@ = material available online
Books and booklets
@ Gentzkow, Matthew and Jesse M. Shapiro: Code and Data for the Social Sciences: A Practitioner's Guide. Available at http://web.stanford.edu/~gentzkow/research/CodeAndData.xhtml or http://web.stanford.edu/~gentzkow/research/CodeAndData.pdf
@ Grolemund, Garrett and Hadley Wickham: R for Data Science
@ Hastie, Trevor, Robert Tibshirani, and Jerome Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (selected chapters).
@ James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R.
@ M?rken, Knut: Numerical Algorithms and Digital Representation. Mimeo, UiO
Articles
@ Agrawal, Ajay K., Joshua Gans, and Avi Goldfarb (eds). “The Economics of Artificial Intelligence: An Agenda” (forthcoming). University of Chicago Press. Selected chapters.
@ Gentzkow, Matthew, Bryan T. Kelly, and Matt Taddy. “Text as Data.“ Journal of Economic Literature, 57(3):535-74.
@ Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani: “The Parable of Google Flu: Traps in Big Data Analysis”, Science 343(6176): 1203-1205 (2014).
@ Varian, Hal R. 2014. "Big Data: New Tricks for Econometrics." Journal of Economic Perspectives, 28 (2): 3-28.