And now, once again, I bid my hideous progeny go forth and prosper.
Mary Shelley
Where do you go from here?
Assuming I haven’t scared you off of data science,
there are a number of things you should learn next.
IPython
We mentioned IPython earlier in the book.
It provides a shell with far
more functionality than the standard Python shell, and it
adds “magic functions” that allow you to
(among other things)
easily copy and paste code
(which is normally complicated by the combination of blank lines and whitespace formatting)
and run scripts from within the shell.
Mastering IPython will make your life far easier.
(Even learning just a little bit of IPython will make your life a lot easier.)
Additionally, it allows you to create “notebooks”
combining text, live Python code, and visualizations
that you can share with other people, or just keep around
as a journal of what you did (Figure 25-1).
Figure 25-1. An IPython notebook
Mathematics
Throughout this book, we dabbled in linear algebra (Chapter 4), statistics (Chapter 5), probability (Chapter 6), and various aspects of machine learning.
To be a good data scientist, you should know much more about these topics, and I encourage you to give each of them a more in-depth study, using the textbooks recommended at the end of the chapters, your own preferred textbooks, online courses, or even ...
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