Client Eligibility

Who can participate?

Potential clients should meet the following criteria:

• Able to meet with students in the fall to define project requirements

• Able to provide a secondary contact who is technical and who can answer questions about the datasets

• Able to meet with students throughout the winter semester while the project is in process

• Able to provide, at a few designated milestones, feedback and evaluation that forms part of the students' final course assessment

Projects

What are some examples of successful projects?

• Check out this video , this video & this video for completed project deliverables.

What kinds of projects are appropriate for the course?

Potential projects should meet the following criteria:

• Data-centric and revolve around large-scale datasets that require distributed computing and data manipulation, analysis, and visualization that cannot be performed on a personal computer.

• Involve significant technical work, with corresponding amounts of programming and/or data analysis scripting

• Require about 60 hours per week of work during the winter semester (about 15 hours per student per week)

Desirable projects may include the following:

• Parsing, analyzing and interpreting Web log data for your organization/commercial enterprise

• Using enterprise-scale data to improve performance, outcomes, or understanding of a problem

• Developing a data manipulation/cleaning pipeline with Web-based visual summaries for your dataset(s)

• Questions about data that require more than one component of data analysis or management to address

What kinds of projects are NOT appropriate for the course?

Less desirable projects may include the following:

• Work on mission-critical components or processes with critical dependencies on other projects

• Projects that do not involve significant technical or programming work

• Projects without well-defined outcomes or paths to judging success

What are some examples of successful projects?

Accessing Justice—Connecting Low Income Clients with Affordable Attorneys. Students developed an algorithm to create visualizations for the pro bono division of the Chicago Bar Foundation. They were tasked with manipulating variables like poverty level, geographic location, and the type of law cases clients sought, to produce meaningful visualizations that can help the Justice Entrepreneurs Project allocate funds toward proper channels.

Fighting Fire with Data. Students worked with leadership from Fort Myers Fire Department to gather, analyze, and build upon key data sources to develop a tool that can identify, define, and prioritize at-risk buildings.

Library Program Standardization. Students worked with the Public Library Association to build a tool for decision makers involved with Project Outcome that makes sense of survey data, gathered over three years from public libraries in the United States and Canada, through predictive statistical analysis and sentiment analysis.

Customer Segmentation and Creditworthiness Prediction Models. Students worked with Umati Capital to address small- and medium-sized enterprises' lack of access to capital by developing a customer segmentation and credit scoring prediction model to calculate default rate and offer loans among these businesses.

Arts Engagement. By using over 5,000 responses from students across all schools at the University of Michigan on their level and history of engagement with the arts, students used modern data analytic techniques on qualitative and quantitative data to help arts administrators determine key predictors and motivators in college arts engagement.

What do students do during the project?

Winter 2021 course syllabus

How many projects are selected for this course?

• Winter 2021: 17 projects selected

• Winter 2020: 21 projects selected

• Winter 2019: 20 projects selected

* Due to variability in the number of enrolled students each year, these numbers are subject to change and can be used as a rough estimate.