Analysis updated 2026-05-18
Compare average student-teacher ratios across all twelve Berlin districts.
Look up how a specific district's schools deviate from the Berlin-wide average.
Download filtered school staffing data as a CSV file for further analysis.
Identify the ten schools with the highest student-teacher ratios in the program.
| keremtatlisarap/berlin-startchancen-dashboard | 0xhassaan/nn-from-scratch | 3ks/embedoc | |
|---|---|---|---|
| Stars | 0 | 0 | — |
| Language | Python | Python | Python |
| Last pushed | — | — | 2023-06-08 |
| Maintenance | — | — | Dormant |
| Setup difficulty | easy | moderate | hard |
| Complexity | 2/5 | 4/5 | 1/5 |
| Audience | pm founder | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Needs the Berlin open data files and a Streamlit environment to run locally.
The Berlin Startchancen Dashboard is a web application that looks at the ratio of students to teachers in Berlin schools taking part in the Startchancen Program, Germany's largest education funding initiative. The program directs money from the federal government and the states into schools that serve a high number of socially disadvantaged students. The person behind this project believes that money alone does not guarantee good education, and that how many teachers are available per student also shapes how well a school can support its students. This dashboard was built to check whether there are gaps in teacher staffing across Berlin's Startchancen schools, and whether those gaps line up with existing social inequality between districts. The application pulls in official data published by the city of Berlin and presents it through two views. The first view gives a citywide overview, showing the total number of schools, students, and teachers in the program, along with a bar chart comparing the average student to teacher ratio across all twelve Berlin districts, plus a ranking of the ten schools with the highest ratios. The second view lets a user pick a single district and see how it compares to the Berlin average, including a breakdown of teacher gender distribution and a data table that can be filtered and downloaded as a CSV file. The project is built with Python, using Pandas for data handling, Plotly for the charts, and Streamlit for the web interface, with additional libraries for reading Excel files and HTML data. The author notes that generative AI was used to help write and debug the code, but that combining the datasets and checking the results for accuracy was done by hand. All the data comes from Berlin's public open data portal and is released under open government licenses that allow reuse and modification. A live version of the dashboard is linked in the README.
A dashboard that compares student-to-teacher ratios across Berlin schools in a national education funding program, to spot staffing gaps.
Mainly Python. The stack also includes Python, Pandas, Plotly.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
Verify against the repo before relying on details.