Description: It talks about the major areas of data science and how to learn data science with the actuarial background.

Description: How actuaries can become comfortable accessing, integrating and building complex data sets from big and broad sources of information to study the value of multidimensional drivers of change.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: Actuaries provide special subject matter expertise to question (or validate) the results of data analytics models. Other quants often cannot do this.

Description: It is about using actuarial judgement to validate the predictive model.

Description: Describes goals of the section for the coming year.

Description: Evaluates the ability of various model types to automatically approximate various mathematical formulas. This can be used as a guide for feature engineering.