Readings

Session 1

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59.

Session 2

Varian, H. R. (2016). Causal inference in economics and marketing. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7310–7315.

Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.

Further readings

Inferential statistics

• Discovering Statistics Using R, by Andy Field, Jeremy Miles & Zoe Field
• YaRrr! The Pirate's Guide to R, by Nathaniel Phillips
• Learning statistics with R, by Danielle Navarro

Mixed models

• Data Analysis Using Regression and Multilevel/Hierarchical Models, by Andrew Gelman & Jennifer Hill
• Analyzing Linguistic Data: A Practical Introduction to Statistics using R, by R. Harald Baayen

Bayesian statistics

• Statistical Rethinking, by Richard McElreath
• Doing Bayesian Data Analysis, by John Kruschke

Machine learning

• Applied Predictive Modeling, by Max Kuhn & Kjell Johnson
• The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, & Jerome Friedman