Description: Data is everywhere. Health care generates as much as 30% of the world’s data, and new technologies are changing how life insurers analyze consumer information. Actuaries are business-savvy problem solvers. They understand how to leverage the latest technologies and techniques to turn data into actionable insights and solutions for businesses and can communicate those findings to multiple audiences. SOA-credentialed actuaries use predictive analytics to inform organizational and business strategies, with efforts such as identifying chronic conditions in their early stage or determining stress levels through sweat patterns.

Description: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

Description: Introduction to PMML and how to use of it in the R programming language. PMML is an interchange format for predictive models.

Description: This paper describes techniques that can be used to access any specific piece of information in Wikipedia, such as medical information.

Description: A description of the operation of an R package for experience studies. Premium patterns, lapse rates, and mortality rates can be calculated.

Description: An autobiographical journey from actuarial student to data scientist.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: Practical Analytics 4.0 Virtual Event recordings