The Urban Data Science Corps (UDSC) is a summer internship program offered by the School of Cities. UDSC presents an opportunity to undergraduate students at U of T who are interested in data and data science to work with organizations in the public and non-profit sectors focused on urban issues.
Summer interns work with organizations to help them build their capacity to collect, manage, and analyze their own data. Students will gain experience in, and may be expected to have some knowledge of:
- Data cleaning and processing
- Database management
- Exploratory data analysis
- Descriptive statistics
- Data visualization
- Statistical and regression analysis
- Excel, Python, R and/or SQL
- QGIS, ArcGIS, or other GIS programs for spatial analysis
- Creating interactive maps and dashboards
All internships are paid and run for twelve weeks, from May through August.
How it works
- Undergraduate students from across U of T apply to UDSC during the winter semester. Students review the available work placements and indicate their top 4 choices. Applicants must have a solid understanding of the technical requirements listed in the job descriptions.
- Prospective interns’ applications are reviewed by one or more members of the School of Cities’ selection committee, and are matched with a participating organization by the School.
- A faculty mentor is assigned to every participant, and students must attend monthly check-ins with their mentor throughout their internship. Mentors also schedule weekly office hours throughout the summer for UDSC meetings.
- A mandatory 3-day Data Bootcamp is held for all interns in May, to introduce and review the skills they will be expected to deploy during their internship.
- Students have the opportunity to showcase the project they worked on at an event held in August.
- The School of Cities coordinates professional development sessions throughout the summer with the Data Sciences Institute.
Summer 2026 UDSC internships
Applications for this program are now closed.
Arts Etobicoke is a Local Arts Service Organization (LASO) that delivers community arts programs across Etobicoke. Currently, the organization does not collect consistent demographic information from program participants, which limits its ability to understand who it serves and demonstrate program impact. As the City of Toronto increases reporting requirements for LASOs, particularly around demographic reach and equity, Arts Etobicoke is seeking support in developing a practical, ethical, and accessible approach to data collection and analysis. This project invites students to design a demographic data collection framework, analyze existing participation patterns, and benchmark Arts Etobicoke against similar arts organizations in Toronto.
The project objectives are: Develop a demographic data collection framework; Research best practices for collecting demographic information in community arts and public-serving organizations; Consider ethics, consent, cultural safety, accessibility, privacy, and participant comfort; Recommend a standardized set of demographic questions and a simple, implementable collection method; Map and Visualize Community Participation Analyze Arts Etobicoke’s past participation records (e.g., neighbourhood or postal code data); Create visualizations or maps showing participant reach across Etobicoke; Identify areas with strong engagement and areas where access to programs may be limited; Benchmark against other LASOs in Toronto review data, impact reports, and program information from other LASOs; Identify common metrics, KPIs, or demographic indicators used across the sector; Compare approaches and highlight potential opportunities for alignment or improvement.
The Canadian Urban Institute (CUI) is looking for a Data Intern who will collect and analyze data from public and proprietary sources and who can help prototype new functionality and data visualizations for the Main Street Metrics dashboards. They will conduct research, generate best practice findings and develop policy options as well as support work with Applied Solutions Lab and program staff.
Current and recent projects include:
- Measuring main street dashboards
- Transit-oriented development on main streets
- The future of Canadian downtowns
- Municipal economic development and main street strategies
- Major infrastructure construction impact on neighbourhoods and mitigation
Qualifications:
- Ability to work independently and with a team.
- Strong data and web mapping/development skills are highly desirable
- Currently enrolled in Urban Planning, Public Policy, Urban Design, Architecture or similar program with focus on urban issues
- Strong academic performance and exemplary research and writing skills.
- Excellent communication skills.
The Urban Data Scientist (Intern) will work on applied urban data science tasks that contribute to Markham’s emerging simulation and scenario modeling capabilities. The intern will clean, structure, and analyze datasets related to development workflows, land use, mobility, and infrastructure, and conduct geospatial processing to prepare data for simulation environments.
Responsibilities include exploratory data analysis, geospatial analysis (GIS), visualization/dashboard development, and documentation of reproducible workflows (e.g., notebooks, scripts). A key focus of this role is supporting the data foundations for simulation models—such as development application workflow simulations, mobility scenarios, infrastructure demand forecasting, or early-stage digital twin work. The intern will collaborate with planning and innovation staff to translate data into operational and policy insights, identify process inefficiencies, and support evidence-based planning. The role provides practical exposure to modern data science methods, municipal data structures, and digital transformation in city-building.
The role will assist the Active Transportation team in analyzing, visualizing and reporting on cycling and pedestrian trip patterns within Mississauga, utilizing various data sources but in particular the City’s existing set of cycling and pedestrian counts, collected using dedicated counters for several years. There will also be opportunities for field work, data validation, coordination with the City’s shared micro-mobility program (shared e-bikes and e-scooters), and other duties as assigned.
The student will assist with supporting a number of 2026 portfolio objectives, with a heavy emphasis on data analytics. These include:
- MTSAs and TTC stations
- The Citywide Parking Strategy
- A Toronto Parking Authority third-year trend analysis (utilization and financial performance) to unlock City building opportunities
- Service planning for the new EMS operating model, analysis for the AOCC Expansion Framework using a range of operational and geospatial data
- GIS mapping and data analytics to support various real estate requests for location-based analysis
The student intern will be a part of the Measurement, Evaluation, and Learning (MEL) team. The role consists of three main areas:
- Supporting the Manager of MEL in analysis of data gathered as part of climate resilience and wellbeing research conducted at Evergreen Brick Works
- Visualizing the results of the research activities via presentations, infographics and reports
- Assisting the Manager in building staff members’ capacity in data visualization and statistical analysis
- Supporting creation of Key Performance Indicator dashboards in collaboration with different teams at the Organization
- Gather and collect data for different research activities as necessary
This role will also provide a valuable practice and learning opportunity for the student to enhance their skills in data analysis, visualization, and team collaboration with real life examples.
ICLEI Canada is currently developing Climate Insight, a platform that will serve as the go-to destination for relevant, actionable data and information on low-carbon, resilient infrastructure and housing. Climate Insight is being built to empower communities across Canada with the information they need to build low carbon, resilient infrastructure and housing. One of the main pillars of Climate Insight is a geospatial map which combines climate data, social vulnerability indices, and infrastructure data from across Canada.
The team is looking to expand its geospatial data offerings to provide more data and tools to community practitioners.
Final Proposed deliverable(s): Specific deliverables may include:
- Review the list of potential geospatial data and support making recommendations on which should be pursued as a priority. Based on identified priorities, download/access data sets and complete required preparation for the data to be added to the Climate Insight map.
- As required, work with the Climate Insight development team to incorporate the new data layers and complete any required visualization enhancements.
- Create documentation for the data sources and analysis methods (if any) that were used to create the various datasets.
Preferred student skills, knowledge, experience and/or qualities:
- Strong analytical, research and conceptual skills
- Ability to work carefully and accurately with complex information
- Ability to multi-task in a fast-paced, ever-changing and dynamic environment
- Data cleaning and processing
- Exploratory data analysis Data visualization QGIS, ArcGIS, or other GIS programs for spatial analysis
- Creating interactive maps and dashboards Excel, Python, R and/or SQL
- Familiarity with climate change mitigation and adaptation principles would be an asset
ICLEI office location: 401 Richmond St. West, Suite 204 (Richmond St W and Spadina Ave) Closest TTC Station: Osgoode Mode of Engagement(s): Weekly check-ins with project supervisor; weekly team meetings Check-ins with Operations Manager as needed.
The ICLEI office offers hot-desk work location; staff generally attend the office 2 days per week. Schedule and work location will be set in discussion with the project supervisor and project intern.
- Learn about different ways that cycle shops in Toronto and across Canada handle repair inputs
- See if there is an existing best practice that can be adapted or created
- Analyze existing data sets to determine the best way for an AI website to parse and visualize cycle shop repair data confidentially
EHON (Expanding Housing Options in Neighbourhoods) is a broad policy and research initiative led by the City of Toronto, aimed at increasing the availability and diversity of housing types—particularly “missing middle” housing such as multiplexes, laneway suites, and garden suites—in established neighbourhoods. The program addresses housing affordability and choice by removing barriers and enabling new forms of gentle density, while supporting the City’s overall goals for inclusive, sustainable communities and complete neighbourhoods. EHON involves extensive public engagement, policy analysis, and ongoing monitoring to ensure that implemented changes contribute positively to Toronto’s urban fabric.
The EHON internship position will support the development and refinement of advanced data science workflows to enhance research and policy evaluation efforts for the EHON program. This is an excellent opportunity to gain hands-on experience with urban analytics, policy research, and applied data science in a municipal context.
Responsibilities may include:
• Design and automate data pipelines using Python to efficiently collect, process, and visualize urban development and housing data relevant to EHON initiatives.
• Contribute to the enhancement and recalibration of housing uptake models by integrating new datasets, refining analytical methodologies, and interpreting results for policy implications.
• Explore and apply advanced machine learning techniques to optimize data analysis, validation, and scenario testing, supporting evidence-based recommendations and continuous program improvement.
The data analyst will work with open data as well as other data obtained through freedom of information requests and other sources to find patterns and insights that can support the newsroom in identifying new stories as well as in creating data visualizations.
These may include interactive maps, charts, and searchable tables to help readers better understand the issues reported on.
The data analyst is expected to use standard statistical methods and software to produce the work products.
The Community Data Visualization & Mapping Intern will support the Thorncliffe Park Community Hub by compiling, analyzing, and visualizing neighborhood and service-related data to produce user-friendly, interactive maps and dashboards.
The primary outcome of this internship is the development of a comprehensive, accessible community map and data profile of the Hub’s neighbourhood, to be used for public information, service planning, and the future projects.
Working in a community-based setting, the intern will apply data science, GIS, and visualization skills to real-world urban challenges, helping translate complex data into practical tools for staff, partners, and residents.
Key Responsibilities:
- Community Demographic Profiling Integrate existing Hub service data and publicly available datasets (e.g., Census, municipal open data, community datasets).
- Work with the team to develop a comprehensive demographic and service profile of the Thorncliffe Park neighbourhood.
- Work with the team for actionable insights to inform Hub planning, service delivery, and emergency preparedness initiatives.
- Community Mapping and Spatial Analysis Use GIS tools (QGIS and/or ArcGIS) to map neighbourhood assets, services, resources, and community networks.
- Create visual, interactive maps that communicate how the Hub serves the surrounding neighbourhood.
- Support the development of mapping assets that can be integrated into Hub communications, planning, and processes.
- Data Visualization and Interactive Dashboards Design interactive dashboards to visualize program reach, space utilization, volunteer and community engagement, and resource distribution.
- Present data in clear, accessible formats suitable for staff, partners, and community stakeholders.
Key responsibilities include:
- Clean, organize, and integrate datasets (mobility, event, social media, and Toronto Arts’ internal data)
- Assist with geospatial and descriptive analysis to identify neighbourhoods with low cultural participation
- Support classification of events by artform, discipline, and sub discipline
- Contribute to testing and improving the interactive mapping tool
- Produce clear visuals and short summaries to support storytelling and decision making
What you’ll learn:
- Hands on data wrangling, modeling, and geospatial analysis
- How cultural participation patterns relate to equity, urban planning, and city building
- How public facing civic data tools are built, tested, and communicated
- Exposure to Toronto’s arts ecosystem and cultural policy landscape
Ideal candidate:
- Undergraduate student in Data Science, Computer Science, Statistics, Geography, Urban Studies, or similar
- Comfortable with Python, R, or SQL; GIS experience is an asset
- Strong attention to detail and curiosity about urban equity and the arts
The Climate Data Intern will support West Neighbourhood House in exploring and documenting neighbourhood-level climate-related inequities affecting equity-denied populations in Toronto’s downtown west end, including seniors, persons with disabilities, racialized and Indigenous communities, and families with young children.
The intern will work closely with program and community engagement staff to:
1. Identify relevant questions, data needs, and lived-experience insights related to climate hazards such as extreme heat, housing quality, energy poverty, and climate-related health impacts
2. Source, clean, and analyze publicly available and organization-held datasets, apply appropriate data analysis and visualization methods based on their skills and training
3. Organize datasets, conduct exploratory analysis, create maps or visual summaries of neighbourhood-level trends, and document data limitations and gaps
4. Support the synthesis of quantitative findings with qualitative community knowledge to contribute to a Seasonal Climate Inequities report
This position is designed as a learning-oriented, applied data role. West Neighbourhood House will provide strong contextual, community, and policy guidance, and clear project objectives. This internship will allow the student to apply data science skills in a real-world, equity-focused setting while helping the organization build foundational data capacity to support our city-building, advocacy, and climate justice work.
Questions?
Feel free to contact us at education.sofc@utoronto.ca