Stanford Root

Schedule

Stanford Root

Schedule

BMDS 215

Data Science for Medicine

UNITS:3
GRADING:Medical Option (Med-Ltr-CR/NC)
LEVEL:Graduate
GER:—

The widespread adoption of electronic health records (EHRs) has created a new source of big data namely, the record of routine clinical practice as a by-product of care. This graduate class will teach you how to use EHRs and other patient data to discover new clinical knowledge and improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of research questions and the study designs used to address them, describe common healthcare data sources and their relative advantages and limitations, extract and transform various kinds of clinical data to create analysis-ready datasets, design and execute an analysis of a clinical dataset based on your familiarity with the workings, applicability, and limitations of common statistical methods, evaluate and criticize published research using your knowledge of 1-4 to generate new research ideas and separate hype from reality. Prerequisites: CS 106A or equivalent, STATS BMDS 60 or equivalent. Recommended: STATS BMDS 216, CS 145, STATS 305NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.

Syllabus for selected term:
View Autumn 2026 Syllabus

Sections

1 Term
Lecture 1Open
ID: 13682
0 / 90 enrolled
DAYS:Tuesday, Thursday
TIME:3 PM – 4:20 PM
LOCATION:CODAB60
INSTRUCTOR:
Shah, Nigam, Drusinsky, Maya, Mao, Alan, Lin, Bridget, Utti, Vivian
3units

BMDS 215: Data Science for Medicine

3 units · Medical Option (Med-Ltr-CR/NC)

The widespread adoption of electronic health records (EHRs) has created a new source of big data namely, the record of routine clinical practice as a by-product of care. This graduate class will teach you how to use EHRs and other patient data to discover new clinical knowledge and improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of research questions and the study designs used to address them, describe common healthcare data sources and their relative advantages and limitations, extract and transform various kinds of clinical data to create analysis-ready datasets, design and execute an analysis of a clinical dataset based on your familiarity with the workings, applicability, and limitations of common statistical methods, evaluate and criticize published research using your knowledge of 1-4 to generate new research ideas and separate hype from reality. Prerequisites: CS 106A or equivalent, STATS 60 or equivalent. Recommended: STATS 216, CS 145, STATS 305NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.

Offered in Autumn 2026 at Stanford University.

Autumn 2026 sections

  • Lecture — Tuesday Thursday 3:00 PM – 4:20 PM — CODAB60 — Shah, Nigam, Drusinsky, Maya, Mao, Alan, Lin, Bridget, Utti, Vivian (Graduate)

More BMDS courses

  • BMDS 201C: Biomedical Data Science Student Seminar (BMDS 201A, BMDS 201B)
  • BMDS 202: An overview of Biomedical Data Science
  • BMDS 205: Bioinformatics for Stem Cell and Cancer Biology (STEMREM 205)
  • BMDS 210: Modeling Biomedical Systems (CS 270)
  • BMDS 212: Introduction to Biomedical Informatics Research Methodology (BIOE 212, CS 272, GENE 212)
  • BMDS 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)
  • BMDS 216: Representations and Algorithms for Molecular Biology: Lectures
  • BMDS 217: Translational Bioinformatics (BIOE 217, CS 275, GENE 217)
  • BMDS 218: Data Centric AI for Healthcare (CS 287)
  • BMDS 219: Mathematical Models and Medical Decisions
  • BMDS 221: Machine Learning Approaches for Data Fusion in Biomedicine
  • BMDS 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222)

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