Tag: reblog

Deena Gergis is a data science lead at Bayer. I recently discovered Deena’s article on LinkedIn titled “Five Things I Wish I Knew About Real-Life AI.” I think that this article is a great piece of a career advice for all the current and aspiring data scientists, as well as for all the professionals who work with them. Let’ me take Deena’s headings and add my 2 cents.

Besides being a freelancer data scientist and visualization expert, I teach. One of the toughest concepts to teach and to visualize is odds ratio. Today, I stumbled upon a very interesting post that deals exactly with that

OK, so Stephen Wolfram (a mega celebrity in the computational intelligence world and, among other things a physicist) claims that he may have found a path to the Fundamental Theory of Physics. The blog post is long, and I hope to be able to finish reading it in a week or two. The accompanying technical text is a 450-page tome available on a dedicated site.

My colleague, Simon Ouderkik, recorded a REALLY interesting interview with Stephen Levin of Zapier and Emilie Schario of Gitlab on organizing data org in a company, job titles, career ladders, and other important stuff.

My job wasn’t affected by the COVID madness in almost any way. I used to work from home before, and I work from home now, none on my customers cancelled any projects, the health system in Israel is still functioning, all of my relatives are in good health, everything is just fine! I know how unusual I am in the current world, with the skyrocketing unemployment, non-functioning governments, and three-digit body counts. I was about to write about that, but then I read AnnMaria’s post.

You know that I’m a data science consultant now, don’t you? You know that AnnMaria De Mars, Ph.D. (the statistician, game developer, the world Judo champion) is one of my favorite bloggers, and her blog is the second blog I started to follow don’t you?

Many years ago, I terribly overfit a model which caused losses of a lot of shekels (a LOT). It’s not that I wasn’t aware of the potential overfitting. I was. Among other things, I used several bootstrapping simulations. It turns out that I applied the bootstrapping in a wrong way. My particular problem was that I “forgot” about confounding parameters and that I “forgot” that peeping into the future is a bad thing.

Dr. Richard Brath is a data visualization expert who also blogs from time to time. Each post in Richard’s blog provides a deep, and often unexpected to me, insight into one dataviz aspect or another.

Traditional A/B testsing rests on a fundamentally flawed premise. Most of the time, version A will be better for some subgroups, and version B will be better for others. Choosing either A or B is inherentlyinferior to choosing a targeted mix of A and B.

Michael Kaminsky locallyoptimistic.com