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 real data issues in the public and non-profit sectors and explore data science as a career path.
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 wrangling
- Database management
- Descriptive statistics
- Data visualization
- Statistical and regression analysis
- Excel, Python R and/or SQL languages
- ArcMap, QGIS, or other GIS programs for spatial analysis
- Salesforce or Quickbooks
All internships are paid and run for 12 weeks, from May through August.
Applications are currently closed
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 3 choices. Applicants must have a solid understanding of the technical requirements listed in the job descriptions.
- Prospective interns are interviewed 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 and T-CAIREM.
Summer 2023 UDSC internship opportunities
The Centre for Active Transportation (TCAT) is a project of the Toronto non-profit Clean Air Partnership that aims to build evidence and support for safer, more equitable streets for walking and cycling. Many American cities have analyzed the racial and demographic impacts of road violence. Those studies have found that people of colour, lower income residents, and older adults are disproportionately more likely to be seriously injured and killed by drivers. TCAT is looking for a data science intern to apply a similar demographic lens to road safety and traffic data in Toronto, analyzing the demographics of neighbourhoods that see the most fatal collusions, the impacts of Automated Speed Enforcement Cameras, and the parts of the city receiving the greatest road safety investments. The main data skills needed are in data visualization and mapping. organization and data structuring using spreadsheets such as Microsoft Excel and Google Sheets, for example. Programming tools that could help with such tasks would be an asset. Additional tasks might include assisting TCAT in its collaboration with the City of Toronto on specific projects, as needed. A background in transportation and policy evaluation would be an asset.
This project seeks to assess the impact of the changes on main streets in the Greater Toronto and Hamilton Area (GTHA). Main streets have typically been a focal point for urban society. They are places where people congregate for a wide variety of reasons. The aim of this research project is to better understand the varying degree to which main streets have been impacted by the pandemic and what interventions have had the greatest impact to support long-term equity and future-proof the recovery of our main streets. This team of interns will use location-based service (cell phone) data to:
- Develop and implement a methodology to measure and evaluate the impact of COVID on main streets in the GTHA by assessing their relative vitality before, during, and after the peak(s) of the COVID pandemic.
- Identify ‘types’ of main streets using multivariate regression and random forest classifiers that takes into consideration factors such as relative location, local socio-economic conditions, transportation, and local community assets.
- Use regression, cluster analysis and random forest classifiers to identify general trends in vitality across various types of main streets through the stages of the COVID pandemic.
Located in Toronto, Access Alliance Multicultural Health and Community Services (Access Alliance) provides services and addresses system inequities to improve health outcomes for the most vulnerable immigrants, refugees, and their communities. The main data problem interns can expect to solve is to organize, update and operate a PowerBI dashboard to manage Access Alliance’s corporate performance measurement indicators. Interns will be tasked with updating the dashboard that automates Access Alliance’s data reporting efficiently, categorized by different departments. Furthermore, interns will also work on existing spreadsheets to visualize data for reporting key performance measurement indicators. The main data skills needed are in data organization, visualization, and report automation using spreadsheets (e.g. Microsoft Excel and Google Sheet), for example. Programming skills, as well as dashboard development and report automation skills using PowerBI, are required. Additional projects might include assisting Access Alliance with specific tasks, as needed.
The Daily Bread Food Bank works towards long-term solutions to end hunger and poverty and runs innovative programs to support individuals living on low income and experiencing food insecurity. Daily Bread distributes fresh and shelf-stable food, and fresh-cooked meals to member agencies and food programs across Toronto and publishes the Who’s Hungry report – an annual survey that provides qualitative data and analysis about food and income insecurity in Toronto to all levels of government and sector stakeholders.
The Daily Bread’s Research & Advocacy (R&A) team produces regular research reports to support the call for eliminate food insecurity. The R&A team includes the Vice President, Research & Advocacy, a research analyst, and a policy and engagement specialist. Other students may also join the team during the contract. On-site or remote work is possible.
In the city of Toronto, poverty is concentrated along several spatial dimensions. The GIS analyst intern will collect and review potential data sources and produce maps across multiple dimensions, as determined with the R&A team. The new maps may be used by Daily Bread staff and its agencies in advocacy efforts, media, political outreach, grant applications and more.
Maximum City brings together a multidisciplinary team to provide learning and engagement about urban related topics, such as climate change, and is located in Toronto. The organization is looking for a data science intern to help with analysis, including spatial of new data, building on the previous study group that last year’s intern worked on, to better understand how the COVID-19 pandemic has affected how children and youth in Canada play and engage in physical activity. Within this project, the intern would be analyzing how the role of the neighborhood environment reflects upon healthy movement behaviors and well-being. Another possible project relates to children’s independent mobility in the time of COVID-19. Additional projects related to these topics may arise as necessary. There won’t be a lot of data cleaning or organizing to be done, as the data will be ready for analysis. Interested students should have knowledge or familiarity of ESRI files, or a background in GIS. Any skills or previous experience in regression analysis and spatial analysis would be an asset.
Second Harvest is Canada’s largest food rescue organization. They recover surplus, unsold food from across the supply chain and redistribute it to not-for-profits across the country also preventing it from ending up in the landfill. They are located in Toronto and are looking for a data science intern to help to better assess the impacts and reach of their food rescue and redistribution efforts through data and statistics.
The goal of this internship position is to support with the collection, cleanup, management, and organization of data received through the annual Agency Survey. Knowledge of Microsoft Excel, strong data management skills and solid collaborative and communication skills are required. Some experience with Python, an understanding of database design concepts, dashboard development and report automation would be an asset.
There are also opportunities to provide basic descriptive statistics and visualizations for the organization’s end of year report.
The City of Toronto’s Transportation Data & Analytics Unit was established in 2019 and acts as a hub for data analytics, data science, data collection and data management within the City’s Transportation Services Division. They are looking for a data science intern for the summer to work on Toronto transportation related projects. Working knowledge of Python and SQL is required for this position. Skills and experience in GIS, Jupyter Notebooks, data visualization and report writing are an asset. The role and expectations for the intern will be defined together with the team and student at the beginning of the internship, based on existing skills, knowledge and previous experience. This position has flexibility in projects for the intern to work on.
The Ontario Digital Service (ODS) was founded in 2017 with a bold government-wide mission to build digital maturity and data capacity across government. Today, we are a dedicated group of coders, user experience experts, content designers, product managers, digital strategists, policy professionals and data experts with a focus on building simpler, better services for Ontarians.
ODS is leading the development of a provincial data authority, an ambitious new initiative that can responsibly share public data for social and economic good. We are looking for data and policy interns to support this work by investigating and reporting on specific data challenges, including data security and data protection needs, specific needs for small and northern communities, and municipal data standardization.
The main skills required for this internship are in data literacy, research and writing, analytical and political acuity. Additional tasks might include assisting ODS with stakeholder engagements and collaborations with ministry and industry partners on specific use cases, as needed. Knowledge of privacy, digital and data policy evaluation would be an asset.
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