## BIODS 210: Configuration of the US Healthcare System and the Application of Big Data/Analytics

Almost no country anywhere in the world can afford their healthcare. Starbucks spends more money on healthcare than their coffee. General Motors spends more money on healthcare than steel for their cars. The macro economic effects of healthcare costs along with significant sociodemographic shifts impact every part of our society. In the United States there have been twin massive policy responses (health technology and health payment reform) which are now being coupled with unprecedented venture and industry activity. nnThe purpose of this course will be to understand real world and industry based opportunities within the healthcare system. The course will offer an introductory framework for understanding healthcare configuration encompassing macro economics, health policy 101 and market dynamics. The bulk of the course will focus on the major changes in how (big) data and analytics is being applied in industry and why this matters. There will be an introduction to the most relevant fro
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Almost no country anywhere in the world can afford their healthcare. Starbucks spends more money on healthcare than their coffee. General Motors spends more money on healthcare than steel for their cars. The macro economic effects of healthcare costs along with significant sociodemographic shifts impact every part of our society. In the United States there have been twin massive policy responses (health technology and health payment reform) which are now being coupled with unprecedented venture and industry activity. nnThe purpose of this course will be to understand real world and industry based opportunities within the healthcare system. The course will offer an introductory framework for understanding healthcare configuration encompassing macro economics, health policy 101 and market dynamics. The bulk of the course will focus on the major changes in how (big) data and analytics is being applied in industry and why this matters. There will be an introduction to the most relevant frontier technologies being applied to healthcare data including but not limited to mobility, connectivity, machine learning, "omics". Each technology will be showcased by a seminar with a relevant company outlining their business model application to a care healthcare data technology. nnWhat differentiates this course from other health policy and health data courses:n-- Marketplace overview + trends (Prospective look at new emerging models versus what has previously worked)n-- Industry perspectiven-- Venture perspectiven-- Data in action (industry applications of "big data")n-- Course will be heavily focused on real world/industry based application.

Terms: Spr
| Units: 2
| Repeatable
2 times
(up to 4 units total)

Instructors:
Yeung, S. (PI)
;
Kaushal, M. (SI)

## 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)
;
Narasimhan, B. (PI)
;
Sabatti, C. (PI)
...
more instructors for BIODS 232 »

Instructors:
Lu, Y. (PI)
;
Narasimhan, B. (PI)
;
Sabatti, C. (PI)
;
Tamaresis, J. (PI)
;
Tian, L. (PI)
;
Desai, M. (SI)
;
Efron, B. (SI)

## BIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236)

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Exper
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Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Experts in the field will present guest lectures. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS 109, and basic machine learning such as
CS 229. No prior knowledge of genomics is necessary.

Terms: Spr
| Units: 3

Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)

## 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 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)
;
Rivas, M. (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 jointly supported by the Statistics department and Stanford Data Science, and suitable for doctoral 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 usually consist of a student presentation of a relevant technical topic followed by discussion of the topic by all. Topics will be assigned to individuals to combine relevance for the course and suitability to the individual student's background and research interests. Prerequisites: Competence in the basic data science needed for the student's research goals plus preparation for presenting a suitable topic. Before enrolling, participants should have a topic approved as prescribed on the website
https://stat352.stanford.edu.

Terms: Spr
| Units: 1

Instructors:
Chambers, J. (PI)
;
Narasimhan, B. (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

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