Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R, 1st edition

Published by Addison-Wesley Professional (November 28, 2018) © 2019
  • Michael Freeman
  • Joel Ross

Title overview

Leading instructors Michael Freeman and Joel Ross guide readers through installing and configuring the tools needed to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, students will master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales.

 

Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so students can quickly start analyzing their own data and getting answers they can act upon.

  • Guides students through setting up their computer for data science, understanding how the pieces fit together, and successfully using them to solve real problems
  • Introduces R, RStudio, git, GitHub, Markdown, Shiny, and other leading tools
  • Covers everything from preparing raw data to creating beautiful, sharable visualizations
  • Anticipates questions and demystifies complex ideas, reflecting the authors’ experience introducing data science to thousands of students

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Table of contents

  • Part I: Getting Started
  • Chapter 1: Setting Up Your Computer
  • Chapter 2: Using the Command Line
  • Part II: Managing Projects
  • Chapter 3: Version Control with git and GitHub
  • Chapter 4: Using Markdown for Documentation
  • Part III: Foundational R Skills
  • Chapter 5: Introduction to R
  • Chapter 6: Functions
  • Chapter 7: Vectors
  • Chapter 8: Lists
  • Part IV: Data Wrangling
  • Chapter 9: Understanding Data
  • Chapter 10: Data Frames
  • Chapter 11: Manipulating Data with dplyr
  • Chapter 12: Reshaping Data with tidyr
  • Chapter 13: Accessing Databases
  • Chapter 14: Accessing Web APIs
  • Part V: Data Visualization
  • Chapter 15: Designing Data Visualizations
  • Chapter 16: Creating Visualizations with ggplot2
  • Chapter 17: Interactive Visualization in R
  • Part VI: Building and Sharing Applications
  • Chapter 18: Dynamic Reports with R Markdown
  • Chapter 19: Building Interactive Web Applications with Shiny
  • Chapter 20: Working Collaboratively
  • Chapter 21: Moving Forward
  • Index

Author bios

Michael Freeman is a senior lecturer at the University of Washington Information School, where he teaches courses in data science, interactive data visualization, and web development. Prior to his teaching career, he worked as a data visualization specialist and research fellow at the Institute for Health Metrics and Evaluation. There, he performed quantitative global health research and built a variety of interactive visualization systems to help researchers and the public explore global health trends. Michael is interested in applications of data visualization to social justice, and holds a Master’s in Public Health from the University of Washington.

 

Joel Ross is a senior lecturer at the University of Washington Information School, where he teaches courses in web development, mobile application development, software architecture, and introductory programming. While his primary focus is on teaching, his research interests include games and gamification, pervasive systems, computer science education, and social computing. He has also done research on crowdsourcing systems, human computation, and encouraging environmental sustainability. Joel earned his M.S. and Ph.D. in information and computer sciences from the University of California, Irvine.

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