Lecture Notes on Analytics, Data Science and Optimization

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, )

Exploring Data and Drawing Conclusions

• 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)

Advice from the Data Science “Trenches”

• Data Scientists, Ask Yourself Often: So What?

• Create a Common-Sense Baseline First

• 6 steps for leading successful data science teams
  

Inspiration from Legends

• Lessons from a Deep Learning Master

The Intuition Behind Models and Algorithms

• The Intuition Behind Ensemble Learning, Bagging and Random Forests

From Prediction to Action — How to Learn Optimal Policies From Data

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.

Getting Your Hands on Useful Datasets

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

Resources for Self-Study

• Analytics/Data Science/R Self-Study Reading List

Random Stuff

• A Sudoku Solver in Python