Concentration Electives (12 credits)
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:
| ISYE 517 | Decision Making in Health Care |
| BMI/STAT 541 or | Introduction to Biostatistics |
| BMI/POP HLTH 551 or | Introduction to Biostatistics for Population Health |
| STAT/FEW/HORT 571 | Statistical Methods for Bioscience I |
| BMI/POP HLTH 552 | Regression Methods for Population Health |
| BMI/CS 567 | Medical Image Analysis |
| STAT/FEW/HORT BMI 572 | Statistical Methods for Bioscience II |
| BMI 573 | Foundations of Data-Driven Healthcare |
| BMI/CS 576 | Introduction to Bioinformatics |
| BMI/BIOCHEM/BMOLCHEM/MATH 609 | Mathematical Methods for Systems Biology |
| BMI/STAT 641 | Statistical Methods for Clinical Trials |
| BMI/STAT 642 | Statistical Methods for Epidemiology |
| BMI/POP HLTH 651 | Advanced Regression Methods for Population Health |
| BMI/STAT 741 | Survival Analysis Theory and Methods |
| BMI/CS 767 | Computational Methods for Medical Image Analysis |
| BMI/STAT 768 | Statistical Methods for Medical Image Analysis |
| BMI 773 | Clinical Research Informatics |
| BMI/CS 776 | Advanced Bioinformatics |
| BMI 775 | Computational Network Biology |
| BMI/STAT 877 | Statistical Methods for Molecular Biology |
Research Ethics (1 credit) – BMI 738 – Ethics for Data Scientists or any other Responsible Conduct of Research (RCR Course List)
Research Credits (3-6 credit) – BMI 699
Electives (0-3 credit)
The program requires that students complete a total of 31 credits.
For the most accurate and up-to-date course requirements, always consult The Graduate Guide https://guide.wisc.edu/graduate/
Data Science Electives (12 credits)
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:
| STAT 609 | Mathematical Statistics I |
| STAT 610 | Introduction to Statistical Inference |
| STAT 627 | Professional Skills in Data Science |
| STAT 771 | Statistical Computing |
| STAT 849 | Theory and Application of Regression and Analysis of Variance I |
| STAT 850 | Theory and Application of Regression and Analysis of Variance II |
| CS 540 | Introduction to Artificial Intelligence |
| CS 545 | Natural Language and Computing |
| CS 642 | Introduction to Information Security |
| CS 564 | Database management Systems: Design and Implementation |
| CS 570 | Introduction to Human-Computer Interaction |
| CS 577 | Introduction to Algorithms |
| CS 760 | Machine Learning |
| CS 761 | Mathematical Foundations of Machine Learning |
| CS 764 | Topic in Database Management Systems |
| CS 766 | Computer Vision |
| CS 769 | Advanced Natural Language Processing |
| CS 787 | Advanced Algorithms |
| CS/EDPSYCH/PSYCH 770 | Human-Computer Interaction |
| CS/ISYE/MATH 425 | Introduction to Combinatorial Optimization |
| CS/ISYE/MATH/STAT 525 | Linear Optimization |
| CS/ISYE 635 | Tools and Environments for Optimization |
