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Creating a New Data Science Track In a Traditional Biomedical Informatics Training Program

Creating a New Data Science Track In a Traditional Biomedical Informatics Training Program. W. Chapman, D. Borbolla , K. Eilbeck , S. Abdelrahman, J. Ferraro, B. Chapman, C. Weir, J. Hurdle, O. Patterson, E. Hernandez University of Utah. Disclosure. Wendy Chapman is a consultant for IBM.

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Creating a New Data Science Track In a Traditional Biomedical Informatics Training Program

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  1. Creating a New Data Science Track • In a Traditional Biomedical Informatics • Training Program W. Chapman, D. Borbolla, K. Eilbeck, S. Abdelrahman, J. Ferraro, B. Chapman, C. Weir, J. Hurdle, O. Patterson, E. Hernandez University of Utah

  2. Disclosure • Wendy Chapman is a consultant for IBM.

  3. Why create a data science track?

  4. University of Utah1st informatics department in the U.S. 1964 – department 1965 – 1st PhD Homer Warner, Al Pryor, Reed Gardner Clayton PD. “Why not? Let's do it!” Presentation of the Morris F. Collen Award to Homer R. Warner, MD, PhD. J Am Med Inform Assoc 1995;2:2 137-142.

  5. Interviews with new PhD graduates I never hire informatics students—they can do what my research requires There are many things I wanted to do but I didn’t have the technical skills. students faculty

  6. Objective: Enable and empower

  7. What?

  8. I wouldn’t hire most of our grads as a data scientist We are producing informaticists with data science skills What type of graduate are you trying to produce?

  9. Programming Language R is better for statistics Introducing two languages in the first year is not as effective for learning

  10. Leaning towards this • 1st year Python • Intro to Programming • Intro to Data Science from CS • Summer SIG in R • Second year Advanced Stats in R

  11. Unique vs duplicative vs outsourcing CS is not in the business of teaching graduate students to program We shouldn’t be teaching introductory programming

  12. Unique vs duplicative vs outsourcing CS is not in the business of teaching graduate students to program We shouldn’t be teaching introductory programming Teaching introduction to programming

  13. Unique vs duplicative vs outsourcing Many of our data science track students do not have enough math background Why can’t students take machine learning from CS?

  14. Unique vs duplicative vs outsourcing We are an informatics department and shouldn’t be replicating all classes We will be irrelevant if we send our students to CS for too many courses

  15. Unique vs duplicative vs outsourcing Many of our data science track students do not have enough math background Why can’t students take machine learning from CS? Teaching 2 machine learning courses Advanced students take CS ML

  16. Evening the playing field Others have strong computational backgrounds Some of our data science track students are pivoting

  17. Evening the playing field Others have strong computational backgrounds Some of our data science track students are pivoting Summer Data Camp Intro to Python

  18. Data Science Track within BMI Degree This class goes way too slow and caters to beginners! This class is way too advanced!

  19. Data Science Track within BMI Degree This class goes way too slow and caters to beginners! This class is way too advanced! Two Intro to Programming courses

  20. What Not to Teach Data science grads need to know more Data science track requires too many credits Special Interest Group

  21. How?

  22. Relationship of data science and informatics https://www.innoarchitech.com/blog/what-is-data-science-does-data-scientist-do

  23. Learning Health System Thanks to Jonathan Silverstein

  24. Knowledge Data Wisdom Thanks to Jonathan Silverstein

  25. Informatics Department Phenotype

  26. Informatics Department Phenotype

  27. You have to give up something

  28. Decision making Data Science Committee Brings decisions to curriculum committee Final voting by all faculty Who makes what decisions?

  29. Assess and improve the quality of teaching https://sites.google.com/view/biomed-data-science-edu/home

  30. Building infrastructure

  31. Building infrastructure Creating an On-demand On-line Learning Environment for Biomedical Informatics and Data Science Wed. 15:45 – 16:15 Grand Ballroom Salon EFG

  32. Iterative Report to curriculum committee Focus groups Individual discussions Try Assess Improve Repeat

  33. Conclusion We keep improving Our “products” are pretty impressive - In spite of the implication of our improving over time

  34. Q & A

  35. Thank you!

  36. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise. AMIA 2019 Informatics Educators Forum | amia.org

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