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Carlson Analytics Lab Process

Solve Business Challenges with Data Analytics

Clients come to the Carlson Analytics Lab with all kinds of business analytics problems. From engineering new ways to harness the power of data to in-depth analyses, and from machine learning to data-driven optimization, student teams provide analytics solutions.

The Lab solicits projects during summer and early fall, after which engagement agreements are signed. A fall pitch event provides time for clients and students to meet and learn about the projects. Teams are assigned in early December and begin pre-work. The 14-week project engagements begin in January and run until May.

Partnering with the Carlson Analytics Lab

Ellen Trader and Ravi Bapna talk about the benefits of partnerships between client companies and the Carlson Analytics Lab.

people working in group

Collaborative Engagement

Projects begin officially in mid January (the start of spring semester) with regular meetings. Working collaboratively, student teams and clients further define project requirements, with an emphasis on how the data analytics work will support business goals. Teams document requirements, approach, timelines, and expected deliverables for client approval.

 

Expert Guidance

Over the course of 14 weeks, each team works on their project under the guidance of a faculty advisor and with support from professional staff. Teams meet with their client weekly to report on progress and get answers to questions. At the project mid point, teams give a formal presentation of initial results and plans for completing the work.

MSBA students talking with faculty
Solve Business Challenges with Data and Analytics

Results You Can Use

Depending upon the project particulars, teams deliver analyses, reports, code, models, live dashboards, and knowledge transfer documentation, ensuring that client needs are met and that solutions are implemented.

At the conclusion of the project, students earn course credit, and their work is graded by faculty, staff, and clients. This model ensures rigor and professionalism in the work.