Data Science from Scratch – The book for getting started on Data Science

data science from scratch first principles with python

Review of Data Science from Scratch

The following is a review of the book Data Science from Scratch: First Principles with Python by Joel Grus.

Data Science from scratch is one of the top books out there for getting started with Data Science. It’s second edition has recently been published, upgrading and improving the content of the first one. Lets see what this awesome book offers.

We will begin by clarifying who this book is for: it is for people with at least some sort of programming knowledge in Python, and some basic algebra and statistics knowledge. If you are not there yet, take a look at our awesome recommended Python Programming books, and our Statistics and probability courses. If you meet the above pre-requisites, and want to learn Data Science from a basic level then you will love this book.

The author does a great job explaining the topics and introducing little pinches of humor every now and then to keep the reading entertaining, even in the most complex topics. If you are considering going into Machine Learning and Data Science, this book is a great first step.

It will teach you the most fundamental data science concepts, encouraging you to write simple functions of code so that you really learn what is happening under the hood of the most popular Python Data Science libraries like Numpy, Pandas and Scikit Learn.

In our opinion this a great way of learning, as it gives you a through practical understanding of how these libraries work instead of just teaching you to use the most common functions. You will have a very strong foundation, that will allow you to learn easier and progress with more confidence than what you can adquiere from other resources.

It is a really pedagogical book, and a great read to develop an strong understanding of the fundamental concepts necessary to understand data science and machine learning.

Book Description and Contents

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.

You can find the full contents of the new edition here. Also, the 1st edition (outdated) is freely available on PDF on the following link. Lastly, you can find a video review of the previous edition

About the book

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. He blogs infrequently at and tweets all day long at @joelgrus.

Pages: 406 pages

Publication: First publication 2015, Second Publication 2019

Data Science from Scratch second edition pdf

Summary of Data Science from Scratch

Data Science from Scratch is one of the best books for those that have a little programming knowledge, feel conformable with statistics, and want to get introduced in a swift and painless manner to the Data Science world.

In our opinion (and the opinion of many other experts too) this book is one of the best resources for beginners, as its teaching style is based on understanding and not just doing, which turns out to having great benefits in the posterior learning paths and careers of its readers.

 As for the actual content of this book, it is fascinating and useful. 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! You can Data Science from Scratch on Amazon here:

Data Science from Scratch: First Principles with Python
  • Grus, Joel (Author)
  • English (Publication Language)
  • 403 Pages - 06/11/2019 (Publication Date) - O'Reilly Media (Publisher)

For more reviews like this one, check out our Machine Learning books category!

Thank you very much for reading How to Learn Machine Learning and have an awesome day!

Tags: Data Science, Machine Learning, Data Science for Beginners, Machine Learning for beginners.

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