MBA@UNC student Jeff Duresky shares his experience in the inaugural section of the new Data Analytics course.

As the current “it” phrase, “data analytics” can take on numerous meanings depending on the company or industry. For many, “data analytics” and “big data” are just buzzwords that occupy squares on the office-speak bingo card. They are something people discuss from 30,000 feet as a way to “shift the operating paradigm.”

The MBA 706 Data Analytics course bridges the perceptual divide and delivers both an overview of analytics within the business environment and an experiential understanding through case studies and group discussions.

As with many MBA@UNC courses, students are thrown immediately into the deep end. Professors Tarun Kushwaha and Adam Mersereau take an approach of working through the details in order to appreciate, and reinforce, the broad concepts.

The first few weeks are spent learning concepts—such as logistic regression, classification, clustering and tree-based models—and the SAS JMP statistical discovery software. As someone who thought a neural network was the T-1000’s operating system in Terminator 2, I found it challenging to understand the concepts while learning a new piece of software.

However, as a new course, each synchronous section had a professor and a coordinating professor, along with small-group breakout sessions. The immediate feedback in these sessions, as well as detailed guides to SAS JMP, helped to reinforce the “how” of the software, which in turn allowed me to focus on the concepts.

Homework for Data Analytics consisted of three individual assignments, two group assignments and a team term project. The syllabus stated that “the capstone assignment for this course requires you to get your hands dirty analyzing a real data set in an open-ended setting,” and the project deliverables certainly accomplished this broad goal. Our final paper used analysis of means (ANOM), linear and logistic regression, decision trees, and applying the neural network model to training and validation data sets to understand and predict interest rates—which is a sentence I would not have imagined writing, much less understanding, at the beginning of the quarter.

Big data has become a cliché due to its ubiquity. However, the prevalence of the term does not diminish its importance. The ability to leverage data can be a competitive advantage for any number of industries. By chance, I was able to immediately use techniques from this class for a case competition entry. Long term, the Data Analytics course gave me a base skillset of the intuition and theory behind the “data analytics” office-speak bingo that I can build upon as I progress in my career.