Description: This article shares our experience of using the cloud to do large-scale parallel processing and discusses some background regarding the cloud.

Description: This article introduces SymPy, a computer algebra system (CAS) for the Python programming language.

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

Description: Up until recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops . In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents a promising prospect for overcoming problems of big data.

Description: This article goes through some basic data exploration techniques to show how different approaches look.

Description: A summary of a survey the Society of Actuaries recently conducted of health payer and provider executives to glean insights into predictive analytics trends.

Description: An exploration of moving predictive modeling capabilities in R to real time operations.

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: A review of the Bridging the Gap Series: Application of Predictive Modeling in VA/FIA Risk Management at the Equity Based Guarantees Conference in Chicago.

Description: This article explains how to create more maintainable models using abstractions by relating it to option, machine learning and machine intelligence models.