Data Science Foundations Tools and Techniques: Core Skills for Quantitative Analysis with R and Git (Addison-Wesley Data & Analytics Series) - Tapa blanda

Sinopsis

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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