Books
One of the most valuable learning resources we have is books. Whether paper, digital or audio; books are the key to unlocking vasts amounts of knowledge in a small & mobile form factor.
Books are a versatile tool. They generally cover more background and detail on a subject matter than other types of training materials, which is especially important for learning technical & complex topics.
While there have been many books written over the years about Data Science & Artificial Intelligence, some better than others, here are a few of my top picks based on their content, formatting, authors and readability.
Deep Learning with Python
by François Chollet
Publication Date 12/22/2017
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
Deep Learning with R
by François Chollet, J. J. Allaire
Publication Date 02/09/2018
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples.
You'll practice your new skills with R-based applications in computer vision, natural-language processing and generative models.
Python for Data Analysis
by Wes McKinney
Publication Date 10/21/2017
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Written by the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
R for Data Science
by Hadley Wickham, Garrett Grolemund
Publication Date 01/07/2017
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
The authors guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
R Data Science Quick Reference
by Thomas Mailund
Publication Date 08/08/2019
In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them.
In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.
After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis.
Sams Teach Yourself SQL in 10 Minutes
by Ben Forta
Publication Date 12/26/2019
Sams Teach Yourself SQL in 10 Minutes offers straightforward, practical answers when you need fast results. By working through the book’s 22 lessons of 10 minutes or less, you’ll learn what you need to know to take advantage of the SQL language.
Lessons cover IBM DB2, Microsoft SQL Server and SQL Server Express, MariaDB, MySQL, Oracle and Oracle express, PostgreSQL, and SQLite.
Google BigQuery The Definitive Guide
by Valliappa Lakshmanan, Jordan Tigani
Publication Date 11/12/2019
Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently.
Valliappa Lakshmanan, tech lead for Google Cloud Platform and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.
Python Crash Course
by Eric Matthes
Publication Date 05/03/2019
Second edition of the best selling Python book in the world. A fast-paced, no-nonsense guide to programming in Python. This book teaches beginners the basics of programming in Python with a focus on real projects.
Python Crash Course, 2nd Edition is a straightforward introduction to the core of Python programming. The author dispenses with the sort of tedious, unnecessary information that can get in the way of learning how to program, choosing instead to provide a foundation in general programming concepts, Python fundamentals, and problem solving. Three real world projects in the second part of the book allow readers to apply their knowledge in useful ways.
Readers will learn how to create a simple video game, use data visualization techniques to make graphs and charts, and build and deploy an interactive web application. Python Crash Course, 2nd Edition teaches beginners the essentials of Python quickly so that they can build practical programs and develop powerful programming techniques.
Python Flash Cards
by Eric Matthes
Publication Date 01/15/2019
These colorful programming study cards help new Python coders drill and reinforce the concepts, syntax and terminology they'll need to become successful professional programmers.
Keep your coding skills sharp on the go! Python Flash Cards take a tried-and-tested method and give it a programming makeover. The author of the best-selling Python Crash Course, distills essential Python programming knowledge into this 101-card deck you can use anywhere.
Work through the deck in order or shuffle it up for a new study session every time. You can brush up on foundational programming principles and vocabulary like data structures, logical control, and program flow, quiz yourself on Python syntax, and test your skills against exercises and challenges designed to keep you on your toes — all in one sitting.
