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
Analytics, statistics, operations research, data science and machine learning - with which term do you prefer associate? Are you from the House of Capulet or Montague, or do you even care? Shakespeare's Juliet derides excess identification with names in the famous play, Romeo and Juliet. "What's in a name? That which we call
When you go to the grocery store, you see that items of a similar nature are displayed nearby to each other. When you organize the clothes in your closet, you put similar items together (e.g. shirts in one section, pants in another). Every personal organizing tip on the web to
"I've seen the future of data science, and it is filled with estrogen!" This was the opening remark at a recent talk I heard. If only I'd seen that vision of the future when I was in college. You see, I’ve always loved math (and still do). My first calculus
I recently read the book "Die Zahl die aus der Kälte kam" (which would be The Number That Came in from the Cold in English) written by the Austrian mathematician Rudolf Taschner. He is ingenious at presenting complex mathematical relationships to a broader audience. One of his examples deals with
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
