Learn Predictive Analytics to Pass Exam PA

While meeting all of the learning requirements of Exam PA, the 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.

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.

Machine learning models covered are Generalized Linear Models, Gradient-Boosted Trees, Random Forests, Hiearchical and KMeans Clustering, and Principal Component Analysis. We discuss model training, validation, as well as the advantages and disadvantages to each algorithm. 

200 Pages / 18 Chapters (Online Book), Practice Exercises

Spend more time practicing and less time studying.  The practice exams are written in the CPT format of real exams.  They use RStudio, Word, and Excel just like Prometric does.  

Calculate your exam score and compare it to other people to discover your weak areas.  Each original practice exam is designed to test areas of the syllabus not covered in the official exams in order to keep your skills sharp.

Learn at your own pace with the highly-condensed, annotated video solutions.  Watch in fast forward as we walk through every task and write the Executive Summary.  

2 hrs 11 mins (Videos), 15 hrs, 45 mins (Self-Paced Learning)

Save yourself time by watching our “speed runs” of the official exams. Learn how to complete these exams in under 3 hours and 15 minutes instead of the total 5 hours and 15 minutes, giving you plenty of extra time to review your work, expand on your short-answer questions, and reduce test anxiety.  

Each of the 5 SOA past exams and practice projects are discussed in detail.  Alternative methods of answering the questions are discussed and the pros and cons are shown.  

 11 hrs 55 mins (Videos), 21 hrs, 30 mins (Self-Paced Learning)

This is the heart of Exam PA.  Make your report perfect under exam conditions with these writing methods.  Even if your answer is “incorrect” you will learn how to defend it appropriately to earn partial credit.  Avoid brain fog and “writer’s block” with our answer outlines.

Improve your writing speed to put more words on the page.  Our typing test provides a benchmark in Words-Per-Minute (WPM) as well as overall word counts.  Know if your answers are long enough or too short for each type of question by looking at our example answers.

Explain every concept in plain English.  Learn to use non-technical language to explain complex topics.  Our Discussion Forum dates back to December 2019 and contains insights into what the exam grading committee wants to see.

Is English not your first language?  This presents a big challenge because Data Science is a language in itself.  Our Live Chat has been helpful for a number of candidates who are not native English speakers.

21 Discussion Threads, 1 hr, 38 mins (Videos), 11 pages (Cheat Sheets)

Our tutorials teach R for data scientists and actuaries.  Learn data visualization, data storytelling, data transformations and cleaning, and machine learning.  

There’s no need to be an R expert.  We teach how to explain outputs effectively and can dive into the deeper details as needed.  Most questions focus on comprehension and explanation.  While having R experience is certainly helpful, so also is a background with SQL, SAS, VBA, or other programming tools or languages.  

Avoid technical difficulties, computer crashes, and code bugs.  We show you the cleanest way to get every answer, without getting lost in the weeds.

200-pages of R examples (Study Guide), dozens of multiple-choice R questions, 2 hrs of lessons (Video Tutorials)

Exam PA contains a huge amount of material.  There’s more on the syllabus than anyone can learn without memorizing certain topics.  Our cheat sheets let you review the critical info needed to pass.

The cheat sheets explain all R output relevant to the exam, helping you to explain the concepts.  The statistical definitions explain each algorithm, with pros and cons, using both technical and non-tecnical language.

11 pages