## BIODS 215: Topics in Biomedical Data Science: Large-scale inference

The recent explosion of data generated in the fields of biology and medicine has led to many analytical challenges and opportunities for understanding human health. This graduate-level course focuses on methodology for large-scale inference from biomedical data. Topics include one-dimensional and multidimensional probability distributions; hypothesis testing and model comparison; statistical modeling; and prediction. This course will place a special emphasis on applications of these approaches to i) human genetic data; ii) hospital in-patient and health questionnaire data, which is increasingly available with the emergence of large precision initiatives like the UK Biobank and Precision Medicine Initiative; and iii) wearable and social network data.

Terms: Win
| Units: 2-3

## BIODS 217: Analytics Accelerator (CME 217)

This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics focused on COVID-19 during fall 2020 with the option to continue into winter 2021. Students apply a computational and data analytics lens and will use design thinking methodology. The course exposes students to ethics, emotional intelligence, unintended consequences of their work and team building supported by relevant lectures on data science and med/bio topics. Pre-requisites: none.nThe course application generally opens 5-6 weeks before registration for each quarter. If you missed the application for the quarter, please submit your application anyway to be added to the waitlist and to receive information regarding upcoming quarters.
https://forms.gle/oLtUe7dMKGy8bb2Z9

Terms: Aut, Win
| Units: 3
| Repeatable
2 times
(up to 6 units total)

Instructors:
Hanson, K. (PI)
;
Iaccarino, G. (PI)
;
Ioannidis, A. (PI)
...
more instructors for BIODS 217 »

Instructors:
Hanson, K. (PI)
;
Iaccarino, G. (PI)
;
Ioannidis, A. (PI)
;
Rivas, M. (PI)
;
Pendo, 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)
;
Tian, L. (PI)
;
Desai, M. (SI)
;
Efron, B. (SI)
;
Lavori, P. (SI)
;
Narasimhan, B. (SI)
;
Tamaresis, J. (SI)

## BIODS 235: Best practices for developing data science software for clinical and healthcare applications

Best practices for developing data science software for clinical and healthcare applications is a new seminar aimed to provide an overview of the strategies, processes, and regulatory hurdles to develop software implementing new algorithms or analytical approaches to be used in clinical diagnosis or medical practice. Upon completing this seminar, biomedical scientists implementing diagnostics, analytical, or AI-driven clinical decision support software should better understand how to protect, transfer, commercialize, and translate their inventions into the clinic. Topics include: Intellectual property strategies and technology licensing challenges; software development and quality best practices for the clinic; regulatory frameworks for clinical decision support and diagnostics informatics applications. It is open primarily to graduate students across Stanford and combines short lectures, guest industry speakers, and workshop sessions to allow participants to receive feedback on curren
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Best practices for developing data science software for clinical and healthcare applications is a new seminar aimed to provide an overview of the strategies, processes, and regulatory hurdles to develop software implementing new algorithms or analytical approaches to be used in clinical diagnosis or medical practice. Upon completing this seminar, biomedical scientists implementing diagnostics, analytical, or AI-driven clinical decision support software should better understand how to protect, transfer, commercialize, and translate their inventions into the clinic. Topics include: Intellectual property strategies and technology licensing challenges; software development and quality best practices for the clinic; regulatory frameworks for clinical decision support and diagnostics informatics applications. It is open primarily to graduate students across Stanford and combines short lectures, guest industry speakers, and workshop sessions to allow participants to receive feedback on current related projects that are undertaking. Enrollment limited to 25 to allow participants present their current projects. Prerequisites: Basic experience in programing and algorithm or software tool development. Ideally, the participant is actively implementing a new method/process/application in software aimed to be used in the clinic.

Terms: Win
| Units: 1

Instructors:
Bustamante, C. (PI)
;
De La Vega, F. (SI)

## BIODS 260B: Workshop in Biostatistics (STATS 260B)

Applications of statistical techniques to current problems in medical science. 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 an acceptable one page summary of two of the workshops, with choices made by the student.

Terms: Win
| Units: 1-2
| Repeatable
for credit

Instructors:
Newman, A. (PI)
;
Rivas, M. (PI)

## 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)
;
Efron, B. (PI)
;
Hastie, T. (PI)
...
more instructors for BIODS 299 »

Instructors:
Bustamante, C. (PI)
;
Efron, B. (PI)
;
Hastie, T. (PI)
;
Lu, Y. (PI)
;
Olshen, R. (PI)
;
Palacios, J. (PI)
;
Plevritis, S. (PI)
;
Rivas, M. (PI)
;
Sabatti, C. (PI)
;
Salzman, J. (PI)
;
Tian, L. (PI)
;
Tibshirani, R. (PI)
;
Yeung, S. (PI)
;
Zou, J. (PI)

## 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

Instructors:
Plevritis, S. (PI)
;
Yeung, S. (PI)

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