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Applied Machine Learning for Data Scientists and Software Engineers Framing--The First Steps Toward Successful Execution

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Applied Machine Learning for Data Scientists and Software Engineers Framing--The First Steps Toward Successful Execution by Kelleher, Andrew; Kelleher, Adam, 9780134116549
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  • ISBN: 9780134116549 | 0134116542
  • Cover: Paperback
  • Copyright: 11/19/2018
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All 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 everything from science to marketing. This book 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 all the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They then explain how to “wrangle” your data into a form where it can be easily used, analyzed, and visualized so everyone can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming 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 interrogating your own data and getting answers you can act upon.

  • 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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