Data Science Practice
Focus on Iteration Speed
The OODA loop (Observe-Orient-Decide-Act) framework was developed by USAF Colonel John Boyd to improve fighter pilot performance in the field. We can apply a similar framework to improving the efficiency with which we develop data science models. The key insight is to embrace the iterative nature of model development and streamline each component of these iterations.
Advice for Early Career Data Scientists
Coming out of college, I had some ideas about how I was going to become successful and what my career was going to look like. Of course, I was all wrong. Here is the advice I would offer a young me.
Spinning up PostgreSQL in Docker for Easy Analysis
My typical analysis workflow is to start with data in some kind of database, perhaps Redshift or Snowflake. Often I’m working with millions or even billions of rows, but modern databases excel at operating with data at scale. Moreover, SQL is an intuitive and powerful tool for combining, filtering, and aggregating data. I’ll often do as much as I can in SQL, aggregate the data as much as I can, then export the data as a CSV to continue more advanced statistical calculations in python.
Repeatability
As businesses continue to invest in data-driven decision making, it becomes increasingly important to ensure the methods underlying those decisions are reliable. Unfortunately, we cannot take this for granted! Read on to learn a collection of best practices to make sure your decision making process rests on a sturdy foundation.