grad-biomedicaldata-ms

The current explosion of biomedical data provides an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. However, fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data management, data analysis, and machine learning. 

The M.S. degree program in biomedical data science covers core concepts and allows for concentrated coursework, in both methodology and application. The goal of the program is to prepare graduates to:

  1. Understand and apply key concepts and methodologies from computer science and statistics to biology and biomedicine.
  2. Demonstrate knowledge of biological, biomedical, clinical, and population health concepts and problems.
  3. Contribute to the solutions of the central computational problems in biology and medicine, using methods from computer science and statistics.

Potential students include both those with bachelor’s degrees in an area of data-science (e.g., computer science, statistics), as well as health professionals and clinicians (e.g., M.D.'s, Pharm.D.'s, R.N.'s). It is expected that admitted candidates will have demonstrated an aptitude for computer science and math, fundamental programming skills, knowledge of data structures and algorithms, and at least two semesters of college calculus. We will however consider candidates who have a wide range of undergraduate backgrounds; providing opportunities to develop necessary skills immediately upon entering the program.

Applying to the Program:

  • A formal online application with required fee through the UW–Madison Graduate School
  • Three letters of recommendation
  • Transcripts from each higher-education institution attended
  • A statement of purpose
  • GRE or MCAT scores
  • Applicants whose native language is not English, or whose undergraduate instruction was not in English, must provide an English proficiency test score (TOEFL, MELAB, or IELTS)
  • Evidence of quantitative preparation, including at least two semesters of college calculus (similar to MATH 221MATH 222) and either a course in linear algebra (similar to MATH 340) or courses in programming and data structures

Application Deadline:  January 12

For additional information about admission to the program, see MS Program in Biomedical Data Science on the department website.

Graduate School Admissions

Graduate admissions is a two-step process between academic degree programs and the Graduate School. Applicants must meet requirements of both the program(s) and the Graduate School. Once you have researched the graduate program(s) you are interested in, apply online.  

Graduate School Resources

Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid. Further funding information is available from the Graduate School. Be sure to check with your program for individual policies and processes related to funding.

Program Resources

Funding guarantees are not provided for students in this program. Students are encouraged to explore funding options available across campus.

Minimum Graduate School Requirements

Review the Graduate School minimum academic progress and degree requirements, in addition to the program requirements listed below.

Major Requirements

MODE OF INSTRUCTION

Face to Face Evening/Weekend Online Hybrid Accelerated
Yes No No No No

Mode of Instruction Definitions

CURRICULAR REQUIREMENTS

Minimum Credit Requirement 30 credits
Minimum Residence Credit Requirement 16 credits
Minimum Graduate Coursework Requirement Half of the coursework (15 out of 30 total credits) must be completed in graduate-level coursework; courses with the Graduate Level Coursework attribute are identified and searchable in the university's Course Guide.
Overall Graduate GPA Requirement 3.00 GPA required.
Other Grade Requirements Students must earn a B or above in all core curriculum coursework.
Assessments and Examinations No formal examination required. The research track requires a research project of 3–6 credits.
Language Requirements No language requirements.

Required Courses

Core Courses Required
B M I/​COMP SCI  576 Introduction to Bioinformatics3
B M I/​COMP SCI  567 Medical Image Analysis3
B M I 826 Special Topics in Biostatistics and Biomedical Infomatics (BMI 573 Health Informatics)3
B M I/​STAT  541 Introduction to Biostatistics3
or B M I/​POP HLTH  551 Introduction to Biostatistics for Population Health
or STAT/​F&W ECOL/​HORT  571 Statistical Methods for Bioscience I
Concentration Electives6
In consultation with their faculty advisor, students will select electives in an area of concentration within biomedical informatics. Examples include but are not limited to:
Statistical Methods for Clinical Trials
Statistical Methods for Epidemiology
Advanced Bioinformatics
Statistical Methods for Molecular Biology
Computational Methods for Medical Image Analysis
Statistical Methods for Medical Image Analysis
Health Systems Engineering
Health Information Systems
Data Science Electives6-7
In consultation with their faculty advisor, students will select two courses as electives in computer science and/or statistics. Coursework of high relevance includes the following areas:
Computational Methods for Medical Image Analysis
Statistical Methods for Medical Image Analysis
Statistical Methods I
Statistical Methods II
Mathematical Statistics I
Introduction to Statistical Inference
Professional Skills in Data Science
Statistical Computing
Theory and Application of Regression and Analysis of Variance I
Theory and Application of Regression and Analysis of Variance II
Introduction to Algorithms
Advanced Algorithms
Computer Vision
Database Management Systems: Design and Implementation
Topics in Database Management Systems
Introduction to Human-Computer Interaction
Human-Computer Interaction
Introduction to Artificial Intelligence
Machine Learning
Mathematical Foundations of Machine Learning
Natural Language and Computing
Advanced Natural Language Processing
Introduction to Combinatorial Optimization
Linear Programming Methods
Tools and Environments for Optimization
Introduction to Information Security
Professional Track Electives 1, 26-7
or
Research Track Electives 1, 37

Graduate School Policies

The Graduate School’s Academic Policies and Procedures provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.

Major-Specific Policies

Graduate Program Handbook

The Graduate Program Handbook is the repository for all of the program's policies and requirements.

Prior Coursework

Graduate Work from Other Institutions

With program approval, students are allowed to count no more than 9 credits of graduate coursework from other institutions. Coursework earned five or more years prior to admission to a master's degree is not allowed to satisfy requirements.

UW–Madison Undergraduate

With program approval, students are allowed up to 7 credits numbered 300 or above from a UW–Madison undergraduate degree to count toward the degree. Coursework earned five or more years prior to admission to a master's degree is not allowed to satisfy requirements.

UW–Madison University Special

With program approval, students are allowed to count no more than 9 credits of course work numbered 300 or above taken as a UW–Madison Special student. Coursework earned five or more years prior to admission to a master's degree is not allowed to satisfy requirements.

Probation

The status of a student can be one of three options:

  1. Good standing (progressing according to standards; any funding guarantee remains in place).
  2. Probation (not progressing according to standards but permitted to enroll; loss of funding guarantee; specific plan with dates and deadlines in place in regard to removal of probationary status).
  3. Unsatisfactory progress (not progressing according to standards; not permitted to enroll, dismissal, leave of absence or change of advisor or program).

ADVISOR / COMMITTEE

All students are required to conduct a yearly progress report meeting with their advisor, scheduled by December 17 and completed by April 30. Failure to do so will result in a hold being placed on the student's registration.

CREDITS PER TERM ALLOWED

15 credits

Time Constraints

If students have been absent for five or more years, they must file a new Graduate School application for admission and submit it with a new application fee. Master's degree students who have been absent for five or more consecutive years lose all credits that they have earned before their absence. Students may count the coursework completed before their absence for meeting graduate degree-credit requirements; the Graduate School will not count that work toward the Graduate School's minimum residence credit requirement.

Other

Funding guarantees are not provided for students in this program. Students are encouraged to explore funding options available across campus.

Graduate School Resources

Take advantage of the Graduate School's professional development resources to build skills, thrive academically, and launch your career. 

1. Understand, apply, and evaluate common informatics theories, methods, and tools related to biological and biomedical problems, health care and public health.

2. Apply, adapt, and validate an existing approach to a specific biomedical and health problem.

3. Produce solutions that address academic or industrial needs using informatics tools and knowledge.

4. Evaluate the impact of biomedical informatics applications and interventions.

5. Understand the challenges and limitations of technological solutions.

6. Demonstrate scholarly oral and written presentations.

7. Adhere to the professional and legal standards of conduct in Biomedical Data Science.

Faculty: Broman, Buchanan, Burnside, Chappell, Chen, Chung, Craven, Dewey, Doan, Dyer, Elwert, Gangnon, Gianola, Gitter, Keles, Kendziorski, Kim, Lu, Mao, Mendonça, Mumford, Newton, Ong, Page, Palta, Patel, Peissig, Rathouz (Chair), Rosa, Rosenberg, Roy, Singh, Sorkness, Tang, Wahba, Yandell, Velten, Yu, Zhang, Zhu