The SAS Data Science Blog
Advanced analytics from SAS data scientists
I have been working on streaming analytics in conjunction with a project at Duke Energy, so a few months ago I was contacted by a colleague who wanted to look at the feasibility of applying what I’ve learned to our Internet of Things (IoT) initiative. In particular, we wanted to see if
SAS is hosting this year’s European Analytics 2015 conference in Rome November 9 – 11. This three-day inspiring event will give you the chance to boost your company’s analytics culture in an international environment to make sure your knowledge and expertise meet the demands of the digital era. But what if
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
How do we hire data scientists at SAS, since we are not unique in our search for a rare talent type that continues to be in high demand? This post is the last in a series on finding data scientists, based on best practices at SAS and illustrated with some
I am noticing a trend. At the ASSA meetings in January (where economics, sociology and finance academics and practitioners gather to discuss their research) I was surprised to see how much “machine learning” was trending with economists. The session “Machine Learning Methods in Economics and Econometrics,” with papers by Susan
There is a job category unfamiliar to most people that plays a crucial role in the creation of analytics software. Most can surmise that SAS hires software developers with backgrounds in statistics, econometrics, forecasting or operations research to create our analytical software; however, most do not realize there is another
