Enjoy fast, free delivery, exclusive deals, and award-winning movies & TV shows with Prime
Try Prime
and start saving today with fast, free delivery
Amazon Prime includes:
Fast, FREE Delivery is available to Prime members. To join, select "Try Amazon Prime and start saving today with Fast, FREE Delivery" below the Add to Cart button.
Amazon Prime members enjoy:- Cardmembers earn 5% Back at Amazon.com with a Prime Credit Card.
- Unlimited Free Two-Day Delivery
- Streaming of thousands of movies and TV shows with limited ads on Prime Video.
- A Kindle book to borrow for free each month - with no due dates
- Listen to over 2 million songs and hundreds of playlists
- Unlimited photo storage with anywhere access
Important: Your credit card will NOT be charged when you start your free trial or if you cancel during the trial period. If you're happy with Amazon Prime, do nothing. At the end of the free trial, your membership will automatically upgrade to a monthly membership.
-33% $43.94$43.94
Ships from: Amazon.com Sold by: Amazon.com
$38.68$38.68
Ships from: Amazon Sold by: TEXbooks Plus
Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required.
Read instantly on your browser with Kindle for Web.
Using your mobile phone camera - scan the code below and download the Kindle app.
OK
Data Science from Scratch: First Principles with Python 2nd Edition
Purchase options and add-ons
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.
- ISBN-101492041130
- ISBN-13978-1492041139
- Edition2nd
- PublisherO'Reilly Media
- Publication dateJune 11, 2019
- LanguageEnglish
- Dimensions6.9 x 0.9 x 9.1 inches
- Print length403 pages
Frequently bought together
More items to explore
From the brand
-
-
Sharing the knowledge of experts
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 (June 11, 2019)
- Language : English
- Paperback : 403 pages
- ISBN-10 : 1492041130
- ISBN-13 : 978-1492041139
- Item Weight : 1.6 pounds
- Dimensions : 6.9 x 0.9 x 9.1 inches
- Best Sellers Rank: #24,847 in Books (See Top 100 in Books)
- #7 in Data Mining (Books)
- #10 in Data Processing
- #23 in Python Programming
- Customer Reviews:
About the author
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.
Customer reviews
Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.
To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.
Learn more how customers reviews work on AmazonReviews with images
-
Top reviews
Top reviews from the United States
There was a problem filtering reviews right now. Please try again later.
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 :)
Maybe you truly plan on entering data science as a field but don't know where to start?
Or perhaps you've seen one of the author's brilliant/hilarious talks about why he doesn't like Jupyter Notebooks or how to answer the infamous "FizzBuzz" programming interview question using Tensorflow neural networks (seriously, look up Joel Grus on YouTube).
If you know a little bit of Python, a little bit of relevant math, and want to go into any data science or machine learning path, then this book is a must-have. It certainly won't be the only resource you'll need, but it helps you get the most out of other content you'll likely look into later (like how to code up a machine learning pipeline, or maybe a large language model if you're really adventurous).
Far too many machine learning lessons out there just tell you to import certain Python libraries (scikit-learn for example) and start using them without giving you any basic understanding of how those imported functions even work to begin with. Even to this day there are still college courses and coding bootcamps that ask you to download a Jupyter Notebook file and just hit "Shift + Enter" and look at the output.
You're not going to learn how to code that way!!!
Joel Grus does an excellent job of filling in this gap by teaching you more Python than what a statistics professional would usually know and more math than what a typical software developer would know. And that's key if you want to go into a field that relies on both.
All the information for Python and math that you need to get started is here. It's 27 chapters that get you familiar with Python and how to use it, as well as the math used in data science and ML (linear algebra, probability and statistics, algorithms, etc).
You eventually learn enough of both as you go through the chapters to start applying what you learn for some real-world usage.
I've had this book for years and it's still as useful as when it first came out, but the only exception I've seen is that the Twitter API tutorial in the book no longer applies to the paid format that Twitter now uses to access that feature. The tutorial is still good for learning how API's get put to use.
Once you've read this book and have gotten familiar with all it has to offer, your next step will probably involve looking into a book about how to actually use pre-built data science libraries (like what you find in the Anaconda distribution of Python).
This book may turn out to be heavily responsible for my first startup, but that's a story for later.
Reviewed in the United States on April 17, 2023
Maybe you truly plan on entering data science as a field but don't know where to start?
Or perhaps you've seen one of the author's brilliant/hilarious talks about why he doesn't like Jupyter Notebooks or how to answer the infamous "FizzBuzz" programming interview question using Tensorflow neural networks (seriously, look up Joel Grus on YouTube).
If you know a little bit of Python, a little bit of relevant math, and want to go into any data science or machine learning path, then this book is a must-have. It certainly won't be the only resource you'll need, but it helps you get the most out of other content you'll likely look into later (like how to code up a machine learning pipeline, or maybe a large language model if you're really adventurous).
Far too many machine learning lessons out there just tell you to import certain Python libraries (scikit-learn for example) and start using them without giving you any basic understanding of how those imported functions even work to begin with. Even to this day there are still college courses and coding bootcamps that ask you to download a Jupyter Notebook file and just hit "Shift + Enter" and look at the output.
You're not going to learn how to code that way!!!
Joel Grus does an excellent job of filling in this gap by teaching you more Python than what a statistics professional would usually know and more math than what a typical software developer would know. And that's key if you want to go into a field that relies on both.
All the information for Python and math that you need to get started is here. It's 27 chapters that get you familiar with Python and how to use it, as well as the math used in data science and ML (linear algebra, probability and statistics, algorithms, etc).
You eventually learn enough of both as you go through the chapters to start applying what you learn for some real-world usage.
I've had this book for years and it's still as useful as when it first came out, but the only exception I've seen is that the Twitter API tutorial in the book no longer applies to the paid format that Twitter now uses to access that feature. The tutorial is still good for learning how API's get put to use.
Once you've read this book and have gotten familiar with all it has to offer, your next step will probably involve looking into a book about how to actually use pre-built data science libraries (like what you find in the Anaconda distribution of Python).
This book may turn out to be heavily responsible for my first startup, but that's a story for later.
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.
Top reviews from other countries
His writing style is friendly and informal. Despite this he covers the mathematical and Computational topics in reasonable depth and always points to further reading at the end of chapters.
The fact that all code used in the book is also explained therein makes the algorithms very graspable.
I would recommend this book anybody who wants to either start with data science or fill in some gaps like was the case with me.
I would love read more books written by Mr. Grus'. I have become a fan.
Highly recommended if that's the first book you buy