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Teaching Data Science Through Case Studies in Public Health

Teaching Data Science Through Case Studies in Public Health

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

Stephanie Hicks

July 17, 2019
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  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

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  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

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  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.

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  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

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  5. What is a case study?

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  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

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  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

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  8. Teaching

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  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.

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  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.

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  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

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  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

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  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

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  14. https://opencasestudies.github.io
    Leah Jager
    Margaret
    Taub

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  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

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  16. OCS – Health Expenditure
    https://opencasestudies.github.io/casestudies/ocs-healthexpenditure.html

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  17. OCS – Health Expenditure
    https://opencasestudies.github.io/casestudies/ocs-healthexpenditure.html

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  18. OCS – Health Expenditure
    https://opencasestudies.github.io/casestudies/ocs-healthexpenditure.html

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  19. OCS – Firearm Legislation and Fatal Police
    Shootings in the US
    https://opencasestudies.github.io/casestudies/ocs-police-shootings-firearm-legislation.html

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  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

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  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

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  22. Thank you!
    JHU Biostatistics
    • Marie Diener-West
    • Karen Bandeen-Roche
    • Scott Zeger
    Feel free to send comments/questions:
    Twitter: @stephaniehicks
    Email: [email protected]
    JHU Research Assistants
    • Alexandra Stephens
    • Pei-Lun (Perry) Kuo
    • Hanchao (Ted) Zhang
    • Kexin (Sheena) Wang

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