This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination. Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained. Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings. Next, Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies. Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities. Finally, Module 4 will focus on reproducibility, evaluation and ethical issues when deploying models based on biomedical data, with emphasis on translation to practice. Emphasis will be placed questions, data and methods that advance health and medicine. Primary learning goals for this course include how to frame biomedical health questions, what data are needed to answer those questions, and what methodological constructs can be leveraged to probe and answer those questions. This course is a newly designed course for the PhD program of the Department of Biomedical Data Science but open to all. Basic familiarity with the following concepts & skills will be necessary for completing the problem sets: A crash course on commonly used bioinformatics tools (see BIOS 201), A theoretical exploration of bioinformatics algorithms (see BIOMEDIN BMDS 214), an introduction to programming/Python/Linux (see BIOS 201, BIOS 205, CS 106A, etc.), an intro to statistics and probability (see STATS BMDS 116, STATS BMDS 200). If you have any questions about whether this course is the right level for you, please speak with a TA or instructor. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
3 units · Medical Option (Med-Ltr-CR/NC)
This course introduces the data modalities and methods valuable to ask and answer probing and novel questions that advance biomedicine. You will get exposure to a variety of current data types from imaging and omics to patient-centric and digital health generated data types. You will also be exposed to the core methodological concepts useful to analyze these data in isolation or in combination. Specifically, in four separate modules taught by expert faculty in each area the basic principles of each module will be defined and explained. Module 1, Clinical Data and Systems, will explain the basics of Electronic Health Records, and how they operate in health care settings. Next, Module 2, Image Data Health Science, will focus on an introduction to the main imaging modalities in medicine and how methodological analysis using machine vision can be used on large studies. Module 3 will focus on fusing different data streams such as clinical, imaging, molecular and other data modalities. Finally, Module 4 will focus on reproducibility, evaluation and ethical issues when deploying models based on biomedical data, with emphasis on translation to practice. Emphasis will be placed questions, data and methods that advance health and medicine. Primary learning goals for this course include how to frame biomedical health questions, what data are needed to answer those questions, and what methodological constructs can be leveraged to probe and answer those questions. This course is a newly designed course for the PhD program of the Department of Biomedical Data Science but open to all. Basic familiarity with the following concepts & skills will be necessary for completing the problem sets: A crash course on commonly used bioinformatics tools (see BIOS 201), A theoretical exploration of bioinformatics algorithms (see BIOMEDIN 214), an introduction to programming/Python/Linux (see BIOS 201, BIOS 205, CS 106A, etc.), an intro to statistics and probability (see STATS 116, STATS 200). If you have any questions about whether this course is the right level for you, please speak with a TA or instructor. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Offered in Winter 2026 at Stanford University.