By Rohan Athreya
This semester, Michigan Data Consulting (MDC) ran five project teams consulting for the U-M
Ford School, NOMAD Science, Ballotpedia, Riot Games, and a student-led real estate start-up. For each
project, a group of around 5 students plus two project leads provided data-related services to their
assigned organization, such as analyzing proprietary data regarding their performance metrics in order to
recommend ways to improve their operations, and engineering solutions that make the process of
accessing data more efficient. Throughout the duration of the project, members expanded their data
science skill sets by learning new industry-focused technologies like SQL, R, & Tableau along with
techniques like machine learning & artificial intelligence, applying these skills to real world situations in
preparation for their future careers.
All five projects recently culminated, with each team sharing their deliverables and
recommendations to their clients meetings. In terms of achievement, our end-of-semester feedback survey
informed us that our members did feel more confident in their data science abilities and are excited to take
on new projects next semester.
A challenge that many of our teams encountered involved receiving either incomplete or overly
complex datasets. For example, in one of our projects, we received a large dataset that required extensive
cleaning. This unexpectedly took up multiple weeks, which resulted in a rushed timeline and extra
planning. We ended up being able to get back on track by putting in additional work hours and
coordinating delegation of tasks across the team. This was a great experience to learn how to adapt
quickly and manage a constantly changing timeline in project management.
As this was our first semester operating at an organization, the executive board learned in real
time about which tasks required the most attention, and many discussions were had at board meetings
about how to best structure the board and divide the work to make the club as productive as possible,
showing how we espoused BLI’s habit of building a team. Furthermore, our project leads took advantage
of the diversity of perspectives on their teams when delegating the necessary work amongst their
members, assigning tasks based on experience. This allowed for our final deliverables to include truly
insightful evidence-based recommendations for our clients, and detailed how our leads adopted BLI’s
habit of valuing difference.
In the future, we will make sure to continue emphasizing communication with our project
partners to ensure that we are able to do the work that they are expecting of us. We will also continue to
work closely as an executive board to make sure that our tasks are delegated appropriately such that our
organization runs smoothly.
Our next steps involve planning ahead for next semester, when we plan to source a wider breadth
of data-driven projects from industry sponsors and recruit more talented students to apply their data
science skills to positively impact society.
