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  1. Advanced Modules
  • Learn by Skill Level


  • Getting Started: Introduction to Data, R, and Econometrics
    • Intro to JupyterNotebooks
    • Intro to R
    • Intro to Data (Part 1)
    • Intro to Data (Part 2)

  • Beginner: Using R and Data in Applied Econometrics
    • Introduction to Statistics I
    • Introduction to Statistics II
    • Central Tendency
    • Dispersion and Dependence
    • Confidence Intervals
    • Hypothesis Testing
    • Data Visualization I
    • Data Visualization II
    • Distributions
    • Sampling Distributions
    • Simple Regression

  • Intermediate: Econometrics and Modeling Using R
    • Simple Regression
    • Multiple Regression
    • Issues in Regression
    • Interactions

    • Geographic Computation
    • Chi-Square Test
    • t-test
    • ANOVA
    • Regression
    • Wrangling and Visualizing Data

  • Advanced Modules
    • Classification and Clustering
    • Differences In Differences
    • Geospatial I
    • Geospatial II
    • Instrumental Variables I
    • Instrumental Variables II
    • Large Language Model APIs (Python)
    • Linear Differencing
    • Training LLMS
    • Sentiment Analysis Using LLMs (Python)
    • Transcription (Python)
    • Vocalization (Python)
    • Word Embeddings (Python)
    • Word Embeddings (R)
    • Panel Data
    • Synthetic Controls
Categories
All (16)
2SLS (2)
AI (1)
CRS (2)
LLMs (1)
Microsoft Azure text-to-speech (1)
OpenAI (1)
PyTorch (1)
R (11)
S2 (1)
Whisper (1)
advanced (14)
audio transcription (1)
before-after estimator (1)
causality (3)
comparison measure estimator (1)
continuous treatment (1)
cosine similarity (2)
cross-section data (1)
diarization (1)
difference in differences (3)
distance (1)
econ 425 (1)
econ 495 (1)
econ 499 (1)
elbow plot (1)
event study (1)
fine-tuning (1)
fixed effects (1)
gTTS (1)
geospatial (2)
hedonic (1)
instrumental variables (2)
k-means clustering (1)
large language models (2)
linear differencing (1)
mozilla TTS (1)
natural language processing (2)
ols (1)
p-value (1)
panel data (1)
parallel trends (1)
pooling (1)
preprocessing (1)
pricing (1)
python (5)
random effects (1)
regression (5)
sentiment-analysis (1)
shapefile (2)
stargazer (1)
synthetic control (1)
t-test (1)
text-to-speech (1)
trade (1)
treatment timing (1)
triple difference estimator (1)
vector (2)
vectors (2)
visualization (1)
vocalization (1)
word embeddings (2)
word2vec (2)

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…
4 Jun 2024

3.2.1 - Advanced - Instrumental Variables
An introduction to estimating causal effects with instrumental variables on Jupyter and R.
10 Jun 2024

3.2.2 - Advanced - Instrumental Variables 2
An introduction to estimating causal effects with instrumental variables on Jupyter and R.
3 Jun 2024

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…
26 Jul 2024

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…
24 Aug 2024

3.5.1 - Advanced - Geospatial Analysis
This notebook introduces geospatial analysis with vector data in R. We go over basic geospatial objects and operations.
24 Jun 2024

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.
24 Jun 2024

4.1 - Advanced - Classification and Clustering
This notebook introduces the classification and clustering models, especially for economic and sociological datasets.
18 Oct 2022

4.2 - Advanced - Introduction to Sentiment Analysis
This notebook explains how to perform basic sentiment analysis and Reddit web scraping using R.
4 Jul 2024

whisper audio

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.
7 Aug 2024

4.6 - Advanced - Fine-Tuning Large Language Models for Sentiment Analysis
An introduction to fine-tuning LLMs using BERT, in Python.
29 Jul 2024
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Classification and Clustering
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