Information Analytics Project - SI 485

Proposal open date: June 1
Proposal close date: August 2
Project timeline: September-April
Project duration: 30 weeks
Number of projects needed: 25

SI 485: Information Analytics Project Syllabus

Description

In SI 485: Information Analytics Project, advanced undergraduate students deliver data-oriented solutions through the development and analysis of data sets, building tools to extract useful information for clients through manipulation, analysis and visualization.

Deliverables

Written report
Deliverables will vary by project and be based on the client needs and project scope. Examples may include:
- New data sets
- Data analysis strategy
- Additions to existing data sets
- Analysis of data
- Other negotiated deliverables

Client Requirements

Level of engagement for course/program
- Minimum of three meetings in the fall semester (September-December)
- Weekly meetings in the winter semester (January-April)
- Regular feedback and communication to student team based on the course schedule
- Introduction to stakeholders and users for interviews and/or data gathering
- Access to organizational data, systems and/or resources necessary to project completion
- Attend final presentation
- Complete project evaluation
Special requirements for the course/program
- Provide a secondary contact who is technical and who can answer questions about the datasets
- Data for the project must be ready to provide to students by the end of September

Additional Details

Projects for this course span the entire academic year
- In the fall semester (September-December), students will start project preparation in a 2-credit fall course (SI 405).
- In the winter semester (January-April), students will focus on the project work of data analysis.
In the fall prep course, students will meet with the client 2-3 times to finalize the project scope, project plan, and to ensure they have access to systems and data as needed.
In the winter course, students will meet with the client weekly and move forward on the project.
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

Past Projects

Fighting Fire with Data. Students worked with leadership from the 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.
Creating Corporate Solutions with Polaris and Sponsor United. UMSI students applied their training to two separate projects in user experience and data science to improve a website interface and a sponsorship deal process.
Community Health in the Dominican Republic. UMSI students worked with the non-profit Puente, which creates mobile data software to assess, map and prioritize community needs in the Dominican Republic. Students used machine learning models to cluster communities by indicators of community wellness in order to identify areas with the most need.
BankBlackUSA. Students worked to create a full interactive dashboard and comparison tool that could be easily integrated to the Bank Black site through link embedding. The dashboard provided users with easily digestible information about Minority Depository Institutions, Community Development Financial Institutions, and why using them can benefit individuals and communities.
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.
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.

Client Testimonials

"This project was something we've been trying to get done for a long time, but kept being de-prioritized. UMSI students acted as an extension of our team and were the resources we needed to hit our internal goals. The product, tech, and design teams have a better sense than ever of what information and themes are coming in through App Store reviews. The impact was huge."
- Becky Roth, Sr Manager of Product Marketing Expedia Group
"The students provided an invaluable perspective that improved our risk profile work on a broad level, and allowed us to think through the risks implied in the data in a more consistent manner. They were dedicated, highly motivated and technically adept in multiple technologies needed to create a solution. They helped us look at the data set in a new way and improve our product overall."
- Dennis Neil, University of Michigan Information Assurance
"Our team focuses on the experience our customers have with our digital support materials. The UMSI students' work allows us to determine what we implement next for our users and the impact it will have on them. They've essentially given us the necessary data to back our team's big initiatives and track our impact—a project where we didn't have the proper headcount to really dig into."
- Emily Gottschalk, Qualtrics

Past Clients

Contact us

Engaged Learning Office | [email protected] | (734) 763-1251

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