Data Science Foundations Tools and Techniques Core Skills for Quantitative Analysis with R and Git

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  • Edition: 1st
  • Format: Paperback
  • Copyright: 2018-09-13
  • Publisher: Addison-Wesley Professional
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Supplemental Materials

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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.


This unique guide brings together all the skills you need to get started with data science -- one of the world’s fastest growing fields! Leading data science instructors Michael Freeman and Joel Ross start by guiding you through installing and configuring all the free open source software you’ll need to solve professional-level data science problems. Next, they introduce key concepts, show how to work with data, and build your understanding with practical examples. You’ll learn crucial R programming skills, and find easy-to-understand reference content to help you get the syntax right and troubleshoot your own code. Step by step, you’ll learn through practical exercises that can be combined into complete data science projects. Everything’s focused on practical application, so you can get real results, fast!

  • Understand the field of data science (and why it’s growing so fast)
  • Install and configure R, RStudio, git, GitHub, and VSCode
  • Keep track of your work with Git and GitHub
  • Use simple Markdown code to style documents for easier understanding
  • Work with the R programming language and its objects
  • “Wrangle” data into shape for processing
  • Create R visualizations with ggplot and Plotly
  • Publish with RMarkdown
  • Build interactive applications with Shiny
  • And much more

Table of Contents

Part 1: Getting Started
1. What Is This Book (and This Field) About?
2. Configuring your Computer for Data Science


Part 2: Keeping Track of your Code
3. Introduction to Git and GitHub
4. Using Markdown


Part 3: Foundational R Skills
5. Introduction to Programming for Data Science


Part 4: Data Wrangling
6. Introduction to Data Frames
7. Data Wrangling with the DPLYR library


Part 5: Data Visualization
8. An Overview of Visualization Principles
9. Visualization in R: ggplot and Plotly


Part 6: Building and Sharing Applications
10. Publishing Reports on GitHub using RMarkdown
11. Building Interactive Applications using Shiny

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