Program Overview

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Biomedical Data Science, 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 MS degree program in biomedical data science covers core concepts and allows for concentrated coursework, in both methodology and application.

Admissions

Please consult the table below for key information about this degree programs admissions requirements. The program may have more detailed admissions requirements, which can be found below the table.

• Fall Deadline: December 15
• Spring Deadline: The program does not admit in the spring.
• Summer Deadline: The program does not admit in the summer.
• GRE (Graduate Record Examinations): Not required.
• English Proficiency Test: Refer to the Graduate School: Minimum Requirements for Admission policy.
• Other Test(s) (e.g., GMAT, MCAT): n/a
• Letters of Recommendation Required: 3

Applicants include both those with bachelors degrees in an area of data-science (e.g., computer science, statistics), as well as health professionals and clinicians (e.g., MD's, PharmD's, RN's). It is expected that admitted applicants 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. The program will consider applicants 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 UWMadison Graduate School
• Three letters of recommendation
• Unofficial transcripts from each higher-education institution attended
• A statement of purpose
• International degree-seeking applicants must prove English proficiency
• Evidence of quantitative preparation, including at least two semesters of college calculus and either a course in linear algebra or courses in programming and data structures

Funding

Graduate School Resources

The Bursars Office provides information about tuition and fees associated with being a graduate student. Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid. Further funding information is available from the Graduate School.

Program Resources

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

Requirements

Minimum Graduate School Requirements

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

Major Requirements

Mode of Instruction

Mode of Instruction: Face to Face

Mode of Instruction Definitions

• Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students typically take enough credits aimed at completing the program in a year or two.
• Evening/Weekend: Courses meet on the UWMadison campus only in evenings and/or on weekends to accommodate typical business schedules.
• Face-to-Face: Courses typically meet during weekdays on the UW-Madison Campus.
• Hybrid: These programs combine face-to-face and online learning formats.
• Online: These programs are offered 100% online.

Curricular Requirements

• Minimum Credit Requirement: 30 credits
• Minimum Residence Credit Requirement: 16 credits
• Minimum Graduate Coursework Requirement: 15 credits must be graduate-level coursework.
• 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.
• Language Requirements: No language requirements.

Required Courses

Course List:

• Concentration Electives: 12 credits
  • Examples include but are not limited to:
    • I SY E 517: Decision Making in Health Care
    • B M I/STAT 541: Introduction to Biostatistics
    • B M I/POP HLTH 552: Regression Methods for Population Health
    • B M I/COMP SCI 567: Biomedical Image Analysis
• Data Science Electives: 12 credits
  • Examples include but are not limited to:
    • STAT 609: Mathematical Statistics I
    • STAT 610: Introduction to Statistical Inference
    • COMP SCI/E C E 766: Computer Vision
    • COMP SCI 544: Introduction to Big Data Systems
• Research Ethics Course: 1-2 credits
  • B M I 738: Ethics for Data Scientists
• Professional Development Elective: 1 credit
  • B M I 800: Becoming a Biomedical Data Scientist
• Research: 4 credits
  • B M I 699: Independent Study

Electives

Between the Concentration Electives and Data Science Electives, students must complete at least 6 credits of computer sciences-oriented courses and 6 credits of statistics-oriented courses.

Policies

Prior Coursework

• Graduate Credits Earned at Other Institutions: With program approval, students are allowed to transfer no more than 9 credits of graduate coursework from other institutions.
• Undergraduate Credits Earned at Other Institutions or UW-Madison: Refer to the Graduate School: Transfer Credits for Prior Coursework policy.
• Credits Earned as a Professional Student at UW-Madison (Law, Medicine, Pharmacy, and Veterinary careers): Refer to the Graduate School: Transfer Credits for Prior Coursework policy.
• Credits Earned as a University Special Student at UWMadison: With program approval, students are allowed to transfer no more than 9 credits of course work numbered 300 or above taken as a UWMadison University Special student.

Probation

Refer to the Graduate School: Probation policy.

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.

Credits Per Term Allowed

15 credit maximum. Refer to the Graduate School: Maximum Credit Loads and Overload Requests policy.

Time Limits

Refer to the Graduate School: Time Limits policy.

Grievances and Appeals

These resources may be helpful in addressing your concerns:

• Bias or Hate Reporting
• Graduate Assistantship Policies and Procedures
• Hostile and Intimidating Behavior Policies and Procedures
• Office of the Provost for Faculty and Staff Affairs
• Employee Assistance
• Employee Disability Resource Office
• Graduate School
• Office of Compliance
• Office Student Assistance and Support
• Office of Student Conduct and Community Standards
• Ombuds Office for Faculty and Staff
• Title IX

Professional Development

Graduate School Resources

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

Learning Outcomes

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.