The SAS Data Science Blog
Advanced analytics from SAS data scientists
Wouldn’t it be cool if we establish a mechanism that provides more data scientists easy access to SAS Reinforcement Learning capabilities, from a centralized location and using a standardized approach?
SAS research statistician Ji Shen reveals how to train a machine to be a batting coach.
SAS' Michael Lamm gives an overview of Bayesian Additive Regression Trees (BART) and demonstrates training and scoring BART models in SAS Visual Statistics.
Robert Blanchard's role as a data scientist at SAS has afforded him the flexibility to live where he wants, in his case, on a beach in San Diego.
The Proc Python procedure, Python code editor & Python code step facilitate low-code analytics calling Python and SAS from a common interface. Data scientists also appreciate the connection to Python & R through the Model Studio Open-source Code node. Older methods of interaction include the swat and sas_kernel packages running on Python clients.
SAS' Ricky Tharrington and Jagruti Kanjia explain two ways bias shows up in model predictions.