---
product_id: 294483010
title: "O'Reilly Doing Data Science"
price: "11065F"
currency: XPF
in_stock: true
reviews_count: 5
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---

# R Code Examples Included Comprehensive Data Science Topics Based on Columbia University Course O'Reilly Doing Data Science

**Price:** 11065F
**Availability:** ✅ In Stock

## Summary

> 📈 Unlock the data-driven future with insights from the pros!

## Quick Answers

- **What is this?** O'Reilly Doing Data Science
- **How much does it cost?** 11065F with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [frenchpolynesia.desertcart.com](https://frenchpolynesia.desertcart.com/products/294483010-oreilly-doing-data-science)

## Best For

- Customers looking for quality international products

## Why This Product

- Free international shipping included
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## Key Features

- • **Practical R Coding:** Includes commented R code snippets to sharpen your hands-on skills.
- • **Strategic Data Insights:** Covers algorithms, financial modeling, recommendation engines, and data visualization.
- • **Interdisciplinary Mastery:** Bridges statistics, programming, and communication for a holistic data science foundation.
- • **Trusted Academic Resource:** Endorsed by experts and perfect for beginners with math and programming background.
- • **Industry-Backed Curriculum:** Learn from real-world case studies by data scientists at Google, Microsoft, and eBay.

## Overview

O'Reilly's Doing Data Science is a well-regarded, interdisciplinary introduction to data science based on Columbia University's course. It offers practical R code examples, real-world case studies from top tech companies, and covers essential topics like statistical inference, algorithms, and data engineering. Ideal for professionals with some math and programming experience, this book bridges theory and practice to accelerate your data science journey.

## Description

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype? This insightful book, based on Columbia University's Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O'Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Review: Beautiful with excellent references but the code needs work - This is a beautiful, thoughtful survey with excellent references. I am an academic data scientist with nearly 20 years experience and I wanted a book to offer my students who are starting in the field. This is it. The "difficulty" with data science is in the breadth of skills that are needed. Because data scientists need training in art, communication, statistics, and programming nobody is prepared to handle all the tasks and the neophyte (and expert) will need to fill in around their weaknesses. This book does a brilliant job of working around that issue. The writing is superb for a beginning to intermediate reader and the graphics and aside boxes are engaging. More importantly. the references are plentiful and spot on. In the areas I know well the authors suggest the things I recommend and where I am weak the recommendations have proven interesting. While this is a broad survey, there is some depth here. There are formulas throughout but the book does not get bogged down in proofs and derivations. There are programs written in R code scattered throughout. The code is nicely commented but there is not a deep dive into how it words. So, the reader who knows some R will learn a few new tricks but it does not interrupt the flow of the book. A reader who types the R code will run into problems. Clearly the authors/editors did not attempt to run the code after the type setter mangled it. For example, on page 39 there is a line which begins with a + and that character needed to be on the previous line. In other places, (like page 49) functions are invoked (count) but the authors have not included the commands to make the functions available (in this case library(plyr)). Sadly there does not seem to be an errata for the book and these will be major headaches or show stoppers for novices. While this book could be improved with a code supplement on the web (including the code to make all the graphics, complete solutions to the example/problems and an errata), this is a wonderful buy for readers of all levels.
Review: A good starter book for Data Science especially if you have some Math background - I have read the book 'Data Science from Scratch' (O'Reilly) by Joel Grus twice as my first proper data science book twice before reading this book twice from cover to cover. I would say that there are some overlaps between the books and I would say that the presentation of this book has a more anecdotal feel and there is no lack of in-depth mathematics in this book. Comparing 'Data Science from Scratch' with this book, 'Data Science from Scratch' has more comprehensible mathematics and more abundant in terms of the total lines of codes involved and the scope of data science covered. This book mainly uses R and there are knowledge found in this book that are absent in 'Data Science from Scratch', vice versa. I would recommend that a reader reads both 'Data Science from Scratch' and this book, reading 'Data Science from Scratch' first, understanding it especially the elucidation and explanations of Data Science concepts before reading this book. Upon reaching a certain level of understanding of both books would certainly help in an individual's understanding and proficiency of data science.

## Features

- Used Book in Good Condition

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #235,029 in Books ( See Top 100 in Books ) #135 in Programming Algorithms #439 in Databases & Big Data #628 in Computer Software |
| Customer Reviews | 4.5 out of 5 stars 135 Reviews |

## Images

![O'Reilly Doing Data Science - Image 1](https://m.media-amazon.com/images/I/81QtFnzXJsL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Beautiful with excellent references but the code needs work
*by I***G on 7 March 2014*

This is a beautiful, thoughtful survey with excellent references. I am an academic data scientist with nearly 20 years experience and I wanted a book to offer my students who are starting in the field. This is it. The "difficulty" with data science is in the breadth of skills that are needed. Because data scientists need training in art, communication, statistics, and programming nobody is prepared to handle all the tasks and the neophyte (and expert) will need to fill in around their weaknesses. This book does a brilliant job of working around that issue. The writing is superb for a beginning to intermediate reader and the graphics and aside boxes are engaging. More importantly. the references are plentiful and spot on. In the areas I know well the authors suggest the things I recommend and where I am weak the recommendations have proven interesting. While this is a broad survey, there is some depth here. There are formulas throughout but the book does not get bogged down in proofs and derivations. There are programs written in R code scattered throughout. The code is nicely commented but there is not a deep dive into how it words. So, the reader who knows some R will learn a few new tricks but it does not interrupt the flow of the book. A reader who types the R code will run into problems. Clearly the authors/editors did not attempt to run the code after the type setter mangled it. For example, on page 39 there is a line which begins with a + and that character needed to be on the previous line. In other places, (like page 49) functions are invoked (count) but the authors have not included the commands to make the functions available (in this case library(plyr)). Sadly there does not seem to be an errata for the book and these will be major headaches or show stoppers for novices. While this book could be improved with a code supplement on the web (including the code to make all the graphics, complete solutions to the example/problems and an errata), this is a wonderful buy for readers of all levels.

### ⭐⭐⭐⭐⭐ A good starter book for Data Science especially if you have some Math background
*by E***L on 22 April 2021*

I have read the book 'Data Science from Scratch' (O'Reilly) by Joel Grus twice as my first proper data science book twice before reading this book twice from cover to cover. I would say that there are some overlaps between the books and I would say that the presentation of this book has a more anecdotal feel and there is no lack of in-depth mathematics in this book. Comparing 'Data Science from Scratch' with this book, 'Data Science from Scratch' has more comprehensible mathematics and more abundant in terms of the total lines of codes involved and the scope of data science covered. This book mainly uses R and there are knowledge found in this book that are absent in 'Data Science from Scratch', vice versa. I would recommend that a reader reads both 'Data Science from Scratch' and this book, reading 'Data Science from Scratch' first, understanding it especially the elucidation and explanations of Data Science concepts before reading this book. Upon reaching a certain level of understanding of both books would certainly help in an individual's understanding and proficiency of data science.

### ⭐⭐⭐⭐⭐ The Only Book You'll Need
*by M***M on 22 December 2020*

There are many books out there on Data Science, Machine Learning and Data Analytics. I can think of none that understand the subject so well they make it easy for the reader to understand. Einstein once said if you can't explain something in simple terms you don't really understand it. Well O'Neil and Schutt pass the Einstein test. Other books I've read on the subject are either written by a computer scientist who is light on maths and so they leave the maths opaque. Or they are a mathematicians who are strong on stats and light on programming. O'Neil and Schutt are strong in both and as the title would suggest its about doing Data Science rather than the academic theory of Data Science and Machine Learning. Another thing I love about this book is it debunks the jargon. Very refreshing. Whoever you are at whatever level this is the last word in Data Science. I would consider it perfect if it included code for Python too. It doesn't it's R and the R code requires updating since publication but that's easily done if you are interested. Can't recommend this book more highly. The essential read for the subject.

## Frequently Bought Together

- Doing Data Science: Straight Talk from the Frontline
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- Data Science from Scratch: First Principles with Python

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*Last updated: 2026-08-26*