Hicks Assistant Professor, Biostatistics Johns Hopkins Bloomberg School of Public Health Faculty Member, Johns Hopkins Data Science Lab @stephaniehicks
transparent ways you do your data analysis" This encompasses: • literate code documents (e.g. Rmarkdown, Notebooks, etc.) • analysis scripts • pipelines • environment management tools • etc. Mike Love Tiffany Timbers
This encompasses: • literate code documents (e.g. Rmarkdown, Notebooks, etc.) • analysis scripts • pipelines • environment management tools • etc. We define Data Science Workflows broadly, as: I’m going to focus on this as it relates to Teaching and Outreach
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.
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
problem to be solved Problem: a dilemma to be resolved or a decision to be made Supporting information: data, exhibits, interviews, supporting documentation, etc
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
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.
students online, in person – 25 TAs – Python) (Harvard SPH – Biostats -- 150 students – online, in person – 8 TAs – R) (Johns Hopkins SPH – 25 students – in person – PhD Biostats – 2 TAs – R)
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
into statistics courses targeted towards students with non-mathematical backgrounds (e.g. public health, sciences) “Using a reproducible workflow in statistics is vital to a complete data analysis, yet for faculty and students with limited computing background, learning version control tools such as Git can be difficult and intimidating. [Here] we outline some of the ways that the Git workflow can be implemented in statistics courses at all levels”
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