The Michigan Institute for Data Science (MIDAS) invites you to register for its Summer 2021 Bootcamp for biomedical scientists.

Bootcamp on Introductory Data Science

July 26 – 30, 2021 7am to 4:15pm

Description and Expected Results:

Participants will learn how to a) utilize supervised and unsupervised machine learning for clinical applications; b) employ deep learning for clinical applications; c) determine which data science/artificial intelligence techniques are appropriate for a given clinical application; and d) implement these methodologies in Python code.

After completing the bootcamp, participants will be able to determine which data science/artificial intelligence techniques are appropriate for a given clinical application and apply them to their own clinical and/or research activities.

What: the main components include:

  • Math and algorithmic foundations for data science
  • Key concepts of data science
  • Introduction to Python programming
  • Machine learning, support vector machine, artificial neural network, deep learning
  • Example of biomedical research projects with data science
  • Incorporating data science in biomedical grant proposals

Who should attend: this workshop is open to all biomedical scientists, but the content is geared towards junior faculty members and those from the public and private sector who are interested in learning about incorporating data science into their research. Please register by June 25. Later registrants will be accepted only if spots are available. 

Prerequisite: college level math and statistics.

Joint Providership ACCME Statement:

This activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of the University of Michigan Medical School and The Michigan Institute for Data Science (MIDAS). The University of Michigan Medical School is accredited by the ACCME to provide continuing medical education for physicians.

The University of Michigan Medical School designates this live activity for a maximum of XX AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Lead instructor:

Kayvan Najarian
Professor of Computational Medicine and Bioinformatics
MIDAS Associate Director

Other Instructors:

Ivo Dinov
Professor of Human Behavior and Biological Sciences

Jonathan Gryak
Research Assistant Scientist of Computational Medicine Bioinformatics,
Senior Scientist at MIDAS

Michael Mathis
Assistant Professor of Anesthesiology

Nambi Nallasamy
Assistant Professor of Ophthalmology

Michael Sjoding
Assistant Professor in the Division of Pulmonary
Critical Care and the Department of Internal Medicine

Ryan Stidham
Assistant Professor of Gastroenterology

Bootcamp Schedule

Monday, July 26

7:00am – 8:30am 

Session 1: Welcome and introduction to the program

8:30am – 8:45am

Break

8:45am – 10:15am

Session 2: Math foundations I – Brief introduction to mathematical foundations of machine learning

10:15am – 10:30am

Break

10:30am – 12:00pm

Session 3: Math foundations II – Brief introduction to mathematical foundations of machine learning

12:00pm – 1:00pm

Lunch Break

1:00pm – 2:30pm

Session 4: Clustering vs Classification; k-means; k-Nearest Neighbors

2:30pm – 2:45pm

Break

2:45pm – 4:15pm

Session 5: Introduction to Python programming

Tuesday, July 27

7:00am – 8:30am

Session 6: Linear regression, logistic regression

8:30am – 8:45am

Break

8:45am – 10:15am

Session 7: Simple Classification methods and feature analysis

10:15am – 10:30am

Break

10:30am – 12:00pm

Session 8: Model validation and assessment

12:00pm – 1:00pm

Lunch Break

1:00pm – 2:30pm

Session 9: Using machine learning for clinical and health applications I

2:30pm – 2:45pm

Break

2:45pm – 4:15pm

Session 10: Python programming for linear regression, logistic regression; ridge regression and Naïve Bayes

Wednesday, July 28

7:00am – 8:30am

Session 11: Artificial neural networks I

8:30am – 8:45am

Break

8:45am – 10:15am

Session 12: Regression trees

10:15am – 10:30am

Break

10:30am – 12:00pm

Session 13: Random Forest

12:00pm – 1:00pm

Lunch Break

1:00pm – 2:30pm

Session 14: Using machine learning for clinical and health applications II

2:30pm – 2:45pm

Break

2:45pm – 4:15pm

Session 15: Python programming for neural networks, regression trees and random forest

Thursday, July 29

7:00am – 8:30am

Session 16: Support vector machines

8:30am – 8:45am

Break

8:45am – 10:15am

Session 17: Deep learning I

10:15am – 10:30am

Break

10:30am – 12:00pm

Session 18: Deep Learning II

12:00pm – 1:00pm

Lunch Break

1:00pm – 2:30pm

Session 19: Python programming for support vector machines 

2:30pm – 2:45pm

Break

2:45pm – 4:15pm

Session 20: Python programming for deep learning

Friday, July 30

7:00am – 8:30am

Session 21: Strategies to add data science flavor to health related projects and grant proposals

8:30am – 8:45am

Break

8:45am – 10:15am

Session 22: Using machine learning for clinical and health applications III

10:15am – 10:30am

Break

10:30am – 12:00pm

Session 23: Using machine learning for clinical and health applications IV

12:00pm – 1:00pm

Lunch Break

1:00pm – 2:30pm

Session 24: Guidelines on using machine learning for clinical applications

2:30pm – 2:45pm

Break

2:45pm – 4:15pm

Session 25: Wrap-up; Q&A; plans for follow-up sessions during the following year

Workshop on Introductory Data Science (Fall 2020)

Workshop on Introductory Data Science

When: Oct. 27, 2020, 9 am to 4 pm, virtual event.

Workshop objectives: This workshop will provide an introduction to data science from a biomedical perspective. After participating in this workshop trainees will be able to determine in which areas of clinical practice the application of data science, machine learning, artificial intelligence, or statistics is most appropriate, and where these techniques can be integrated into their own practice.

ACCME Statement: The University of Michigan Medical School is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The University of Michigan Medical School designates this live activity for a maximum of 5.5 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Who should attend: this workshop is open to all biomedical scientists, but the content is geared towards junior faculty members who are interested in learning about the bootcamp and who plan to incorporate data science in their research.

What: The workshop includes an introduction to key concepts of data science, overview of the bootcamp and other training opportunities on campus, presentations by biomedical faculty members who have successfully incorporated data science in their research.

Lead instructor: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

For questions, please contact:James Walsh, MIDAS Administrative Assistant Sr. (walshjam@umich.edu)

09:00am – 09:15am Welcome & brief MIDAS presentation

Jing Liu, MIDAS Managing Director


09:15am – 09:30am Introduction of instructors and participants

Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director


09:30am – 10:30am Goals and general overview of training program; who can best benefit from this training?

Speaker: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director


10:30am – 10:45am Break


10:45am – 11:15am Why is training in data sciences necessary for physician-scientist in all fields?

Speaker: Brahmajee Nallamothu, Professor in the Division of Cardiovascular Diseases and the Department of Internal Medicine


11:15am – 12:00pm Data sciences, machine learning, artificial intelligence and statistics; similarities and differences

Speaker: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director


12:00pm – 01:00pm Lunch break


01:00pm – 01:45pm Success story I – Data science for clinical diagnosis of ARDS

Speaker: Michael Sjoding, Assistant Professor in the Division of Pulmonary and Critical Care and the Department of Internal Medicine


01:45pm – 02:30pm Success story II – Data science for anesthesiology

Speaker: Michael Mathis, Assistant Professor of Anesthesiology


02:30pm – 02:45pm Break


02:45pm – 04:00pm Description of boot camp, data science training resources on campus, Q&A and wrap-up

Moderator: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director


04:00pm – 04:30pm Open Virtual Networking [Link to Networking Platform]

Hosted via Remo, introduction and instructions will be given by James Walsh (MIDAS Admin.)