https://github.com/sdcastillo/PA-R-Study-Manual

An online study guide for the SOA's predictive analytics exam.
https://github.com/sdcastillo/PA-R-Study-Manual

data-science data-visualization machine-learning predictive-modeling r-programming

Last synced: 4 months ago
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An online study guide for the SOA's predictive analytics exam.

• Host: GitHub
• URL: https://github.com/sdcastillo/PA-R-Study-Manual
• Owner: sdcastillo
• License: cc0-1.0
• Created: 2019-09-21T13:15:31.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-12-16T04:57:22.000Z (7 months ago)
• Last Synced: 2024-01-17T14:18:43.533Z (6 months ago)
• Topics: data-science, data-visualization, machine-learning, predictive-modeling, r-programming
• Language: HTML
• Homepage:
• Size: 63.1 MB
• Stars: 5
• Watchers: 2
• Forks: 5
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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• jimsghstars - sdcastillo/PA-R-Study-Manual - An online study guide for the SOA's predictive analytics exam. (HTML)

README

        

This is the study guide for [ExamPA.net](https://exampa.net), the online course for the SOA's Predictive Analytics exam. While meeting all of the learning requirements of Exam PA, this **250-page study guide** gives you data science and machine learning training. You will learn how to get your data into R, clean it, visualize it, and use models to derive business value.  Just as a scientist sets up lab experiments to form and test hypothesis, you’ll build models and then test them on holdout sets.

The chapters on R-programming cover the foundational concepts with a focus on modern data science applications.  We give you time-saving coding tips and ways of checking your answers within RStudio.

All of the statistical theory is explained, from linear regression to gradient boosted trees, and examples are provided of each model that you can reproduce.  Following the course materials “An Introduction to Statistical Learning“, we discuss model training, validation, as well as the advantages and disadvantages to each algorithm.

https://www.youtube.com/embed/F2okL4a2YcM

*We are thankful to all of the reviewers, guest editors, and past exam-takers who have helped to improve this book.  Thanks to the following persons who made changes to this book and its past versions: David Hill, Erlan Wheeler, Caden Collier, Peter Shelbe, Abhinav Gadde, Allen Meriken, [Kevin Kuo](https://github.com/kevinykuo), Aamir Ali, Matthew Caseras, and Liu Chang.*