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Product details
File Size: 9997 KB
Print Length: 330 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 1 edition (April 14, 2015)
Publication Date: April 14, 2015
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00W4DTP2A
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Amazon Best Sellers Rank:
#39,480 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
Good introduction to data analysis.The clear syntax grammar of Python helps a lot to clarify the meaning of author.
In my view, too many people want to be data scientist and use advance statistical techniques that they don't fully understand. I believe Grus closes this gap with this fun introductory book to some of the basic techniques in data analysis. If you don't come from a statistical background (MS/PhD), then you should start with this book and move on to other texts such as Pattern Recognition and Neural Networks by Brian D. Ripley for a thorough review of classification methods, or the more popular and modern The Elements of Statistical Learning By Hastie, Tibshirani and Friedman.
I thoroughly enjoyed this book, one of my favorite books ever on programming. It does three things superbly: covers the basic low level tools of a data scientist (the "from scratch" part), gives a great overview of useful Python programming examples for those new to Python, and gives an amazingly succinct yet high level overview of the mathematics and statistics required for data science.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.
Started programming Python immediately with this book. Lots of concepts for one book. I like that that there are enough details to write a functional program for each chapter and not too many details. It's important to find a proper material to start, and this book, in my opinion, does it perfectly.
This is a great book-- well written, easy to digest and informative. I've been in Data Mining and Statistical Analysis for a little over a decade now; I was looking for a book to share with my team to ensure we were all up-to-speed on some foundational concepts: this book is it. EDIT: I also forgot to mention, it has probably the best get-up-and-running in Python introduction I've seen (see, e.g., Chapter 2, ~20pp.)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.
Minus one star for using outdated Python 2.7. Essentially ALL data science tools you are likely to run across have been updated to Python 3.4+. I would have knocked off two stars but this book is actually quite good and delivers on its title.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.
You will construct functions that calculate the variance, standard deviation etc. up to constructing more advanced machine learning functions, from scratch.What is pretty underrated about this book is the very clever/clean (and challenging) way of constructing code and solving problems. Things can get very ....nested. Very valuable resource that I come back to again and again.
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