Teaching Data Science Through Case Studies in Public Health

Presentation on July 29, 2019 at the Joint Statistical Meetings in Denver, Colorado.

Transcript

1. Teaching Data Science Through Case Studies in Public Health Stephanie Hicks Assistant Professor, Biostatistics Johns Hopkins Bloomberg School of Public Health Faculty Member, Johns Hopkins Data Science Lab @stephaniehicks
2. What is a case study? https://ia600203.us.archive.org/11/items/cu31924018826713/cu31924018826713.pdf C. C. Langdell Dean of Harvard Law School from 1870 to 1895
3. What is a case study? Before Langdell's tenure, the study of law was very technical And students were simply told what the law is. During Langdell’s tenure, he applied the principles of pragmatism to the teaching of law à students were compelled to use their own reasoning powers to understand how the law might apply in a given case.
4. What is a case study? “Law, considered as a science, consists of certain principles or doctrines. To have such a mastery of these … is what constitutes a true lawyer; and hence to acquire that mastery should be the business of every earnest student of law.” – C. C. Langdell C. C. Langdell Dean of Harvard Law School from 1870 to 1895
5. What is a case study?
6. Elements of a case study Background: provides context for the problem to be solved Problem: a dilemma to be resolved or a decision to be made Supporting information: data, exhibits, interviews, supporting documentation, etc
7. Characteristics of a good case study Real real-world situations / real problems / based on real events / realistic, complex, and contextually rich situations / contemporary / recent / tells a real story Focused on students engages students / student centered / students make choices / active learning Link between theory and practice application of concepts in practice / bridges the gap between theory and practice / make choices about what theory to apply / highlight connections between academic topics and real-world situations / connects the academy and the workplace Ambiguous complex and ambiguous / present unresolved issues, situations, or questions / ”art of managing uncertainty” / without a detailed script / coping with ambiguities
8. Teaching
9. Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016 1. Teach statistical thinking. (Teach statistics as an investigative process of problem-solving and decision making). 2. Focus on conceptual understanding. 3. Integrate real data with a context and purpose. 4. Foster active learning. 5. Use technology to explore concepts and analyze data. 6. Use assessments to improve and evaluate student learning.
10. Case studies in data science? The American Statistician, Vol. 53, No. 4, pp 370-376 “The model calls for … substantial exercise[s] with nontrivial solutions that leave room for different analyses.
11. Case studies in data science? The American Statistician, Vol. 53, No. 4, pp 370-376 Elements of a “case study”: • Introduction • Data • Background • Investigations • Theory
12. Case studies at Johns Hopkins • Public Health Biostatistics • undergraduate Public Health Studies majors • modular structure: public health question, data, guided analysis, report • Statistical Methods in Public Health • MPH students and PhD students in Public Health/Nursing • occasional class-length detailed example analyses used to demonstrate statistical methods • Advanced Data Science • graduate students in Biostatistics (and others with interest) • exclusive use of case studies to motivate the concepts/topics
13. Characteristics of a good case study Real real-world situations / real problems / based on real events / realistic, complex, and contextually rich situations / contemporary / recent / tells a real story Focused on students engages students / student centered / students make choices / active learning Link between theory and practice application of concepts in practice / bridges the gap between theory and practice / make choices about what theory to apply / highlight connections between academic topics and real-world situations / connects the academy and the workplace Ambiguous complex and ambiguous / present unresolved issues, situations, or questions / ”art of managing uncertainty” / without a detailed script / coping with ambiguities
14. https://opencasestudies.github.io Leah Jager Margaret Taub
15. OCS: elements of a data science case study •Motivation • What is the question? What is the context/background for the question? •What is the data? •Data import •Data wrangling •Exploratory data analysis / data visualization •Data analysis •Summary of results
16. OCS – Health Expenditure https://opencasestudies.github.io/casestudies/ocs-healthexpenditure.html
17. OCS – Health Expenditure https://opencasestudies.github.io/casestudies/ocs-healthexpenditure.html
18. OCS – Health Expenditure https://opencasestudies.github.io/casestudies/ocs-healthexpenditure.html
19. OCS – Firearm Legislation and Fatal Police Shootings in the US https://opencasestudies.github.io/casestudies/ocs-police-shootings-firearm-legislation.html
20. Goals for OCS • Open • Large • Broad coverage of statistical methods and data science skills • Rich in terms of contextual questions of interest • Easily adaptable/modifiable • Continuously curated • Provide support for statistical software beyond R
21. How can you be involved? • Use our case studies (or parts of our case studies) ! • Provide feedback on our case studies • Contribute your own case studies • Contribute ideas for case studies https://opencasestudies.github.io
22. Thank you! JHU Biostatistics • Marie Diener-West • Karen Bandeen-Roche • Scott Zeger Feel free to send comments/questions: Twitter: @stephaniehicks Email: shicks19@jhu.edu JHU Research Assistants • Alexandra Stephens • Pei-Lun (Perry) Kuo • Hanchao (Ted) Zhang • Kexin (Sheena) Wang