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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.
Top customer reviews
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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.
At first I was very worried about this book based on the first few chapters for the one reason that the author was cracking jokes throughout the text and I thought if it kept up for the rest of the book I was going to be very upset. But it did not happen and it turns out to have been a very reasonable way to ease into this complicated subject.
The author steps through the toolbox of the data scientist, chapter by chapter, giving useful, insightful, clear pieces of code and textual explanations of each topic. So, for those new to data science it gives just enough to get the basic idea of a concept in terms of code and mathematical explanation, and then moves on to the next topic.
It is often said that in writing, less is better and this book gets things down to their essence. That is one of the great things about the book - that the length of each chapter is about 20 pages (over 25 chapters). So each chapter can be read and the code even exercised in about an hour. Further, the references at the end of each chapter invite the reader to expanded information at the level of one or more entire textbooks or references. Thus the book can be seen as kind of boiling down a 25-volume set of highly technical subject matter into roughly 300 pages.
The topics that were explored the best seem to be the ones on probability, working with data, regression, clustering, and databases (SQL). Some of the small but dense code samples were tough to follow but that is based on their algorithmic complexity - such as that for logistical regression and MapReduce. Occasionally the author uses a term that is not defined or in the index (such as data munging - which I still haven't looked up to see what it means). There are only a small number of typos which indicates good editing. While the Python crash course was pretty good, Python is a vast language and there could have been more to that section.
I read this book from cover to cover and stepped through logically all the code (but did not actually run any of it) and I would wholeheartedly recommend this book for anyone wanting to work in the area of data science or its related fields, such as big data engineering or data analysis.
As a bonus, Joel has a very entertaining sense of humor and writes some seriously elegant Python code. I learned as much about coding as anything else. I swear I felt like I was in the movie Inception while trying to unpack some of his amazingly efficient list comprehensions.
I'll be returning to this book again and again. Great job Joel!
This is a very basic book on Data Science but it gives a broad overview which helps you get a perspective on the tools that are available. This book teaches methods by developing actual code for these methods. You will find in work situations that you will use library functions instead of "rolling your own" but this book helps bring the details together by having you actually code these techniques. I support this approach 100% Once you have this overview, you can drill down into specifics with other materials like textbooks or cookbooks.
I'd did flinch at some of the explanations in this book but it really is a "from Scratch" approach and some things are simplified to avoid distractions.
This book also teaches basic Python 2.7 with a quick start chapter, so it is self contained for any scientist or engineer that wants to get started adding Data Science techniques to their repertoire.