Data4Justice Accelerator Program

Reserve a place in our upcoming winter session!

Kickstart your social justice research agenda with QSIDE!

The QSIDE Institute is proud to announce the launch of the Data4Justice Accelerator, a training and professional development program aimed at propelling activists interested in building skills to drive data-driven social justice research and evaluation work.

What is the Data4Justice Accelerator?

The QSIDE Accelerator is a 5-week-long program designed to help individuals learn how to design and execute social justice research and evaluation projects.

What will I learn?

As a participant in the Data4Justice Accelerator, you will learn how to:

  • find and collect data using APIs and data scraping;
  • clean and organize data using RStudio & R;
  • use data science methodologies to uncover meaningful insights and relationships in your data;
  • build data stories and effective visualizations to showcase results and inform policy and sway public opinion; and,
  • turn qualitative into quantitative data using Natural Language Processing (NLP).

Each participant will collaborate on a shared research project that advances justice and equity, and leave with a well-developed idea for future research, and newfound skills to make it happen.

Who should apply?

Anyone interested in learning how to conduct social justice data science research is welcome to apply. A basic understanding of statistics may prove useful, but is not required. This program assumes that individuals are coming from widely different and diverse backgrounds with widely different types of expertise and skillsets, and is designed with a beginner mindset. All who can commit to participating fully are welcome to apply; however, space is limited.

What are the obligations and the time commitment?

As part of the Data4Justice Accelerator participants agree to:

  • Participate fully in the 5-week winter 2026 intensive program, which will run from January 11th, 2026 – February 14th, 2026. This includes:
    • Reading and working through 8-10 short chapters per week.
    • 2 weekly discussion posts.
    • Submission of data science output in order to determine quality, correctness, and an understanding of the material.

We understand many people have personal and financial obligations that run parallel to this accelerator. This program is self-paced, with office hours by appointment.

To complete this course, participants must have access to a working computer and reliable internet.

Tuition

Participation in the Data4Justice Accelerator is $2500 for general registration or $1250 for currently enrolled students with valid student ID. Need-based fee waivers are available upon request. If you would like to participate in the Data4Justice Accelerator and are in need of financial assistance, please reach out to tyrone.bass@qsideinstitute.org.

Registration is live, but spots are limited!

Curriculum Outline

 Module 1: Using Generative AI (Or Not)

  • Module 1: Overview 
  • Lesson 1: How LLMs Work
  • Lesson 2: Ethical Considerations and Risks
  • Lesson 3: Effective Prompting Strategies for Data Tasks
  • Lesson 4: Review of Key Concepts

Module 2: Getting Started With R and RStudio

  • Module 2: Overview
  • Lesson 5: Installing and Launching RStudio Copy
  • Lesson 6: Organizing Your Work
  • Lesson 7: Understanding the RStudio Interface
  • Lesson 8: Paths and Working Directories
  • Lesson 9: Installing and Loading Packages
  • Lesson 10: Running and Understanding Basic Commands
  • Lesson 11: Feeling Comfortable in RStudio
  • Lesson 12: Common Troubleshooting Steps
  • Lesson 13: Review of Key Concepts

 Module 3: Acquiring Data

  • Lesson 14: Inspecting Data in R 
  • Lesson 15: Reading Delimited Text Files
  • Lesson 16: Reading Excel Workbooks
  • Lesson 17: Reading RDS and RData Files
  • Lesson 18: Using an API to Acquire Data
  • Lesson 19: Web Scraping
  • Lesson 20: Review of Key Concepts

 Module 4: Cleaning Data

  • Lesson 21: Writing Clear Code
  • Lesson 22: Data Types in R
  • Lesson 23: Missing Data
  • Lesson 24: Duplicate Data
  • Lesson 25: Text Cleaning
  • Lesson 26: Data Validation
  • Lesson 27: Cleaning Up Variable Names
  • Lesson 28: Review of Key Concepts

 Module 5: Wrangling Data

  • Lesson 29: Tidy Data
  • Lesson 30: Reshaping
  • Lesson 31: Filtering Data 
  • Lesson 32: Grouping and Summarizing Data
  • Lesson 33: Joining Datasets
  • Lesson 34: Review of Key Concepts

 Module 6: Visualizing Data

  • Lesson 35: Foundational Principles of Good Visualizations
  • Lesson 36: Common Visualization Types
  • Lesson 37: Getting Started with ggplot2
  • Lesson 38: Common Visualization Pitfalls
  • Lesson 39: Review of Key Concepts

 Module 7: Working with Census Data

  • Lesson 40: Working with Census Data
  • Lesson 41: Critiquing the Census
  • Lesson 42: Getting Started with Tidycensus
  • Lesson 43: Finding and Understanding Census Variables 
  • Lesson 44: Retrieving Census Data
  • Lesson 45: Review of Key Concepts

 Module 8: Making Maps

  • Lesson 46: Map Types and Their Uses
  • Lesson 47: Planning Your Map
  • Lesson 48: Map Projetions
  • Lesson 49: Simple Maps and Mapping Census Data
  • Lesson 50: Building a Map from Raw Data
  • Lesson 51: Case Study: Free and Reduced Lunch in Cook County, Illinois
  • Lesson 52: Combining Data Sources for Further Analysis
  • Lesson 53: Review of Key Concepts

 Module 9: Natural Language Processing

  • Lesson 54: Getting Prepared
  • Lesson 55: Text Preparation
  • Lesson 56: Exploring Word Frequencies
  • Lesson 57: Sentiment Analysis
  • Lesson 58: Topic Modeling
  • Lesson 59: Topic Modeling with N-Grams
  • Lesson 60: Review of Key Concepts

 Module 10: Summary and Review

  • Lesson 61: Review of Key Concepts