Useful Resources

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.

Authors Hadley Wickham and Garrett Grolemund 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.

You’ll learn how to:

• Wrangle—transform your datasets into a form convenient for analysis
• Program—learn powerful R tools for solving data problems with greater clarity and ease
• Explore—examine your data, generate hypotheses, and quickly test them
• Model—provide a low-dimensional summary that captures true "signals" in your dataset
• Communicate—learn R Markdown for integrating prose, code, and results

This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkison's Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, it's easy to:

• produce handsome, publication-quality plots, with automatic legends created from the plot specification
• superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales
• add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression
• save any ggplot2 plot (or part thereof) for later modification or reuse
• create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots
• approach your graph from a visual perspective, thinking about how each component of the data is represented on the final plot

This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.

Hadley Wickham is an Assistant Professor of Statistics at Rice University, and is interested in developing computational and cognitive tools for making data preparation, visualization, and analysis easier. He has developed 15 R packages and in 2006 he won the John Chambers Award for Statistical Computing for his work on the ggplot and reshape R packages.

An Essential Reference for Intermediate and Advanced R Programmers

The book presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. Hadley Wickham (http://hadley.nz/), illustrates the elegance, beauty, and flexibility at the heart of R.

The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn:

• The fundamentals of R, including standard data types and functions
• Functional programming as a useful framework for solving wide classes of problems
• The positives and negatives of metaprogramming
• How to write fast, memory-efficient code

This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

If you are using R and you think you’re in hell, this is a map for you.

A book about trouble spots, oddities, traps, glitches in R.

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. 

Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.

Easy to understand and fun to read, Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned.

You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.

• Learn simple data types, and basic math and text operations
• Use data-wrangling techniques with Python’s built-in data structures
• Explore Python code structure, including the use of functions
• Write large programs in Python, with modules and packages
• Dive into objects, classes, and other object-oriented features
• Examine storage from flat files to relational databases and NoSQL
• Use Python to build web clients, servers, APIs, and services
• Manage system tasks such as programs, processes, and threads
• Understand the basics of concurrency and network programming

If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you?

In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to:

• Search for text in a file or across multiple files
• Create, update, move, and rename files and folders
• Search the Web and download online content
• Update and format data in Excel spreadsheets of any size
• Split, merge, watermark, and encrypt PDFs
• Send reminder emails and text notifications
• Fill out online forms

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way.

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Writen by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book.

Machine learning is transforming the way businesses operate. Being able to understand trends and patterns in complex data is critical to success; it is today one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

Python Machine Learning gives you access to the world of machine learning and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.