Week 2 - Galvanize Data Science Bootcamp

Week 2 just ended. Intensity is up. We are full throttle learning new concepts everyday and pushing the limits on what our the human brain can handle.

We began with probability and frequentist statistics. I would consider myself highly trained in this area after my Ph.D and significant training at Intel, so conceptually no issues. However, assignments were long with heavy coding requirements, so I was learning a lot.

We moved on to Bayesian statistics and I felt like my entire world flipped upside down. This is not a topic often taught outside of statistics or math departments at a university and it clearly should be. We began our Bayesian lessons by analyzing variations of webpages and their associated click rates. I really enjoyed the lessons on these methods, and was grateful that we now had enough plotting skills in Pandas and Matplotlib to visual the convergence of our statistical results.

Next we began the well documented ‘multi-arm bandit’ problem. Say you have 5 versions of the website and one of them is clearly best. Should you run an experiment until you are confident in a delta, and then select the best website as your new baseline? Or if you notice certain websites are much worse, can you update the sites your audience views so they are weighted in favor of the most effective sites? This concept effectively stems from methods used in Monte Carlo simulations in the 1970s where a Boltzmann method was utilized to minimize optimization in a local minima.

As much as I enjoyed learning these new Bayesian methods, I still need time to reflect on this very different approach to statistics. It seems clear that the instructors, and many data scientists are in favor of Bayesian statistics because of the simplicity with which you can interpret the results. However, I felt their neglect for frequentist approaches to data science were unwarranted and hyperbolic. Intel was extremely successful in chip manufacturing because of their frequentist approach to experimentation. Many other companies also utilize these methods to extend their competitive advantage. To simply say industry cannot interpret what a p-value means seems a bit ignorant to me, but I will move on.

I now feel amazing after our second week. I learned some very neat statistical methods, and the assignments hammered home the key learnings. Can't wait for the next week!