https://bigdataanalytics.mit.edu/
Module 1: Making sense of unstructured data
Clustering
Spectral Clustering, Components and Embeddings
Case Studies
Module 2: Regression and Prediction
Classical Linear & nonlinear regression & extension
Modern Regression with High-Dimensional Data
The use of modern Regression for causal inference
Case Studies
Module 3: Classification, Hypothesis Testing and Anomaly Detection
Hypothesis Testing and Classification
Deep Learning
Case Studies
Module 4: Recommendation Systems
Recommendations and ranking
Collaborative filtering
Personalized recommendations
Case Studies
Wrap-up: Parting remarks and challenges
Module 5: Networks and Graphical Models
Introduction
Networks
Graphical Models
Case Studies
Module 6: Predictive Modeling for Temporal Data
Introduction
Prediction engineering
Feature engineering
Modeling and evaluating predictive models