Chapter 22 Concluding remarks, next steps, and open problems

Last updated: 6 June 2021.

22.1 Concluding remarks


22.2 Next steps

This book has covered a lot of ground. Chances are there are aspects that you want to explore further, building on the foundation that you have established. If so, then I’ve accomplished what I set out to do.

For learning more about R in terms of data science, there is only one recommendation that is possible and that is Wickham and Grolemund (2017). To deepen your understanding of R itself, go next to Wickham (2019a).

If you’re interested in learning more about causality then start with (cunninghamnorap?) and Huntington-Klein (2021), before moving onto books such as…

If you’re interested to learn more about statistics then begin with Johnson, Ott, and Dogucu (2022), McElreath (2020), and Gelman et al. (2014).

There is only one next natural step if you’re interested in learning more about statistical (what’s come to be called machine) learning and that’s James et al. (2017) followed by Friedman, Tibshirani, and Hastie (2009).

If you’re interested in sampling then the next book to turn to is Lohr (2019). To deepen your understanding of surveys and experiments, go next to Gerber and Green (2012) in combination with Kohavi, Tang, and Xu (2020).

Graphs and communication in that regard should go to…

Writing go to…

Thinking through production and SQL and things like, a next natural step is…

22.3 Open problems

  • Optimal names


Friedman, Jerome H., Robert Tibshirani, and Trevor Hastie. 2009. The Elements of Statistical Learning. Springer.
Gelman, Andrew, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. 2014. Bayesian Data Analysis. 3rd ed. CRC Press.
Gerber, Alan, and Donald Green. 2012. Field Experiments: Design, Analysis, and Interpretation. W W Norton.
Huntington-Klein, Nick. 2021. The Effect: An Introduction to Research Design and Causality. Chapman & Hall.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2017. An Introduction to Statistical Learning with Applications in r.
Johnson, Alicia A., Miles Ott, and Mine Dogucu. 2022. Bayes Rules! An Introduction to Bayesian Modeling with r. CRC Press.
Kohavi, Ron, Diane Tang, and Ya Xu. 2020. Trustworthy Online Controlled Experiments: A Practical Guide to a/b Testing. Cambridge University Press.
Lohr, Sharon L. 2019. Sampling: Design and Analysis. CRC Press.
McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.
———. 2019a. Advanced r. CRC Press.
Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science.