The data science curriculum at the University of Rochester prepares students for the challenging task of deriving insight from vast quantities of structured and unstructured data, in a variety of disciplines. At the core of the curriculum are the capstone and practicum courses for undergraduate and graduate students, respectively. These semester-long courses emphasize a team-based learning experience where students conduct real-world analytics projects using data provided by sponsoring organizations. Students participating in the projects are required to complete a rigorous set courses in computational statistics, data mining, machine learning and computational tools in data science.

Since the program was launched in 2016, 46 companies have offered over 75 projects to students (as of July 2021), covering everything from consumer retail and healthcare to agriculture, government and finance. Past sponsors include large corporations, start-ups, not-for-profit institutions, government agencies, and academic institutes.

Each project begins with a sponsor presentation highlighting:

• Their business need(s)
• The goals of the data analytics effort
• A description of the available data

Students then work in teams of three to four members over a eight to ten week period (one semester) to understand the business problem, clean and analyze the data, and devise a solution. During the project, the students are strongly encouraged to establish and maintain regular communication with an assigned contact from the sponsoring entity. Day-to-day progress on the project is tracked by University faculty assigned to the course.

During the semester, student teams meet with their sponsors periodically to review progress and receive feedback, presenting at mid-semester and final presentations. At the end of the semester, the following records (typically generated in every project) are provided to the sponsor:

1. A written report summarizing the project and its findings
2. The final presentation
3. Code, notebooks, and/or data visualization workbooks (e.g. Tableau) used in the project
4. Instructions (ReadMe) documenting the steps required to run the archived code
5. Updated versions of datasets after data cleaning and pre-processing (if applicable)

• A well-defined problem statement that can be articulated in one or two concise paragraphs
• A data set that can be accessed by the students, either as a flat file (e.g. CSV), or via remote access to the sponsor’s database/data warehouse, using software tools made available to the students
• A problem that requires statistical analysis or machine/deep learning techniques to solve (e.g., predictive modeling, data mining, knowledge discovery, statistical correlations, visualizations)
• A business or technical contact at the sponsoring organization who can communicate with student teams over the duration of the project

There is no sponsorship fee or funding requirement to sponsor a capstone/practicum project.

All we require is a challenging business problem and data for analysis. Problems should be solvable using programming languages such as R, Python, and Julia. They must also require some level of data wrangling/munging and exploratory analysis.

If students need access to sensitive or proprietary information to complete the project, the sponsor can request that their team complete a non-disclosure agreement/intellectual property (NDA/IP) agreement.

Many of our previous sponsors have successfully anonymized business sensitive information in data sets and problem descriptions, while still maintaining the necessary technical information required for students to execute the project. We understand the time, effort and commitment required to sponsor a student project, and are grateful to sponsor organizations for their engagement.

The capstone and practicum courses  are offered twice a year (Fall and Spring). Accordingly, the following deadlines are applicable for the two semesters:

Interested in sponsoring a project? For more information, or to participate in the capstone/practicum program, please contact:

Ajay Anand
Deputy Director, Goergen Institute for Data Science
ajay.anand@rochester.edu 

Cantay Caliskan
Assistant Professor of Instruction, Goergen Institute for Data Science
cantay.caliskan@rochester.edu 

We look forward to working with you to help you define your project.