Data Science for Finance

The Objectives page says that the rest of the class is about

  • what we're going to do with the data. producing and improving a model. applied econometrics (ie more conceptual than mathematical rigor) to understand and improve the output
  • understanding the how "data analysis/ML/<<buzz word #51>>" fit into the bigger picture of producing and using our domain knowledge of from finance. to quote Prof Gunther: data < info < knowledge < wisdom
  • learning from the model: what does the output of my analysis mean? (A and B are related, but WHY)

So that's what we're going to do. We have enough raw tools and you should have (nearly) completed your first farm-to-table analysis (scrap, clean, and process the data) for Assignment 5.

Over the next month, we going to look at

  • uncovering relationships
  • building prediction models
  • regression (how-to, why-to, and what it "means")
  • logit, and some quick passes at other algorithms
  • but more importantly: the whole time, we're going to stay focused on "WHY" we are doing what we are doing rather than just flying ahead blind

This should be fun!