Data Science from Scratch: First Principles with Python 1st Edition

by Joel Grus (Author)

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ISBN-13: 978-1491901427

ISBN-10: 149190142X

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Data Science from Scratch: First Principles with Python
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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.

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, Map Reduce, and databases.

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Editorial Reviews

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.

Product details

Publisher ‏ : ‎ O'Reilly Media; 1st edition (April 30, 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 330 pages
ISBN-10 ‏ : ‎ 149190142X
ISBN-13 ‏ : ‎ 978-1491901427
Item Weight ‏ : ‎ 1.32 pounds
Dimensions ‏ : ‎ 7.01 x 0.69 x 9.17 inches

Best Sellers Rank: #388,480 in Books (See Top 100 in Books)
- #76 in Computer Programming Structured Design
- #129 in Enterprise Data Computing
- #245 in Data Modeling & Design (Books)

Customer Reviews:
288 ratings

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5 star		63%
4 star		17%
3 star		10%
2 star		7%
1 star		2%

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Umair

It says alot, but teaches little.

Reviewed in the United States on March 6, 2019

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+1 for concise and to the point.
+1 for relevant topics.
-1 for lack of real life library usage.
-1 for lack of real world data.
-1 NaN
^ 2/5 stars.

I don't normally leave reviews. I buy books, read them, and put them away. I decided to make an exception for this one.

Before I go any further I'll give the benefit of the doubt that perhaps I had a misunderstanding of what this book was about. I am a data scientist who works in cyber security. I have been through a good amount of data science boot camps, books, and online training. They all have one thing in common. For being about "data" they all have pretty useless and unrealistic examples. This book is no exception. (I laughed at a part where the author literally says something similar to "real life data is usually very messy" and continues to not use real data).

I will start out with the good parts about this book. It gives you a good crash course into Python and pretty much every critical data science concept. It is concise and filled with code examples written from scratch with little to no libraries being used (which is also a bad thing, I will explain shortly). The flow of the book is well designed as well.

Now for the bad.
1. Pure Python is great, but should be kept at a minimum. Sure, it gives you a good understanding of how to implement a concept in pure Python, but that is not the industry standard whatsoever. There should have been a healthy amount of real world implementations to offset the typical college classroom feel to the book. It also tends to add too much filler to the content. It would have been much better if it was "read resource X to see how to implement this in pure Python" and not "read book X to see how to use a real world library to do this."

2. The data in this book is like all the data everyone uses in their examples. Completely useless. Randomly generated numbers, endless usage of the "coin flip" probability examples, typical artificial data that, I promise you, nobody analyzes on a daily basis. The book starts off with you role playing as a new data scientist for a fictional social networking platform for Data Scientists. Which was very promising start and I was eager to see how this "character" would deal with the data problems they would face... Spoiler, barely ever spoke about it. Most examples are riddled with typical Statistic 101 and randomly generated data. Yet again, another disappointment on that end.

Maybe I misunderstood what this book was about. I could be wrong. That being said, I am now afraid to touch another "Data Science" book or online resource because I am sufficiently tired of reading about 300 ways to solve a problem with np.random generated arrays then turning to my screen with real world data and literally looking like the Persian Room Cat Guardian.

I am not sure if I recommend this or not. It is a good book in the sense that you learn about what Data Science "contains" but definitely not how it applies to the real world.

30 people found this helpful

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Public name

Reviewed in the United States on February 24, 2016

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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.

70 people found this helpful

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John.P.

Poor print quality

Reviewed in the United States on February 9, 2019

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The media could not be loaded.

+ Great overview of meathods and tools
+ well written I found the author funny, clear, and thorough
- wish it dove deeper into libraries
- some pages were blank other pages had printing errors. Dozens of pages throughout the text are completely blank and it was the sections I was most interested in reading.

10 people found this helpful

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Kelly Maurice

Outstanding Book Providing Exactly What The Title Says

Reviewed in the United States on June 9, 2016

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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.

21 people found this helpful

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Top reviews from other countries

jbot

A good book for learning something about Python and data science

Reviewed in the United Kingdom on October 15, 2015

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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 May 22, 2015

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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 December 27, 2015

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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 July 12, 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 July 25, 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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