Weekly Materials & Readings

Data Science and Public Policy, McCourt School for Public Polcy, Georgetown University

Week 1, September 2nd

• Topics: Course overview, logistics, introduction to R

• Lecture: Week 1 - Introduction

• Coding Materials: Week 1 - Introduction to R

• Data: Pooling Data

Week 2, September 8th

• Topics: How to represent text as data, Bag of Words Assumption, Tokens and Pre-Processing

• Lecture: Week 2 - From Text to Matrices: Representing Text as Data

• Coding Materials: Week 2 - Text Data Processing

• Data:

  • Tweets from Elections

Week 3, September 15

• Topics: vector space model, comparing documents, textual complexity.

• Lecture: Week 3 - Descriptive Inference

• Coding Materials: Week 3 - Descriptive Inference

Week 4: September 22

• Topics: Dictionaries, Pros and Cons, and Using Off-the-Shelf Models

• Lecture: Week 4 - Dictionaries and Off-the-Shelf Classifiers

• Coding Materials:

  • Week 4 - Dictionaries and Off-the-Shelf Classifiers

  • Week 4 - Transformers Based Classifiers

  • Week 4 - Transformers Based Classifiers in R , html here

• Data: Streamming Chat Comments

Week 5: September 29

• Topics: Building a training data, regularization, model selection, and model evaluation.

• Lecture: Week 5 - Supervised Learning

• Coding Materials: Week 5 - Supervised Learning: Training your own classifiers

Week 6: October 6

• Topics: Unsupervised learning, topic models, and clustering

• Lecture: Week 6 - Unsupervised Learning

• Coding Materials: Week 7 - Unsupervised Learning: Topic Models