Data Science Across Disciplines

Session-05: MULTI-MODEL THINKING AND RIGOUR IN DATA SCIENCE

This week we focus on multi-model approaches as a way of thinking and how critical, pluralistic thinking can improve our understanding of the underlying phenomena implicit in data. We also discuss how to adopt a comprehensive approach to the data science process, and investigate indicators of rigor in data science.

The practical session involves combining perspectives derived from different computational models, as well as considering how diverse theoretical frameworks can help us approach phenomena of interest in different ways.

Reading lists & Resources

Required reading

• Breiman, L., 2001. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), pp.199-231. [pdf - article + comments/responses]
  • This is a delightful read from a legend, the article is on the first 18 pages followed by comment letters. The relevant parts for this week’s discussion are on sections 5 and section-8 (remember the discussion on Rashomon effect?), however, the opening parts are also mind opening.
• Page, S., 2018. Why “Many-Model Thinkers” Make Better Decisions. [online] Harvard Business Review. Available at: https://hbr.org/2018/11/why-many-model-thinkers-make-better-decisions [Accessed 30 October 2020]. [pdf]
  • Scott Page also has a full book on this which I can only recommend [link]
• Heale, R. and Twycross, A., 2015. Validity and reliability in quantitative studies. Evidence-based nursing, 18(3), pp.66-67. [pdf]
• James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning (Vol. 112, p. 18). New York: Springer. [link to the book] [the book page for other materials such as code]
• This is an amazing book overall and it’s freely available. For this week, you can check Chapter-5 on “Resampling Methods”. Focus on Section 5.1 and also look into 5.2 on Bootstrapping.

Optional reading

• Barbour, R.S., 2001. Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?. Bmj, 322(7294), pp.1115-1117. [pdf]
• Rule, A., Birmingham, A., Zuniga, C., Altintas, I., Huang, S.C., Knight, R., Moshiri, N., Nguyen, M.H., Rosenthal, S.B., Pérez, F. and Rose, P.W., 2019. Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. [pdf]