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 Global Forum 2020 is not the conference experience we thought it would be. Thousands of us had planned to gather in person to share our enthusiasm and knowledge about SAS and power of data and analytics. We were going to combine our skills and knowledge to inspire one another
In his recent article Perceptions of probability, Rick Wicklin explores how vague statements about "likeliness" translate into probabilities that we can express numerically. It's a fun, informative post -- I recommend it! You'll "Almost Certainly" enjoy it. To prepare the article, Rick first had to download the source data from
At SAS, we've published more repositories on GitHub as a way to share our open source projects and examples. These "repos" (that's Git lingo) are created and maintained by experts in R&D, professional services (consulting), and SAS training. Some recent examples include: sas_kernel, which provides Jupyter notebook support for SAS.
Right now I’m crossing the Pacific toward Australia and New Zealand for the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (a.k.a. KDD), a Data Science Melbourne MeetUp, and the SAS Users of New Zealand conference. New Zealand is the birthplace of open source R. So this trip
I used "Dropbox" in the title for this post, but these techniques can be used for other cloud-based file sharing services, such as GitHub and Google Drive. Using PROC HTTP (added in SAS 9.2), you can easily access any "cloud-based" file as long as you have a public link to
I work on a variety of projects at SAS, most of which require some level of team collaboration in source management systems. Due to the many technologies that we work with, SAS developers use different source management tools for different purposes. I've got projects in CVS, Subversion, and Git. When