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Data Science from Scratch, 2nd Edition by Joel Grus

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Chapter 27. Go Forth and Do Data Science

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

I 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.)

Note

In the first edition, I also recommended that you learn about the IPython (now Jupyter) Notebook, a computational environment that allows you to combine text, live Python code, and visualizations.

I’ve since become a notebook skeptic, as I find that they confuse beginners and encourage bad coding practices. (I have many other reasons too.) You will surely receive plenty of encouragement to use them from people who aren’t me, so just remember that I’m the dissenting voice.

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 ...

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