In this week we returned to R and Machine Learning to look at the curse of dimensionality and clustering. We went to the depths of Principal Component Analysis, Lasso and Ridge Regression, and K-means and Hierarchical Clustering. The real ‘Aha!” moment for me this past week was my gaining a concrete understanding of what an eigenvalue is in essence. It was beautiful.
Throughout the week the different webscraping projects were presented. My own was much reduced in scope from what I first intended, but I think I have the framework for a truly great data product on my head. Right now only 4 days worth of webscraping stands in my way.
We finished off the week with another session with Henri Dwyer from Dataiku. The session was quite enjoyable and the power of Data Science Studio is really starting to sink in. Unfortunately, I won’t have time to explore it more until after the bootcamp.
Next week it’s back to Python to touch on the foundations of Computer Science - read algorithms and big-O analysis. Personally, I have to do some self-study as I will be away for one day participating in a data hackathon. On the project side, some other members of the cohort and I have decided to work on the Kaggle Rossman challenge as a new venture in building our portfolios.