Welcome!

I teach the following courses at MIT:

- Data, Models and Decisions (15.060)
- The Analytics Edge (15.071)
- Hands-on Deep Learning (
*new course launching Spring 2022!*)

Occasionally, I write brief notes to supplement the lecture materials. Those notes, as well as my data science posts published on Medium and the MIT Sloan site, are gathered here.

I hope you find the notes useful. Feedback is welcome (@rama100, )

- I Have Data. I Need Insights. Where Do I Start?
- An Alternative to the Correlation Coefficient That Works For Numeric
*and*Categorical Variables - How to Use Causal Inference In Day-to-Day Analytical Work (Part 1 of 2)
- How to Use Causal Inference In Day-to-Day Analytical Work (Part 2 of 2)

If you know how to build predictive models, you can leverage this knowledge to learn **optimal policies** - rules that tell you the best way to act in various situations - directly from data.

Policy optimization problems are very common in the business world (e.g., arguably, every personalization problem is a policy optimization problem) and knowing how to solve them is a data science superpower. The following series of blog posts aims to give you that superpower :-)

- In Part 1, I motivate the need to learn optimal policies from data. Policy optimization covers a vast range of practical situations and I briefly describe examples from healthcare, churn prevention, target marketing and city government.
- In Part 2, I walk through how to create a dataset so that it is suited for policy optimization.
- In Part 3, I describe a simple (and, in my opinion, magical) way to use such a dataset to estimate the effectiveness of
**any**policy.. - In Part 4, I show how to use such a dataset to
**find an optimal policy**.

- A Lightning Guide to Web-Scraping Data with R
- Finding Useful Datasets through R (Under preparation …)