Books I’m reading
- Skin in the Game by Nassim Taleb
- Yet another mental models book called Super Thinking written by Gabriel Weinberg, founder of DuckDuckGo.
- Meditations by Marcus Aurelius translated by Gregory Hays. Given my recent interest in Stoicism, I’ve been told this is a classic.
- All I Want To Know Is Where I’m Going To Die So I’ll Never Go There, a compilation of material from Buffet and Munger written as a dialogue between Munger, Buffet, the Librarian, and the Seeker.
Moved back to the Austin area in Texas
Personally this made sense for me as this area is where I grew up and Austin is becoming quite the tech center. But the primary reason for this move at this time was to be closer to my immediate family.
Principles of Data Science
I’m a self-taught programmer and was fortunate to get hired as an applications developer for some time. Eventually I pivoted to data science and got a masters degree in mathematics. What I’ve noticed is that much of the data science community is not focused on reproducible and robust software.
Programming best practices like version control, unit and integration testing, dockerization, and other best practices are rarely seen in typical data science workflows. This makes sense for a variety of reasons - some data scientists come from non-programming backgrounds, and other data scientists do not focus on productionizing solutions but rather doing prototype work that’s passed on as initial research to a machine learning engineering team.
No doubt, the modern day data scientist can be expected to wear multiple hats but I believe rigor and reproducibility in research is of utmost importance. So my aim is to integrate as many software best practices as possible within data science along with investigating generalizable principles to be carried out when doing various analyses and model building.