printer friendly pageBIODS 203: Methods for Reproducible Population Health and Clinical Research (EPI 203, HRP 203)
Methods for Reproducible Population Health and Clinical Research is focused on key principles of rigorous and reproducible population health and clinical research. The course provides introductory technical training in collaborative workflows and reproducible programming practices using GitHub and R as well as core topics related to research integrity and scholarly publishing, including academic incentives, authorship, code and data sharing requirements, preregistration, conflicts of interest, and reporting guidelines. Content is designed for health policy, biomedical data science, and epidemiology graduate students supported by NIH training grants with reproducibility training requirements. Students in such programs should consult with their program director to ensure that this course will fulfill specific requirements of their program. Prerequisite: Basic knowledge of R.
Terms: Spr
| Units: 2
Instructors:
Rose, S. (PI)
BIODS 206: Applied Multivariate Analysis (STATS 206)
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Prerequisites:
STATS 116/118,
STATS 191/203,
MATH104 (recommended). See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Spr
| Units: 3
Instructors:
Owen, A. (PI)
;
Zhao, S. (TA)
BIODS 216: Generative AI and Medicine (MED 216)
This seminar course will explore the applications of Generative AI Technologies (ChatGPT, DALL-E, and many others) to medicine and healthcare. Course meetings will include a mix of outstanding speakers from health, business and technology as well discussions of burgeoning commercial and research projects in the space. We will ask students to brainstorm and informally pitch their own ideas for Generative AI projects to their peers and select faculty from academia and venture capital. All students are welcome. There are no prerequisites, but this course will be of interest to students who have taken
MED 213, "The Digital Future of Healthcare".
Terms: Aut, Spr
| Units: 1
Instructors:
Lin, B. (PI)
;
Lungren, M. (PI)
;
Yeung, S. (PI)
;
Norden, J. (SI)
;
Koul, A. (TA)
;
Liu, L. (TA)
BIODS 232: Consulting Workshop on Biomedical Data Science
The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devot
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The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students. Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are workshops that provide an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of our facilitator. At the workshop, the researcher explains the project, goals, and needs. Experts in the area across campus will be invited and contribute to the brainstorming. After the workshop, the facilitator will follow up,helping with immediate action items and summary of the discussion. The last session of each month is devoted to drop-in consulting. DBDS faculty are available to provide assistance with your research questions. Skills required of practicing biomedical consultants, including exposed to biomedical and health science applications, identification of data science related questions, selection or development of appropriate statistical and analytic approaches to answer research needs. Students are required to attend the regular workshops and participate one to two consulting projects as team members under the supervision of faculty members or senior staff. Depending on the nature of the consulting service, the students may need to conduct numerical simulation, plan sample size, design study, and analyze client data. the formal written report needs to be completed at the end of consulting projects. May be repeated for credit. Prerequisites: course work in applied statistics, data analysis, and consent of instructor.
Terms: Aut, Win, Spr
| Units: 1-2
| Repeatable
2 times
(up to 4 units total)
Instructors:
Lu, Y. (PI)
;
Sabatti, C. (PI)
;
Tamaresis, J. (PI)
...
more instructors for BIODS 232 »
Instructors:
Lu, Y. (PI)
;
Sabatti, C. (PI)
;
Tamaresis, J. (PI)
;
Tian, L. (PI)
;
Desai, M. (SI)
;
Tamaresis, J. (SI)
BIODS 260C: Workshop in Biostatistics (STATS 260C)
Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student
Terms: Spr
| Units: 1-2
| Repeatable
for credit
BIODS 271: Foundation Models for Healthcare (CS 277, RAD 271)
Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models. Prerequisites: Familiarity with machine learning principles at the level of
CS 229, 231N, or 224N
Terms: Spr
| Units: 3
BIODS 299: Directed Reading and Research
For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
Instructors:
Bustamante, C. (PI)
;
Daneshjou, R. (PI)
;
Efron, B. (PI)
...
more instructors for BIODS 299 »
Instructors:
Bustamante, C. (PI)
;
Daneshjou, R. (PI)
;
Efron, B. (PI)
;
Hastie, T. (PI)
;
Lu, Y. (PI)
;
Newman, A. (PI)
;
Palacios, J. (PI)
;
Raj, S. (PI)
;
Rivas, M. (PI)
;
Rodriguez, F. (PI)
;
Sabatti, C. (PI)
;
Salzman, J. (PI)
;
Tian, L. (PI)
;
Tibshirani, R. (PI)
;
Yeung, S. (PI)
;
Zou, J. (PI)
BIODS 352: Topics in Computing for Data Science (STATS 352)
A seminar-style course with lectures on a range of computational topics important for modern data-intensive science, jointly supported by the Statistics department and Stanford Data Science, and suitable for advanced undergraduate/graduate students engaged in either research on data science techniques (statistical or computational, for example) or research in scientific fields relying on advanced data science to achieve its goals. Seminars will alternate a presentation of a topic, usually by an expert on that topic, typically leading to exercises applying the techniques, with a follow up lecture to further discuss the topic and the exercises. Prerequisites: Understanding of basic modern data science and competence in related programming, e.g., in R or Python.
https://stats352.stanford.edu/
Terms: Spr
| Units: 1
Instructors:
Narasimhan, B. (PI)
;
Chambers, J. (SI)
BIODS 399: Graduate Research on Biomedical Data Science
Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
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