The SAS Data Science Blog
Advanced analytics from SAS data scientists
It is said that everything is big in Texas, and that includes big data. During my recent trip to Austin I had the privilege of being a judge in the final round of the Texata Big Data World Championship, a fantastic example of big data competitions. It felt fitting that
As an economist, I started at SAS with a disadvantage when it comes to predictive modeling. After all, like most economists, I was taught how to estimate marginal effects of various programs, or treatment effects, with non-experimental data. We use a variety of identification assumptions and quasi-experiments to make causal
My view of the world is shaped by where I stand, but from this spot the future of analytics for 2016 looks pretty exciting! Analytics has never been more needed or interesting. Machine learning established in the enterprise Machine learning dates back to at least 1950 but until recently has
Macroeconometrics is not dead: (and I wish I had paid better attention in my time series course): I wrote this on the way to see one of our manufacturing clients in Austin, Texas, anticipating a discussion how to use vector autoregressive models in process control. It is a typical use
Can pattern recognition software tell us if it is a Hermit Thrush or a Swainson's Thrush we've seen? A few of us have been debating an identification question at work, because we agreed to help Fulbright Scholar and Duke University PhD student Natalia Ocampo-Peñuela with research she is doing related to bird collisions with windows. A sad
If you turned in for my recent webinar, Machine Learning: Principles and Practice, you may have heard me talking about some of my favorite machine learning resources, including recent white papers and some classic studies. As I mentioned in the webinar, machine learning is not new. SAS has been pursuing