertise of the team, and to become involved in multi- centre  research collaborations.  

The BDI/OxPop Building is also home to UK Biobank , a major national and international resource for health research. The Biobank team are leading the development of tools for the acquisition, processing, analysis, and re-use of data from clinical and online assessments, imaging, sensors, genotyping, and national datasets (including hospital episodes, death, and primary care) for a cohort of 500,000 participants. CDT students will have the opportunity to access the expertise of the team, and to become involved in Biobank-based research.  

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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]

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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Health Data Science Concentration

Course information.

In addition to the existing core and elective courses in the Master of Science or PhD programs, the Health Data Science concentration features four core courses and five elective courses. Some of these courses are part of the current Master of Science program courses and some are new courses designed specifically for the Health Data Science concentration. One of the four core courses replace required courses for the traditional MS degree (BIOS 653: Applied Statistics III – Longitudinal Data Analysis).

Core and Elective Courses

Featured core courses in the Health Data Science concentration

Additional core courses can be found here.

Featured elective courses in the Health Data Science concentration

Course Selection Roadmap

Students' computing skills will be assessed for election of courses from the Health Data Science concentration and other degree core/elective courses. First-year MS students in Biostatistics can access information and advice from the department and faculty to plan their sequence of course selections.  Ongoing PhD students are able to complete this concentration. If they choose this route, some additional coursework is needed in order to meet the requirements of both the PhD and the HDS concentration.

HDS students must complete for their capstone courses (i) all four credits of BIOSTAT 699 and (ii) BIOSTAT 629 (1-2 credits). Biostat 629 will correspond to one or two comprehensive projects on mobile health, electronic health records, imaging data, omics data, etc.

Tables I and II below present two examples of course selections for a student with modest computing skills (e.g. having little knowledge of R programming) and for a student with strong computing skills (e.g. having extensive knowledge and experience in R, C++ and Python programming), respectively.

BIOS 607 is designed to prepare students with computing skills. In this way the Health Data Science concentration is more flexible and inclusive as a professional training program for workforce in health data analytics.

Table I. A possible sequence of course selections by an incoming MS student with modest computing skills, who begins with the three modules of BIOS 607.

Table II. A possible sequence of course selections by a first-year MS student with strong computing skills, who does not take BIOSTAT 607 but begins with BIOSTAT 625.

Note that there is one course (BIOS 653) not included in the curriculum of the Health Data Science concentration that is required by the PhD qualifying exams. Students interested in pursuing a PhD should take 653 in place of an elective the 2nd fall semester. Students already in the PhD program should take BIOS 653 for their qualifying exams.

Admissions Information

Students must be admitted to the Master of Science or PhD program in the University of Michigan School of Public Health's Department of Biostatistics. Once admitted, students will declare their intention to pursue the Health Data Science concentration at the end of their first year, by the end of May.

Have Questions?

For more information about the Health Data Science concentration, contact one of our graduate program coordinators.

Fatma Nedjari

Phone: 734-615-9812 Email: [email protected]

Nicole Fenech

Phone: 734-615-9817 Email: [email protected]  

Frequently Asked Questions

How/when do i apply for this program.

The Health Data Science concentration is not an option in the MS application, and thus there is no application procedure. Interested students should simply declare their intention to complete the Health Data Science concentration by May before their first (Fall) semester at Michigan Public Health by notifying a graduate program coordinator (Fatma Nedjari or Nicole Fenech). Students are encouraged to consult with their academic adviser about Health Data Science course selection.

Will I get in? Is there a cap? Am I automatically in? Are there more prerequisites?

There is no screening or selection procedure. This concentration program is open to all incoming Biostatistics MS students and operated as an automatic enrollment option. Interested students are encouraged to make a decision as soon as they arrive in their first Fall semester since the concentration courses are spread out over two years. As a specific track within the MS program, all Health Data Science courses require the same prerequisites as those in the core courses in the MS program.

When will I know if I get in the concentration program?

You may either notify a graduate program coordinator about your desire to pursue the Health Data Science subplan immediately after you decide to accept your admission offer to the Biostatistics MS program or in the beginning of your first Fall semester. At the stage of enrollment, simply follow the courses required by the Health Data Science concentration.

I have been admitted directly from a bachelor's degree program to the PhD program (or I definitely want to do the PhD program). Am I eligible for this Health Data Science concentration?

Yes, although masters' students interested in applying for the PhD program must be sure to include BIOS 653 (Theory and Application of Longitudinal Data Analysis) in their coursework.

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UCLA Graduate Programs

Graduate Program: Data Science in Health

UCLA's Graduate Program in Data Science in Health offers the following degree(s):

Master of Data Science in Health (M.D.S.H.)

With questions not answered here or on the program’s site (above), please contact the program directly.

Data Science in Health Graduate Program at UCLA Suite 51-254 CHS Box 177220 Los Angeles, CA 90095-1772

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Doctoral degree in Public Health or PhD in Health Data Sciences

In the area of Public Health, you have different options:

• PhD within the framework of the structured PhD program Health Data Sciences
• Individual doctorate (Dr. med, Dr. rer. medic.), supervised by IPH staff
• PhD (Dr. PH) in the doctoral program of the TU Berlin, department of Management in Healthcare

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Doctorates and PhD Programs

PhD Health Data Sciences The newly established PhD program Health Data Sciences is designed for qualified young scientists who would like to deepen their methodological knowledge in the fields of biostatistics, epidemiology, meta-research, population health science, or public health and further expand their competence in research and teaching. Information on the structured PhD program in Health Data Sciences (HDS) can be found on the website . 

Individual doctorate Students studying medicine at Charité - Universitätsmedizin Berlin or at another institution can apply for a doctorate at the IPH. Unfortunately, however, our staff capacities for supervising doctoral students at the IPH are limited. Publications in scientific journals are the declared goal of an individual doctorate at the IPH. If you are interested in an individual doctorate in the field of Public Health / Epidemiology, please send your request with a d etailed description of the study project and your curriculum vitae to Dr. Toivo Glatz .

Doctorate (Dr. Public Health) Please refer to the web pages of the Department of Management in Healthcare at the TU Berlin.

For general information on the various doctoral opportunities at the Charité, please visit the website of the Promotion Office .

Master of Data Science in Health

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PhD in Computing & Data Sciences

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

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

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

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

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

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

Learning Outcomes

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

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

Course Requirements

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

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

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

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

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

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

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

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

Language Requirement

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

Qualifying Examinations

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

Dissertation and Final Oral Examination

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

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

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

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PhD in Health Data Science

The PhD Program in Health Data Science at FMUP offers advanced education focused on a markedly multidisciplinary vision in three areas of specialization: Health Informatics, Intelligent Analysis of Health Data; Health Interventions, Policies, and Services.

The Program is aimed at students with a relevant academic or scientific curriculum, with academic education or professional experience in areas such as Medicine, Health Sciences and Technology, Computer Science, Computer Engineering, Mathematics, Statistics, Psychology, Economics, or Management, among others.

With 240 ECTS, the objectives of the cycle of studies include the definition, development, interpretation, and synthesis of health research results, the application of methods for statistical analysis, the integration of health data results into daily practice, and the transposition of the knowledge obtained into health decision making, considering ethical, legal and health data quality issues.

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Health Informatics (MS)

Health Informatics is a cutting-edge discipline and a natural fit to support all health professions. It is a developing discipline with an emphasis on technology as an integral tool to organize, analyze, manage, and use health information, clinical data, images, and knowledge for patient care, health education, administration and research. It is one of the fastest growing fields with a positive job outlook that provides an excellent opportunity to build a career.  

The Master of Science in Health Informatics is a STEM designated program offered by the Health Informatics Program in the School of Health Professions (SOHP). The program consists of a 39-credit curriculum (30-credit core and 9-credit electives) that emphasizes on clinical informatics, human computer interaction, electronic health records, evaluation of healthcare information system, clinical data management, health data analytics and visualization.  

The program is flexible and can be completed on a full-time or part-time basis. The complete MS program is offered in both traditional in-person and distance learning (online) format for student enrollment. In-person classes are offered in the evening, which works well for working professionals who work during the day. The completely online curriculum option is offered in asynchronous format with optional weekly live sessions (evening only) for students to interact with course instructors and other students which are recorded and made accessible to enrolled students.  

The program offers  the opportunity to thrive in a conducive learning environment with a tightly-knitted support system. Rigorous training opportunities are provided and offered to the students to enhance and develop the necessary skills needed to succeed in a health informatics career.  

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Every day, healthcare organizations collect a universe of data—and they need experts who know how to handle it. There's rising demand for specialists who aren't just technically adept but also understand how medical data is used.

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Informatics is a young field, but our faculty are seasoned veterans. Learn from experts who specialize in topics such as human-computer interaction, medical decision making, large-scale implementation of electronic medical records, information security, and using technology to reduce health care disparities.

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Put your training to work..

You'll complete a 120-hour internship in one of the city's busiest healthcare environments, such as Downstate and King's County hospital and the NYC Department of Health and Mental Hygiene.

Hands-on help.

When you pitch in at the student-run Brooklyn Free Clinic, which provides free health care services, you'll help the community—and sharpen your own skills.

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Meet working professionals and volunteer in the community through the Students of the Medical Informatics Association, a student group.

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Health Data Science: Frequently Asked Questions

Below, we provide answers to frequently asked questions about the Master of Science in Health Data Science at Saint Louis University. 

General Questions

No. The program is designed to be taken in sequence to be completed over four semesters. Due to the number of credits and requirements for the degree, finishing in two semesters is not possible.

The program is more career-oriented and is intended to be a terminal degree that prepares students for a career in data science or a related field. The skill set learned can also benefit a research-oriented career or future Ph.D.

No. Prior biomedicine, health or clinical sciences knowledge is not required for admission but is helpful as this degree focuses on health-related data. Our curriculum is focused on statistics and computing with health science applications. 

No. Most courses focus on statistical and computational methods commonly used to analyze clinical data, and the courses do not require prior knowledge of health or clinical sciences.

Similar to other programs in data science, our Health Data Science program provides a strong foundation in data collection, cleaning and preprocessing, statistical analysis, and scalable computational tools. What makes our Health Data Science program unique, is the program's strong emphasis on understanding the health care system and hands-on, applied experience analyzing and interpreting the complex data challenges specific to health care. Our students receive interdisciplinary training with a strong focus on the utilization of data science to improve health and health care.

Health data refers to any data that pertains to the biomedical sciences and public health. Data sets might originate from observational studies, clinical trials, computational biology, electronic medical records, health care claims, genetic and genomic epidemiology and environmental health, network health science and many other fields.

Yes. Students can register for courses within other SLU graduate programs. However, only graduate-level courses may be taken. Additional courses will not count toward credit for the degree requirements.

This project-based research course allows students to gain practical skills in analyzing and interpreting different types of big data in health care. Each student will work with an organization of their choice and a preceptor from the organization. In addition, a health data science faculty member and the capstone coordinator will oversee the capstone course requirements. 

Many resources are available to students, including networking opportunities, career services and research opportunities with faculty and other researchers.

Yes. Students can enroll as full-time or part-time students. 

Yes. You may start the program in the fall or spring semesters.

Our graduates are employed as data scientists, data managers, data analysts, machine learning engineers, statisticians, software engineers and quantitative analysts in academia, government and industry. It is also possible for students to further their education in a doctoral program in a related field.

Absolutely. While many of the examples and problems in the course curriculum will center around current topics in health care, the statistical and computational skill sets and tools obtained are broadly applicable to many areas of data science. The skill set learned in the program is transferable to various data science roles. We have had many graduates who have secured employment in non-health-related fields.

The cost per credit hour is $1,310. SLU's M.S. in Health Data Science requires 30 credit hours of coursework. For more information on tuition and costs, visit Student Financial Services:

View Financial Aid Information 

Admission Questions and Requirements

We require one letter of recommendation. This letter can be written by a current employer, former employer or a previous instructor.

No. Neither the GMAT nor the GRE is required.

Additional information about admission requirements can be found here: 

View Application Information

SLU's application portal does not require an application fee. If you are using a centralized application service like SOPHAS , ATCAS , CSDCAS , HAMPCAS , etc., you will be required to pay the application fee charged by these centralized services. 

You can check your application status by logging into your SLU application account. If you applied using a service such as SOPHAS, AMCAS or others, please return to your original application system.

Domestic Students

Log in to the mySLU portal and navigate to the payment suite in the "Tools" tab. From there, you should see a deposit option on the header. Select the term and the health data science deposit will be available for fall.  Students will not immediately see the deposit on their student account as it is held in a holding account until charges are added. The deposit is released to be applied to their tuition charges at that time.

International Students

• Please submit a deposit via SLUflywire.com
• The Flywire support staff can assist students directly. There is a chat icon at the bottom right of the link provided above that students can work with Flywire support to help answer questions directly.
• International Students must pay their deposit before the I-20 can be issued. 

Information for International Students

SLU’s Office of International Services can assist you with questions regarding your visa.

Email: [email protected]

Phone: +1 314-977-2318 Fax: +1 314-977-3412 Office Hours: 8:30 a.m.-5 p.m. C.S.T., Monday - Friday

OPT is one type of work permission available to certain F-1 nonimmigrant students. It allows students (except those in English language training programs) to obtain real-world work experience directly related to their fields of study.

The STEM OPT extension is a 24-month extension of OPT available to F-1 non-immigrant students who have completed 12 months of OPT and received a degree in an approved STEM field of study as designated by the STEM list.

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• Parsley Health, data intern, remote.

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• Hebert lab.
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Published April 9, 2024

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Opening up about mental health - webinar, dialogue across differences workshop, valley & ridge workshop, may 21-23, 2024, at dickinson, dickinson night at yankee stadium: yankees vs. mariners.

Postdoctoral Research Opportunity in Data Science and Particle Physics

The Cranmer lab at UW–Madison is seeking a postdoctoral research associate to pursue research at the intersection of data science and particle physics. The group is part of the ATLAS Collaboration at the Large Hadron Collider, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP), and the Data Science Institute at UW–Madison. The group has a long history of innovating tools and techniques that have been impactful to high-energy physics and beyond.

The successful applicant would devote at least half time to the IRIS-HEP effort by playing an active role in building an ecosystem of analysis tools (including scikit-hep, awkward, pyhf, cabinetry, MadMiner, RECAST, etc) and their integration into analysis facilities (with tools such as dask, coffea, Reana, etc.). It would be natural for the person that fills this role to join UW-Madison’s ATLAS group and devote a fraction of their time to physics analysis within the ATLAS experiment, but it is not a requirement. There is also an opportunity to devote time to development of novel machine learning techniques with application to science outside of particle physics.

The position could be based either at CERN or at the Data Science Institute in Madison.

UW-Madison’s physics department also has a vibrant effort on CMS, and the UW-Madison’s IRIS-HEP effort also includes a group based in the computer science department and the Center for High-Throughput Computing working on Data Organization, Management and Access (DOMA).

Learn more and apply