Registration is now open for Urban Data Analysis with Python, an in-person workshop series exploring how data can be used to better understand cities and urban change.
Through guided, hands-on learning, participants will build foundational data analysis skills in Python by finding and exploring publicly available urban data, learning to analyze and understand patterns in cities, and developing computational workflows for urban analysis.
The course is designed for learners who are new to Python and for beginners who want to deepen their Python knowledge. Registrants should have an interest in urban issues and be looking to build confidence in urban data analysis and Python in a supportive and applied environment.
The sessions will be held in person in the School of Cities office boardroom – located on the St. George campus of the University of Toronto – on August 10, 17, 24, and 31. They will run from 9.15 a.m. to 2.30 p.m. on each date. A light lunch will be provided.
The total cost for this series is CAD$200.00 (plus HST)
The course emphasizes practical urban data workflows using common Python tools such as Jupyter notebooks, pandas, and GeoPandas. Example datasets will include neighbourhood census demographic data, OpenStreetMap data, and datasets from municipal open data portals.
Each session will include:
- 2 hours of facilitated learning introducing Python and urban data concepts
- Short in-class applied learning exercises
- 2-hour working session devoted to the weekly assignment with facilitator and subject expert support
- One short assignment per week (for a total of 4 assignments)
Participants will work with fixed datasets and clearly defined tasks, allowing the instructors to provide technical support, ensure that everyone is working at a similar pace, and focus on communicating key skills.
The focus of each week will be:
- August 10: Introduction to Python and computational notebooks
- August 17: Analyzing tabular data with pandas
- August 24: Mapping and spatial analysis with GeoPandas
- August 31: APIs, OpenStreetMap, and building a workflow
The learning outcomes for the course will include the abilities to:
- Run and edit Python code in Jupyter notebooks
- Read, clean, and summarize tabular datasets
- Analyze and map spatial datasets using GeoPandas
- Access and process simple API and OpenStreetMap data
- Build small, reproducible workflows for urban data analysis
* Please note that participation in this workshop does not include a certificate of completion.
Facilitator
Aniket Kali is a Data Visualization Developer at the School of Cities, where he has published projects examining rail transit, urban activity, immigration, voting patterns, and rental housing. He is also the facilitator of the School’s Urban Data Analysis and Visualization microcredential. Aniket has an MSc and BSc in Computer Science from the University of Toronto.
