Programming Languages

Python Data Visualization: Parts 1-2

October 1, 2024, 9:00am
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter notebook.

Python Fundamentals: Parts 4-6

October 1, 2024, 2:00pm
This three-part interactive workshop series teaches you intermediate programming Python for people with previous programming experience equivalent to our Python Fundamentals workshop. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

R Data Wrangling and Manipulation: Parts 1-2

October 1, 2024, 1:00pm
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.

Haas: R Fundamentals: Part 1

September 12, 2024, 4:00pm
This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Alex Stephenson

Senior Data Science Fellow
Political Science

I am a Ph.D. Student in the Travers Department of Political Science. My primary research interests are military organizations, policing, the determinants of political violence, and causal inference. I am also interested in creating tools to make software easier to use for non-technical political scientists.

R Fundamentals: Parts 1-4

August 20, 2024, 9:00am
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

R Copilot Assisted Coding Workshop

September 23, 2024, 3:00pm
This workshop provides a beginner-friendly introduction to coding with GitHub Copilot, a popular AI coding assistant. We will start from the basics so you can take advantage of AI assistants to improve your coding and avoid common pitfalls. First, we’ll cover how to install and set-up Visual Studio Code, a free code editor through which we will use GitHub Copilot. Then, we will go through the different features of GitHub Copilot and how to use them to help us code in R.

Python Data Wrangling and Manipulation with Pandas

August 22, 2024, 2:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

Python Fundamentals: Parts 1-3

August 19, 2024, 2:00pm
This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

TEST: Python Data Wrangling and Manipulation with Pandas

August 22, 2024, 2:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.