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Data Science Foundations Tools and Techniques: Addison-Wesley Data & Analytics

Autor Michael Freeman
en Limba Engleză Paperback – 28 aug 2018
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
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Specificații

ISBN-13: 9780135133101
ISBN-10: 0135133106
Pagini: 250
Dimensiuni: 178 x 232 x 10 mm
Greutate: 0.45 kg
Editura: Pearson Education (US)
Seria Addison-Wesley Data & Analytics


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Cuprins

  • 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