Description: Description of SOA's initiative in predictive analytics

Description: The author presents autoencoders in a clear manner and then goes on to show an intriguing application where an autoencoder can alert us to events out of the ordinary.

Description: A discussion on how the look of charts and graphs can affect comprehension of data.

Description: This article discusses ethical issues that have arise with advancements in Data Science. Through examples of these issues address actuarial professionalism.

Description: The artcle looks at the most important features necessary to conduct a meaningful quantitative analysis. The article also discusses how predictive analytics should be used as a tactic to anwer question and not an end in of itself.

Description: The author reviews a book the debunks some of the hype we read about using quantity at the expense of quality. She also points out some unrealistic outcomes when data scientists lack business knowledge.

Description: Editorial for the May 2019 issue of Predictive Analytics & Futurism newsletter.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

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?