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Joel Grus

Data Science from Scratch: First Principles with Python 2nd Edition

by Joel Grus (Author)

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To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these 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 the hacking skills you need to get started as a data scientist. Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and 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, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.

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ISBN-10

1492041130
ISBN-13

978-1492041139
Edition

2nd
Publisher

O'Reilly Media
Publication date

May 16, 2019
Language

English
Dimensions

6.9 x 0.9 x 9.1 inches
Print length

406 pages
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O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Editorial Reviews

About the Author

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours.

Product details

Publisher ‏ : ‎ O'Reilly Media; 2nd edition (May 16, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 406 pages
ISBN-10 ‏ : ‎ 1492041130
ISBN-13 ‏ : ‎ 978-1492041139
Item Weight ‏ : ‎ 1.4 pounds
Dimensions ‏ : ‎ 6.9 x 0.9 x 9.1 inches

Best Sellers Rank: #28,350 in Books (See Top 100 in Books)
- #15 in Data Processing
- #17 in Data Mining (Books)
- #33 in Python Programming

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Joel Grus

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Joel Grus is Principal Engineer at Capital Group, where he leads a small team that designs and implements machine learning and data products. Before that he was a software engineer at the Allen Institute for AI and Google, and a data scientist at a variety of startups.

He's the author of the the beloved "Data Science from Scratch", the quirky "Ten Essays on Fizz Buzz", and the polarizing JupyterCon talk "I Don't Like Notebooks".

He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com.

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4.4 out of 5

617 global ratings

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Alexander S.

Excelente

Reviewed in the United States 🇺🇸 on December 2, 2022

Excelente libro

Useful but not extraordinary

Reviewed in the United States 🇺🇸 on December 22, 2022

Verified Purchase

Ambivalent about this one. On one hand the idea of implementing major ml and data science algorithms bottoms up, only using the base library in Python, is great as you can get a deeper understanding. From this point of view the book is worth reading. However the theory is quite rushed, the mathematics could have been described separately in formulas, not only code, and it lacks any graphical illustrations that would help you to visually understand. Lazy from this point of view. Overall worth reading but do not expect miracles.

One person found this helpful

Helpful

Gerardo Mazzei

Amazing introduction to Data Science

Reviewed in the United States 🇺🇸 on May 15, 2020

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Let me start this review by explaining clearly who this book is for: anyone who has had some form of introduction (even if concise) to programming in Python, algebra, statistics, and probability will find this book a great introduction to Data Science. While the author does a great job at having a crash course on these topics (and I even learned a thing or two here and there), I can see the contents being a bit overwhelming if this is your first point of contact with these subjects. However, should you meet the requirements I mentioned above, you'll find this book a breeze! Joel does a good job at explaining the topics using his signature brand of humor, keeping the read entertaining even in the most advanced areas. I'd even say that this is a must read if you are considering going into machine learning, since it teaches you a thing or two in the topic as well. Please keep in mind that the book is monochrome. If that bothers you, consider viewing the electronic version.

TLDR: If you're looking for a concise introduction to data science and have a bit of knowledge of basic Python, algebra, statistics and probability, look no further than this book! Otherwise, come back once you've picked up those tools and you'll feel right at home :)

20 people found this helpful

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Alejandro Espinoza

Great book about the how of Data Science, but not the why.

Reviewed in the United States 🇺🇸 on June 30, 2020

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In my personal opinion, this is a book to bridge the gap between an experienced mathematician/statician and practical machine learning. The book is more focused on describing how to implement mathematical formulas in Python than to actually explain the math behind it.

I am a proficient Python Engineer and I can read the code and understand what is being done, but the author makes no effort to explain how it reached to that conclusion, or why it matters. The author does implement the mathematical formulas in Python skillfully, it misses the point of the book though.

Another big problem with this book is that it assumes you can learn mathematics by just doing mathematics without understanding the why. It is frustrating to read and follow the author implement mathematical formulas without explaining why. I believe this is the case because the author DOES assume you have the required math to follow the book. I believe the author should add a section in the preface that list the prerequisites for this book:
- Linear Algebra
- Statistics
- Probability
- Vector Calculus
- Continuous Optimization

Above all, it is a good book if used as an index on where to start to understand Data Science, but it definitely doesn't fulfill the promise of being "from scratch". From scratch IMHO means you dive into the internals of Data Science algorithms. I had the expectation that this book was going to be more like "Designing Data-Intensive Applications" for Data Science where the "why" is as important as the "how". Data Science from scratch is a book about the how, with no effort to dive into the why. The book does provide the vocabulary for me to discuss Data Science with practitioners, but I didn't feel it got me any closer to becoming a practitioner myself.

BTW, the fact that book is monochrome doesn't matter the font and figures are very clear and readable.

55 people found this helpful

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Michael Covelli

Great book, really means from scratch

Reviewed in the United States 🇺🇸 on July 28, 2021

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This is a great book. Doing everything from scratch and not just using numpy, sklearn, etc is a great way to learn what's really going on underneath. I'm surprised how far he gets along this path. By the end, you will have implemented a keras-like deep learning setup. It won't be fast enough for production use since it's all using Lists underneath, but you'll be able to see how it all fits together. Also, coming from a more typed language background, I loved the type annotations.

5 people found this helpful

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McKay Montz

I really like this book, but this isnt a one stop shop.

Reviewed in the United States 🇺🇸 on December 21, 2021

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I have decided to go into data science from software engineering. I bough this book to get a "from beginners" guide to becoming a data scientist. I learn better from books than online.

I really like the author, and the way he writes, sometimes its funny, but also its very straight to the point and dry when it comes to a "quick overview" on concepts and things you need to know, and I quickly realized that I need to do some "back learning" on a lot of mathematics. So I did have to stop reading this book and purchase some other books so I can understand what this author was talking about.

Though I had to buy some other books to help be understand this book, I still enjoy this book and think once I have a better foundation in algebra and calculus, I can continue to read this book again. Also you need to have a expert level understanding of python, which I dont either, but baby steps.

I would say this book is great guidline to what you need to know, but it doesnt teach you enjoy from "scratch" like the title says, you still need to put in the work.

3 people found this helpful

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Amazon Customer

Very very good book!

Reviewed in the United States 🇺🇸 on March 17, 2020

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This book is suitable for people with basic python programming skills. It is very good for beginners and advanced users alike. The codes are very clear and without errors. This book teaches you the basics and introduce some expert level topics for you to explore further if keen. If you are a novice data analyst and some harder topics throw you off, you should probably revisit the topics after you have gain more knowledge on data science.

I highly recommend this book as your first book into data science because the codes and thought processes are very clear. 70-80% of the book are data science foundation and basics for you to tackle harder topics later.

3 people found this helpful

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PatrickK

Outstanding

Reviewed in the United States 🇺🇸 on March 18, 2021

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I set out to find a book that would give me a good overview of Data Science and this book was perfect. It described the methods, code, and math in an accessible way. It spent 5-10 pages per technique (and included a primer on python and stats). Great book!

2 people found this helpful

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Top reviews from other countries

Martin

The Paperback is black & white and very poor quality

Reviewed in the United Kingdom 🇬🇧 on August 25, 2020

Verified Purchase

The paperback unfortunately was not printed in colour, only a low resolution black & white, which I was very disappointed with for an O'Reilly book. Colour conveys important information in a book like this.
Without colour coding in the graphs, and with syntax highlighting missing for the code segments, it makes the book very difficult to read.

I'm sure the book itself is great, and I'm looking forward to reading the ePub instead.

17 people found this helpful

El Chubinebrae

Good book, bit pricey though.

Reviewed in the United Kingdom 🇬🇧 on October 15, 2020

Verified Purchase

This is a good book. It's been updated for Python 3 so you won't miss out on any of the new features.

It's definitely not for complete beginners. If you have a foundational knowledge of python then you'll understand some of the concepts outlined. It's also not a tutorial book either, the best way to use this book is to find a part of it and apply it to a data set.

5 people found this helpful

MR S S DADWAL

Misreported

Reviewed in the United Kingdom 🇬🇧 on January 25, 2021

Verified Purchase

This book is actually 380 pages long, not 500 as reported

7 people found this helpful

Velvetytoast

Very good ground up approach to the subject

Reviewed in the United Kingdom 🇬🇧 on January 25, 2020

Verified Purchase

It’s definitely from the ground up - I found it useful to revisit the maths as well as seeing the code - well with the price of the book

3 people found this helpful

Guillermo Martinez Dibene

I didn't like it, but you may

Reviewed in Canada 🇨🇦 on October 15, 2020

Verified Purchase

As the title suggests, this is a "from scratch" approach to Data Science. This can be both good and bad. It is good because the author teaches you how to implement many Machine learning algorithms from scratch, starting by defining vectors and linear algebra objects in the first few chapters. The bad bit of the book is that the author often takes the "from scratch" too literally, for example when reading a .CSV he doesn't use the pandas library or the CSV built-in module. It is also bad because despite being a "from scratch" approach, he doesn't do any of the math. He gives an intuitive explanation as to what the algorithm is, implements it, and then applies it to some famous data set, but you have to know in advance what the math or Statistics behind the algorithm is and how it works (I was hoping the from scratch would mean understanding the statistics part of the methods).

15 people found this helpful

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