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

by Freeman, Michael; Ross, Joel

• ISBN13: 9780135133101
• ISBN10: 0135133106
• Edition: 1st
• Format: Paperback
• Copyright: 2018-11-28
• Publisher: Addison-Wesley Professional

Summary

The Foundational Hands-On Skills You Need to Dive into Data Science

“Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.”

–From the foreword by Jared Lander, series editor

Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience.

 

Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your 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, you’ll 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 you can quickly start analyzing your own data and getting answers you can act upon. Learn to

• Install your complete data science environment, including R and RStudio
• Manage projects efficiently, from version tracking to documentation
• Host, manage, and collaborate on data science projects with GitHub
• Master R language fundamentals: syntax, programming concepts, and data structures
• Load, format, explore, and restructure data for successful analysis
• Interact with databases and web APIs
• Master key principles for visualizing data accurately and intuitively
• Produce engaging, interactive visualizations with ggplot and other R packages
• Transform analyses into sharable documents and sites with R Markdown
• Create interactive web data science applications with Shiny
• Collaborate smoothly as part of a data science team

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Author Biography

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.

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