Data Science at Home

I have a BS in Computer Science. I've taken my share of math, including classes on statistics, graph theory, and optimization. I can barely keep up because my day job doesn't put me near this material. If I was working on my Masters in CS, this is great stuff. If I was trying to adapt existing projects to Big Data, this really helps me avoid some traps and pitfalls. It also helps me decode lingo from software vendors to find the liars. What it won't do is teach you Data Science. I'd get lost for hours if Francesco uploaded his script to his blog with links to projects, papers, and products. It is a very dense lecture though that pulls valuable experiences into one place. Between that and the great improvements to the production process, I'm going to keep listening and gleaning. In late 2019, Francesco turned the mic over to a friend with a soap box that rightly attacked recommender algorithms, but it stepped way far away from a technical, improvisational interview. Francesco has finally started to realize that ML is easily abused and I look forward to his leadership in guiding the ML industry in better methods to avoid traps imposed by data owners on their analysts.

I was hopeful because I seek out opinions that counter the hype, but all I heard were contrarian takes for the sake of being different.

Another really boring, sometimes factually challenged, poorly produced podcast by somebody has little of substance to say.

Disappointed to see that the research by a data science podcast is not up to date. Lidar is no longer large racks. Lazr sells small lidars. NIO ev have small lidars.