The SAS Data Science Blog
Advanced analytics from SAS data scientists
In our previous post, Econometric and statistical methods for spatial data analysis, we discussed the importance of spatial data. For most people, understanding that importance is relatively easy because spatial data are often found in our daily lives and we are all accustomed to analyzing them. We can all relate to
Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels. Do you remember lugging around those old suitcases? If not, good for you - this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his
Machine learning applications for NBA coaches and players might seem like an odd choice for me to write about. Let us get something out of the way: I don’t know much about basketball. Or baseball. Or even soccer, much to the chagrin of my friends back home in Europe. However,
Multi-echelon inventory optimization is ever more a requirement in this era of globalization, which is both a boon and bane for manufacturing companies. Optimizing pricing is also important. Global reach allows these companies to expand to new territories but at the same time increases the competition on their home turf.
We live in a complex world that overflows with information. As human beings, we are very good at navigating this maze, where different types of input hit us from every possible direction. Without really thinking about it, we take in the inputs, evaluate the new information, combine it with our
Optimization for machine learning is essential to ensure that data mining models can learn from training data in order to generalize to future test data. Data mining models can have millions of parameters that depend on the training data and, in general, have no analytic definition. In such cases, effective models