1.2 Other books

The reason for my writing this book is that there is not one specific book that I can point to as a good source of information. Various books all have their pros and cons. These are some that I found that are probably most helpful:

1. Introductory Econometrics (Wooldridge 2018)

• Excellent intro book (also see Using R for Introductory Econometrics (Heiss 2016)
• Great coverage of linear modeling
• No real programming and no machine learning
• Expensive (unless you get an older edition)

2. Hadley Wickham books

• Great for programming and R (not heavy on stats)
• Freely available online (not ggplot2 unless you build it from source)
• R for Data Science (Grolemund and Wickham 2016)
• Advanced R (Wickham 2014)
• Ggplot2 (Wickham 2010)

3. Trevor Hastie & Robert Tibshirani books

• Excellent coverage of the theoretical techniques (not applied knowledge at all)
• Freely available online
• Introduction to Statistical Learning (G. James et al. 2013) with slides/videos
• Elements of Statistical Learning (Hastie, Tibshirani, and Friedman 2009)

4. Data Mining for Business Analytics (Shmueli et al. 2017)

• Too light on material, not enough statistics and programming
• Time series section is complete BS
• Expensive

5. Real econometrics books

• Great coverage of econometrics. Virtually unreadable for students. No programming. No machine learning.
• Econometric Analysis (Greene 2011)
• Econometrics (Hansen 2018)

6. Computational statistics books
   

• Handbook of Statistics 32: Computational Statistics with R
• Handbook of Statistics 9: Computational Statistics
• Computational Statistics by James Gentle
• Expensive and often too theoretical for the audience

References

Wooldridge, Jeffrey. 2018. Introductory Econometrics: A Modern Approach. 6th ed. Cengage.

Heiss, Florian. 2016. Using R for Introductory Econometrics. 1st ed. CreateSpace. http://www.urfie.net/read/mobile/index.html#p=1.

Grolemund, Garrett, and Hadley Wickham. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1st ed. O’Reilly. https://r4ds.had.co.nz/.

Wickham, Hadley. 2014. Advanced R. 1st ed. Chapman & Hall. http://adv-r.had.co.nz/.

Wickham, Hadley. 2010. Ggplot2. 1st ed. Springer. https://github.com/hadley/ggplot2-book.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. 1st ed. Springer. http://www-bcf.usc.edu/~gareth/ISL/.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer. https://web.stanford.edu/~hastie/ElemStatLearn/.

Shmueli, Galit, Peter C Bruce, Inbal Yahav, Nitin R Patel, and Kenneth C Lichtendahl Jr. 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. 1st ed. Wiley.

Greene, William H. 2011. Econometric Analysis. 7th ed. Pearson.

Hansen, Bruce. 2018. Econometrics. Unpublished. https://www.ssc.wisc.edu/~bhansen/econometrics/.