Foundations for Analytics with Python

Chapter 9. Where to Go from Here

Well, we’ve come to the final chapter in the book. We’ve covered a lot of material up to this point. We’ve covered Python basics and how to parse any number of text files, CSV files, Excel files, and data in databases. We’ve learned how to select specific rows and columns from these data sources, how to aggregate and calculate basic statistics using the data, and how to write the results to output files. We’ve tackled three common business analysis applications that require us to use the skills and techniques we’ve learned in creative and useful ways. We’ve also learned how to create some of the most common statistical plots with several add-in packages and how to estimate regression and classification models with the StatsModels package. Finally, we’ve learned how to schedule our scripts to run automatically on a regular basis so we have time to work on other interesting analytical problems. If you’ve followed along with and carried out all of the examples in this book, then I hope you feel like you’ve transitioned from non-programmer to competent hacker.

At this point, you might be wondering where you go from here. That is, what else is there to learn about using Python to scale and automate data analysis? In this chapter, I’ll mention some additional capabilities of the standard Python distribution that are interesting and useful but weren’t necessary for you to learn at the very beginning. Having gone through the preceding chapters in this ...