For about two years, I’ve been working on a book called Computational Methods for Numerical Analysis with R (CMNA), which will present an outline of numerical analysis topics with original (and simplified) implementations in R at a level appropriate for a graduate student or advanced undergraduate. Last night, I sent the latest draft to my editors, and I am quite pleased to say it should be heading into production, soon. The organizational structure of the text is based roughly on the organizational structure of MAPL 460 15-20 years ago: Introduction to Numerical Analysis Error Analysis Linear Equations Interpolation and Extrapolation
Bill Marcus writes about the intersection between deep analytics and big data in HPE Insights: [T]he scale of an organization and its data is key to how much the process will matter, according to James Howard… “If you’re a small bank, machine learning is going to help get you through the day, but it’s not going to be something critical—whereas if you’re doing high-frequency trading, all you’re doing is machine learning.” Read more at HPE Insights: In the minds of machines: Fundamental change from deep analytics – HPE Business Insights By Bill Marcus, contributing writer In Munich, Germany, the technology
David Smith and I are now talking to each other in blog posts and it is only a little weird. Also, I’ve been traveling and am a bit behind. In a comment on this post, he notes this: I suspect the reason why R Core adopted the 0^0=1 definition is because of the binomial justification, R being a stats package after all. I can’t think of any defense for NaN^0=1 though… Well, it turns out there’s a good reason. If we go back C, and try an experiment, we can observe the following example produces these results: Compiling and executing
R has two different ways of representing missing data and understanding each is important for the user. NaN means “not a number” and it means there is a result, but it cannot be represented in the computer. The second, NA, explains that the data is just missing for unknown reasons. These appear at different times when working with R and each has different implications. NaN is distinct from NA. NaN implies a result that cannot be calculated for whatever reason, or is not a floating point number. Some calculations that lead to NaN, other than , are attempting to take
Brookings questions the value of ban the box legislation and includes this gem: [E]mployers might prefer applicants with a college degree not because of what one learns in college but because earning that degree is correlated with greater motivation, intelligence, and diligence – unobservable characteristics that make you a more productive employee. If the characteristics are unobservable, how do we know they correlate with earning a degree or being a more productive employee? Image by xkcd, but you already knew that.