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Advanced Modules

The modules in this unit are Advanced level and contains materials and case studies to support a variety of classes.

Modules

3.1.1 - Advanced - Linear Differencing Models I

This note introducing difference-in-difference style models, particularly for causal models and inference.

3.1.2 - Advanced - Linear Differencing Models II

This notebook introduces students to linear differencing, focusing on techniques of difference-in-differences with variation in treatment timing, and event-studies with a…

3.2.1 - Advanced - Instrumental Variables

An introduction to estimating causal effects with instrumental variables on Jupyter and R.

3.2.2 - Advanced - Instrumental Variables 2

An introduction to estimating causal effects with instrumental variables on Jupyter and R.

3.3 - Advanced - Panel Data

This module goes over the theory of panel data analysis as well as how to apply the theory to real-world data. We look into panel regressions, fixed effects, a few other…

3.4 - Advanced - Synthetic Control

An introduction to estimating causality through the use of synthetic control. Synthetic control is the process by which we create a counterfactual to the unit we actually…

3.5.1 - Advanced - Geospatial Analysis

This notebook introduces geospatial analysis with vector data in R. We go over basic geospatial objects and operations.

3.5.2 - Advanced - Geospatial Analysis

This notebook explores geospatial analysis with vector data in R in more detail. We go over file types, choosing a CRS, as well as an application with real world data.

4.1 - Advanced - Classification and Clustering

This notebook introduces the classification and clustering models, especially for economic and sociological datasets.

4.2 - Advanced - Introduction to Sentiment Analysis

This notebook explains how to perform basic sentiment analysis and Reddit web scraping using R.

4.3.1 - Advanced - Transcription

This notebook introduces how to use machine learning tools to transcribe and diarize audio files.

4.3.2 - Advanced - Vocalization

This notebook demonstrates how to produce human-like speech from text input in a programmatic fashion, using Python.

4.4 - Advanced - Word Embeddings (Python)

This notebook introduces the concept and implementation of word embeddings, as used in AI tools like LLMs, in Python.

4.4 - Advanced - Word Embeddings (R)

This notebook introduces the concept and implementation of word embeddings, as used in AI tools like LLMs, in R.

4.5 - Advanced - LLM APIs 2

This notebook illustrates how to call different Large Language Models (LLMs) using their API, for the purposes of data analysis or computational use.

4.6 - Advanced - Fine-Tuning Large Language Models for Sentiment Analysis

An introduction to fine-tuning LLMs using BERT, in Python.