Link to Content:

O'Reilly

Created/Published/Taught by:

O'Reilly

Joel Grus

Content Found Via:

Amazon

Free? No

Cost Range:

$18.99 - $39.99

Tags: API / bayes theorem / clustering / data visualization / decision trees / gradient descent / hypothesis testing / k nearest neighbors (k-NN) / linear algebra / linear regression / list comprehensions / logistic regression / machine learning / mapreduce / matplotlib / multiple regression / naive bayes / natural language processing (NLP) / network analysis / neural networks / probability / python / recommender systems / SQL / statistics

Difficulty Rating:

From Amazon:

“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, MapReduce, and databases”

Recommended Prerequisites: aptitude for mathematics and some programming skills

Go to Content: Data Science from Scratch: First Principles with Python

## By magsol October 21, 2015 - 3:23 pm

This book is an excellent introduction to the basics of statistics, machine learning, and data science through the lens of the Python programming language. Plenty of code examples are provided, but the algorithms are implemented in as bare-bones Python as possible to expose the reader to the whole step-by-step of implementing each technique. The first chapter is a crash course in the basics of Python, followed by a primer on all the “top-level” concepts in data science: data visualization (feedback); linear algebra, statistics, and probability (nuts and bolts of data science); and finally data ingestion, before then taking the reader through all the major areas of data analytics (e.g. optimization, classification, clustering, recommendation, etc).

The only strike against this book is that its author uses Python 2.7; this version is increasingly being considered “legacy” Python, in favor of Python 3+. All major packages used in the book are perfectly functional in Python 3, but given the lack of backwards-compatibility between the two major versions I would strongly advise against using Python 2.7 at this point. Other than that, it’s a fantastic introduction to data science.

Early Beginner- new to data science / totally new to this topic / few prerequisitesAdvanced - had practical experience with this topic beyond intermediate level / could teach others this or related topics

(2) A Little

Detailed Content Survey:Wanted to learn just for fun / Looked interesting

This is one of my primary go-to content sources

Yes

Most (i.e. read the entire book but didn't complete the exercises)

Any general knowledge in programming, statistics, and linear algebra is certainly helpful, but this book does a very good job of starting from a very low level.

Python

Somehow managed to avoid using NumPy through the whole book!

Beginners or instructors teaching beginner courses

## By jacob February 5, 2016 - 1:57 pm

Excellent book that takes you through a bunch of core Data Science techniques from the ground up using Python giving not only a great overview of many of the mainstays of the field but also offer the user the chance to build an understanding that you just won’t get from plugging data into a pre made library.

The python throughout the book is beautifully written and extremely idiomatic and the crash course at the beginning of the book is worth the price of admission alone.

My only slight complaint is the black and white printing throughout (unlike some of the other O’Reilly DS books) – colour highlighted syntax would have made the code easier to read.

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesAdvanced Beginner - was new to this topic / had some prerequisites

(3) A Good Amount

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