Description: A review of the Johns Hopkins Data Science Specialization courses with special emphasis on how they may be useful to actuaries.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.

Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.

Description: This paper has two related purposes: first, to introduce a new class of Moving Weighted Average MWA graduation formulas where the coefficients are determined in accordance with the relative emphasis placed on smoothness and fit by the graduator, and second, to consider the question regarding what value of n to use when graduating by MWA formula. From the Actuarial Research Clearing House, 1978, Vol.1.