Resources
- Learning from Data (Abu-Mostafa, Caltech)
- Machine Learning (Stanford CS229)
- Statistical Learning (Hastie & Tibshirani, Stanford)
- Big Data (Rockova, Chicago Booth)
- Data Science for Economists (McDermott, Oregon)
- The Effect (Huntington-Klein, Seattle)
- Program Evaluation (Heiss, Georgia State)
- Causal Inference (Imai, Harvard)
- Causal Inference: The Mixtape (Cunningham, Baylor)
- Machine Learning and Causal Inference (Athey & Wager, Sanford)
- Applied Econometrics: Mostly Harmless Big Data (Angrist & Chernozhukov, MIT)
Recommended Readings
Miscellaneous
Computing
Python
R
- Wickham, H., Advanced R
- Bryan, J., Data wrangling, exploration, and analysis with R
- Kabacoff, R., Data Visualization with R
- Grolemund, G. and H. Wickham, R for Data Science
- Dalpiaz, D., Applied Statistics with R
- Dalpiaz, D., R for Statistical Learning