Data Analysis for Policy Making: Voting & Redistricting in Georgia
Use real Georgia voting and redistricting data to understand how analysts turn raw data into policy arguments. Six 20-minute modules. One final Policy Data Brief.
MdR Palacios
Maria del Rosario Palacios is an author, data engineer, policy expert, and civic technology builder with more than 12 years of multilingual data and community work. Rosario previously served as Training Manager at Generation Data, where she launched the first Spanish-language Intro to Progressive Data course and taught data visualization, WhatsApp outreach, and community data practice to organizers across the South. MdR has published three books, including Project Management for Xingones. She has been a 10-year certified train the trainer instructor with UGA's Fanning Institute for Leadership. Her policy data work spans redistricting analysis, voter file auditing, and civic technology infrastructure for movement organizations.
Policy data analysis
Translates raw voter registration, precinct, and redistricting data into clear policy arguments that hold up under scrutiny.
Civic data infrastructure
Builds reusable pipelines and documentation frameworks for voter files, election results, and census block data.
Georgia redistricting work
Supported analysis of legislative district maps and demographic shifts across Georgia counties, including post-Callais landscape tracking.
Generation Data
Four years building data curriculum for organizers, including intro progressive data, visualization, and election data modules.
What you will do
You will play the role of a policy data analyst supporting a civic organization. The course centers on a single throughline: Georgia's voting and redistricting landscape, including the 2026 Callais v. Landry Supreme Court ruling that reshaped how analysts must approach majority-minority district analysis.
Read real data structures
Work with voter file fields, precinct tables, and redistricting datasets as they actually appear.
Apply the policy cycle
Move from issue identification → analysis → recommendation → monitoring in one sitting.
Practice with checkpoints
Each module ends with a short quiz and an action that feeds the final brief.
Produce a Policy Data Brief
A one-page artifact with a research question, key data source, finding, and policy recommendation.
Modules in this course
Six 20-minute modules. Each builds one section of your final Policy Data Brief.
The Data-Driven Policy Cycle
Understand why data matters in policy, learn the four-stage policy cycle, and choose the organizing question your brief will answer.
Begin Module 1 MODULE 2 · 20 MINReading Voting & Registration Data
Navigate Georgia's voter file, calculate turnout rates, read precinct-level tables, and identify common data quality problems.
Begin Module 2 MODULE 3 · 20 MINRedistricting Data & Callais v. Landry
Learn what redistricting data contains, how the Gingles framework worked, and what changed for data analysts after the April 2026 Supreme Court ruling.
Begin Module 3 MODULE 4 · 20 MINDisparate Impact Analysis
Apply racially polarized voting analysis, geospatial proximity modeling, and ballot rejection rate comparisons to real Georgia scenarios.
Begin Module 4 MODULE 5 · 20 MINBuilding Your Policy Finding
Turn data into a defensible claim: select the right comparison, write in plain language, sanity-check your numbers, and avoid overclaiming.
Begin Module 5 MODULE 6 · 20 MIN · FINALWriting the Policy Data Brief
Assemble the five sections of your Policy Data Brief: research question, data source, key finding, methodology note, and policy recommendation.
Begin Module 6Final artifact: Policy Data Brief
A one-page brief built section by section across all six modules. By Module 6 you will have most of it written already.
| Section | Built in | What it contains |
|---|---|---|
| Research question | Module 1 | One sentence in plain language: what policy question are you answering? |
| Data source | Module 2 | Name the dataset, the agency that published it, and the relevant fields. |
| Redistricting context | Module 3 | One paragraph: what legal or policy framework applies to your question? |
| Key finding | Module 4 | Two to three sentences with one number that matters. |
| Methodology note | Module 5 | One sentence explaining how you calculated or compared the data. |
| Policy recommendation | Module 6 | One actionable recommendation grounded in your finding. |
How the course works
| Design need | How it appears in this course |
|---|---|
| Real-world grounding | All examples use Georgia voter files, precinct tables, and district maps. |
| Policy relevance | The post-Callais v. Landry (2026) landscape is used throughout as the live policy context. |
| Adaptive feedback | Checkpoints include correct and incorrect answer explanations. |
| Cognitive load reduction | One concept per page, one action per module that feeds the final brief. |
| Accessible to non-programmers | No code required. Analysis is done by reading tables, interpreting numbers, and writing plain-language summaries. |
| Practical output | You leave with a real Policy Data Brief you can adapt for your organization. |
Who this is for
- Organizers and campaign staff who receive data from analysts and want to understand it better.
- Policy researchers who are new to working with voter files and election data.
- Civic technologists building tools that touch redistricting or voting rights data.
- Anyone who wants to understand what changed after Callais v. Landry from a data perspective.
Prerequisites: Comfort reading a spreadsheet. No programming required.