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Data Science from Scratch: First Principles with Python 1st Edition
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From the Publisher
|Data Science for Business||Data Science from Scratch||Doing Data Science||R for Data Science||Data Science at the Command Line||Python Data Science Handbook|
|What You Need to Know about Data Mining and Data-Analytic Thinking||First Principles with Python||Straight Talk from the Frontline||Visualize, Model, Transform, Tidy, and Import Data||Facing the Future with Time-Tested Tools||Tools and Techniques for Developers|
About the Author
Joel Grus is a software engineer at Google. Before that he worked as a data scientist at multiple startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com.
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Top Customer Reviews
It's the right size and correct coverage for the content and the author's sense of humor (indeed, that of a data scientist) resonates with the audience.
Solid introduction, even better review or brief explanation of commonly encountered topics.
One of the best O'Reilly books I've read in a long time-- in fact, a technical book at the level I used to expect from O'Reilly.
As the "from Scratch" in the title implies, the objective of this book is to teach the fundamental ideas and techniques of data science from first (or nearly first) principles. After working through this book, you'll be better able to meaningfully utilize the pre-packaged software (whether it's Matlab, R, scikit-learn, or whatever) that you will use in "real life".
And although the knowledge you'll gain is largely independent of the programming language, you will as a bonus learn from the clear and elegant python code included. Every key topic, from probability, statistics, and other mathematical subjects, to machine learning and databases, is covered in a crystal clear manner.
In summary, this book is the bee's knees.
Either the author or else the editor makes it too difficult to follow the mathematical explanations because it isn't explicitly specified when an algebraic identity is being used that was established earlier. So you have to take the time to get pencil and paper out yourself and work through the algebraic manipulations before anything makes sense, searching for "how did they get to _there_ from _here_?"
The book lacks some compelling examples of how/why one can and should use a technique that has been addressed.
It does provide you with the basics of how the analysis is done and the math behind a bunch of machine learning models. Unfortunately there's definitely a high expectation of math knowledge in this book, starting with at least Linear Algebra through Calculus.
So, while I think this book had value, it was very different from what I was expecting. I think the target audience for this book is more an academic looking to apply their knowledge to a data science realm, and not so much a programmer wanting to learn how to actually implement machine learning or data analysis in code.