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All Modules

This section contains all materials and case studies to support a variety of classes. Filter by topic using the categories on the right. Filter by class by choosing the appropriate class category.

Modules

0.1 - Introduction to JupyterNotebooks

Welcome to COMET! This is the very first notebook most of you will do, and it introduces you to some basics of Jupyter and using this project. Have fun!

0.2 - Introduction to R

This notebook introduces you to some fundamental concepts in R. It might be a little complex for a start, but it covers basically all of the fundamental syntax you need to…

0.3.1 - Introduction to Data in R - Part 1

This notebook introduces you to data in R, primarily using the tidyverse set of packages. It includes basic data curation and cleaning, including table-based inspection…

0.3.2 - Introduction to Data in R - Part 2

An introduction to analyzing data using tidyverse and dplyr in R including workflows for loading, merging, cleaning and visualizing data.

01 - Jupyter and Stata

This notebook explains how to set up Jupyter, add a Stata Kernel, and connect to COMET. It is the first step to take when embarking on a research project.

01 - Setting Up Jupyter and R

This notebook explains how to set up Jupyter, add an R Kernel, and connect to COMET. It is the first step to take when embarking on a research project.

01 - Setting up PyStata for your Windows computer

This notebook explains how to set up Jupyter, add a PyStata Kernel, and connect to COMET. It is the first step to take when embarking on a research project.

02 - Working with Do-Files

This notebook covers how to write and work with Stata do-files. We go over how to create a do-file, commenting, and generating log-files.

02 - Working with Do-Files

This notebook covers how to write and work with Stata do-files. We go over how to create a do-file, commenting, and generating log-files.

02 - Working with R Scripts

This notebook covers how to write and work with R scripts. We go over how to create a script, commenting, and preparing our R session.

03 - R Essentials

This notebook dives into a few essentials commands in R, including types of data, how to explore our data, and some basic functions.

03 - Stata Essentials

This notebook dives into a few essentials commands in Stata, including describe, summarize, loops, and help.

03 - Stata Essentials

This notebook dives into a few essentials commands in Stata, including describe, summarize, loops, and help.

04 - Opening Datasets

This notebook explains how to load, view, and clean data. We go over importing and previewing our data, as well as preparing the data for analysis.

04 - Working with Locals and Globals

This notebook explains how to create and use locals and globals.

04 - Working with Locals and Globals

This notebook explains how to create and use locals and globals.

05 - Generating Variables

In this notebook, we go over how to create variables. We look into how creating dummy variables works, as well as how to create variables using mathematical expressions.

05 - Opening Data Sets

This notebook explains how to load, view, and clean data. We go over importing and previewing our data, as well as preparing the data for analysis.

05 - Opening Datasets

This notebook explains how to load, view, and clean data. We go over importing and previewing our data, as well as preparing the data for analysis.

06 - Conducting Within Group Analysis

In this notebook, we go over how to create variables. We look into how creating dummy variables works, as well as how to create variables using mathematical expressions.

06 - Generating Variables

In this notebook, we go over how to create variables. We look into how creating dummy variables works, as well as how to create variables using mathematical expressions.

06 - Generating Variables

In this notebook, we go over how to create variables. We look into how creating dummy variables works, as well as how to create variables using mathematical expressions.

07 - Combining Data Sets

This notebook explains how to append and merge data sets.

07 - Conducting Within Group Analysis

In this notebook, we look at within-group analysis. We see how to summarize data for subgroups, how to generate new variables among subgroups, and how to reshape out data.

07 - Conducting Within Group Analysis

In this notebook, we look at within-group analysis. We see how to summarize data for subgroups, how to generate new variables among subgroups, and how to reshape out data.

08 - Combining Datasets

This notebook explains how to append and merge data sets.

08 - Combining Datasets

This notebook explains how to append and merge data sets.

08 - Creating Meaningful Visuals

This notebook goes over how to make all sorts of visuals. We look at different types of graphs, like scatter plots and histograms, exporting figures, and how to edit the…

09 - Combining Graphs

This notebook explains how to combine graphs.

09 - Creating Meaningful Visuals

This notebook goes over how to make all sorts of visuals. We look at different types of graphs, like scatter plots and histograms, exporting figures, and how to edit the…

09 - Creating Meaningful Visuals

This notebook goes over how to make all sorts of visuals. We look at different types of graphs, like scatter plots and histograms, exporting figures, and how to edit the…

1.0.0 - Beginner - Introduction to Statistics

This notebook introduces you to some fundamental statistics and basic probability concepts. It is designed to cover introductory statistics material taught in Foundations of…

1.0.1 - Beginner - Introduction to Statistics using R

This notebook is an introduction to basic statistics using Jupyter and R, and some fundamental data analysis. It is a high-level review of the most important applied tools…

1.1 - Beginner - Introduction to Central Tendency

This notebook is an hands-on introduction to the concepts of Central Tendency at the beginner level using R. It is meant for undergraduates with no or very little prior…

1.1.1 - Beginner - Central Tendency

This notebook is an introduction to basic statistics using Jupyter and R, and some fundamental data analysis.

1.2 - Beginner - Dispersion and Dependence

In this notebook we explore how data is spread out, and what that means for its interpretation. This includes both how individual values may vary, and how values may…

1.3.2 - Beginner - Confidence Intervals

What does it mean to be confident in a statistical estimate? This notebook is an introduction to confidence, and confidence intervals - especially in the context of the…

1.4.2 - Beginner - Hypothesis Testing

What is a hypothesis? How do we test it? This notebook introduces hypothesis testing in two different ways, outlining the connection between them and how we can use this…

1.5.1 - Beginner - Introduction to Data Visualization I

How do we make visualizations of data? This notebook uses the ggplot packages to help create impactful data visualizations using R. We will also discuss the rationale…

1.5.2 - Beginner - Introduction to Data Visualization II

How do we make visualizations of data? This notebook uses the ggplot packages to help create impactful data visualizations using R. This builds on the first notebook…

1.6 - Beginner - Distributions

This notebook introduces the idea of a statistical distribution, including its properties and some commonly used functions associated with it. We also discuss the shape of…

1.8 - Beginner - Sampling Distributions

What is a sampling distribution? This notebook tackles, using a simulation-based approach, the most complex idea in this course. It is code-heavy, but most of it is in the…

10 - Combining Graphs

This notebook explains how to combine graphs.

10 - Combining Graphs

This notebook explains how to combine graphs.

10 - Conducting Regression Analysis

This notebook goes over the theory surrounding linear regressions, as well as how to implement univariate and multivariate regressions in R. We dig into coefficient…

11 - Conducting Regression Analysis

This notebook goes over the theory surrounding linear regressions, as well as how to implement univariate and multivariate regressions in Stata. We dig into coefficient…

11 - Conducting Regression Analysis

This notebook goes over the theory surrounding linear regressions, as well as how to implement univariate and multivariate regressions in Stata. We dig into coefficient…

11 - Exporting Regression Output

Here, we work on how to export our regression results. We introduce some packages to make our regression results look professional and to present our coefficients in a…

12 - Dummy Variables and Interactions

In this notebook, we dive into dummy variables and interaction terms. We look at how to include them in our regressions and how to interpret their coefficients.

12 - Exporting Regression Output

Here, we work on how to export our regression results. We introduce some commands to make our regression results look professional and to present our coefficients in a…

12 - Exporting Regression Output

Here, we work on how to export our regression results. We introduce some commands to make our regression results look professional and to present our coefficients in a…

13 - Good Regression Practice

This notebook covers some good practices that should be implemented when we perform regression analysis. We look at how to handle outliers, multicollinearity…

13 - Using Dummy Variables and Interactions

In this notebook, we dive into dummy variables and interaction terms. We look at how to include them in our regressions and how to interpret their coefficients.

13 - Using Dummy Variables and Interactions

In this notebook, we dive into dummy variables and interaction terms. We look at how to include them in our regressions and how to interpret their coefficients.

14 - Good Regression Practices

This notebook covers some good practices that should be implemented when we perform regression analysis. We look at how to handle outliers, multicollinearity…

14 - Good Regression Practices

This notebook covers some good practices that should be implemented when we perform regression analysis. We look at how to handle outliers, multicollinearity…

14 - Panel Data Regressions

In this notebook, we go over panel data. We look into what it is, how to run regressions with panel data, as well as fixed and random-effects models. We finish by looking at…

15 - Difference-in-Differences Analysis

This notebook introduces difference-in-difference analysis. We look the assumptions required to perform this type of analysis, how to run the regressions, how to run event…

15 - Panel Data Regressions

In this notebook, we go over panel data. We look into what it is, how to run regressions with panel data, as well as fixed and random-effects models. We finish by looking at…

15 - Panel Data Regressions

In this notebook, we go over panel data. We look into what it is, how to run regressions with panel data, as well as fixed and random-effects models. We finish by looking at…

16 - Differences-in-Differences Analysis

This notebook introduces difference-in-difference analysis. We look the assumptions required to perform this type of analysis, how to run the regressions, how to run event…

16 - Differences-in-Differences Analysis

This notebook introduces difference-in-difference analysis. We look the assumptions required to perform this type of analysis, how to run the regressions, how to run event…

16 - Instrumental Variable Analysis

This notebook introduces instrumental variable analysis. We look the conditions that must be satisfied to perform an IV analysis, how the two-stage-least-squares approach…

17 - Instrumental Variable Analysis

This notebook introduces instrumental variable analysis. We look the conditions that must be satisfied to perform an IV analysis, how the two-stage-least-squares approach…

17 - R Workflow Guide

This notebook is here to help us organize our files when conducting large-scale research. We talk about workflow management, coding style, and cloud storage.

17: Instrumental Variable Analysis

This notebook introduces instrumental variable analysis. We look the conditions that must be satisfied to perform an IV analysis, how the two-stage-least-squares approach…

18 - Stata Workflow Guide

This notebook is here to help us organize our files when conducting large-scale research. We talk about workflow management, coding style, and cloud storage.

18 - Stata Workflow Guide

This notebook is here to help us organize our files when conducting large-scale research. We talk about workflow management, coding style, and cloud storage.

2.1 - Intermediate - Introduction to Regression

An introduction to simple regression using Jupyter and R, with an emphasis on understanding what regression models are actually doing. Computation is using OLS.

2.2 - Intermediate - Multiple Regression

An introduction to multiple regression using Jupyter and R, connecting simple to multiple regression. We also discuss some important concepts, including control variables.

2.4 - Intermediate - Issues in Regression

What are the key issues with a regression model? This notebook discusses collinearity, heteroskedasticity, and model specification.

2.5 - Intermediate - Interactions and Non-linear Terms

How do we specific non-linear models? Why would we want to do so? This notebook covers non-linear and interaction models, including marginal effects and related issues.

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.

Projects - Example Project for ECON 325

Let’s put it all together! This notebook is an example of what a “final project” might look like in ECON 325. It summarizes and uses all of the empirical skills and R…

Projects - Example Project for ECON 326

Let’s put it all together! This notebook is an example of what a “final project” might look like in ECON 326. It summarizes and uses all many of the empirical skills and R…