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

Description

This is the Rough Cut version of the printed book.

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

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

Sample Content

Table of Contents

Preface

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

Updates

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