The SAS Data Science Blog
Advanced analytics from SAS data scientists
SAS' Bahar Biller reveals how simulations enable KPI generation, risk quantification, risk management and more.
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.