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Data Science from Scratch: First Principles with Python Paperback – 1 May 2015

by Joel Grus (Author)

253 ratings

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

Book features :

Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability―and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

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.

Print length

330 pages
Language

English
Publisher

O'Reilly Media, Inc, USA
Publication date

1 May 2015
Dimensions

17.8 x 1.75 x 23.3 cm
ISBN-10

149190142X
ISBN-13

978-1491901427
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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
Pages	414	330	408	250	212	500

Product description

About the Author

Joel Grus is Chief Scientist and Head of Analytics at VoloMetrix, an enterprise analytics startup in Seattle. He has been doing data science since before it was called that, at a handful of Seattle startups before this one, and at Bing when one of those startups got bought by Microsoft. He's been using Python for almost 10 years. Joel is active in the Seattle data science community and maintains a blog at joelgrus.com

Product details

Publisher ‏ : ‎ O'Reilly Media, Inc, USA (1 May 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 330 pages
ISBN-10 ‏ : ‎ 149190142X
ISBN-13 ‏ : ‎ 978-1491901427
Dimensions ‏ : ‎ 17.8 x 1.75 x 23.3 cm

Best Sellers Rank: 226,172 in Books (See Top 100 in Books)
- 56 in Computer Programming Structured Design
- 219 in Data Mining
- 242 in Data Modelling & Design

Customer Reviews:
253 ratings

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5 star		61%
4 star		18%
3 star		11%
2 star		7%
1 star		3%

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jbot

A good book for learning something about Python and data science

Reviewed in the United Kingdom on 15 October 2015

Verified Purchase

This is a good book. I didn't know any Python or data science before I started and now I'm about two-thirds through the book and can say that I know some about both...enough to take on various side automation or data analysis projects with Python. The examples are interesting, illustrative and fun to work through.

In terms of level of maths, the book is about building your own algorithms rather than using ones in a library, so while you'll be able to get through the book if you are not that good at maths, to get the most out of it you have to be comfortable with probability/statistics and some calculus ideas, or willing to put the time in to get there, but not really anything too intense. In terms of level of programming, I am probably considered a noob by people who consider themselves programmers, but not a noob by people who consider themselves non-computer scientists, and it was perfect for me.

20 people found this helpful

Peadar Coyle

A worth addition to any data scientists reading list

Reviewed in the United Kingdom on 22 May 2015

Verified Purchase

Clearly written book in a genre that has plenty of alternatives.
* Excellent introduction to data structures throughout
* Useful practical considerations of the underlying algorithms
* Functional programming style which is unusual in this genre
* Lively style by Joel

12 people found this helpful

Henry

Quite a useful intro, but insufficient depth to go beyond that

Reviewed in the United Kingdom on 27 December 2015

Verified Purchase

This is a great overview book which assumes little. Few things are covered in sufficient depth to make a massive impact, but to give you a general overview of techniques and ideas, it serves the purpose. Good way to learn Python and get some foundations in the field of data science, but only barely skims the surface in my opinion.

3 people found this helpful

Amazon Customer

The book is printed in greyscale - Online version is in colour

Reviewed in the United Kingdom on 12 July 2016

Verified Purchase

Originally I seen an online version of this book and the main thing I liked about it was that the code was is colour and therefore much easier to read. However, when I bought the printed version, that was in greyscale. It might not be important to everyone but I probably would not have bought this if I had known before hand. The content itself is very helpful.

16 people found this helpful

John Ratcliffe

Great intro

Reviewed in the United Kingdom on 25 July 2021

Verified Purchase

I had some understanding of concepts and techniques before reading this book. It collects all of these in one place with clear explanations and examples. This book has done a very good job of reinforcing my views and building my confidence. It confirmed my recent decision to stick with Python. (I think Python works best for programmers.) I will now put what I have learned into my mission to finish in the top 1% of Fantasy Premier League.

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