Rotman MMA hosts two yearly Datathons during the month of October- Online and On-Campus. This is one of two posts about the event. You can also read about the 2019 Online Datathon and student’s experiences.
In the words of one participant, “This datathon provides everyone who's interested in data analytics with this great opportunity to actually get a taste of it.“
In this post, Natasha Luo and Zhijian Zhu detail their experience and thoughts regrading the On-Campus MMA Datathon. Both participants were on the same team and landed first place in the Datathon.
Both Natasha and Zhijian found the competition to be very intense. While Zhijian is a data analyst in the field, and has had experience with working with data sets, Natasha comes from a business background and had no expectation of winning the Datathon. Working as an auditor, Natasha endorses the idea of making business decisions with data-driven evidence. When solving the Datathon challenge both were impressed by how much insights can be derived form data and how helpful it is to improve real-life challenges.
Usually, Zhijian has at least one month to deliver the result once receiving information from clients. For this competition, her team only had 6 and a half hours to come up with a solution (competition instruction and the data structure was given a week prior to the competition).
Despite the time crunch, Natasha and Zhijian were able to land first place in their view because of the different industry backgrounds in the team.
“The key to success for our team, was abstracting the business objective behind the business challenge and translating it into an effective statistical model, but this wouldn't become true without understanding and interpreting the data well.”
Tips for success
For the competition, Zhijian recommends to start early and to utilize the time a week prior to the competition, as time is limited during the actual competition. She also mentions their team coach, Professor Alan Esser, who helped them out during the competition and gave them a valuable tip.
“Try to think what are the business insights covered under the data and present your solution in a business-wise useful way”.
Natasha adds on that the team ended up implementing Alan’s advice, and taking the risky approach of putting the obvious solutions onto the slides and explaining it with pure non-technical language. The result show that intuition is key. To arrive at the seemingly simple recommendation and selection logic, their team spent some amount of time exploring the dataset and revising test model multiple times to rationalize the solution and the way it’s presented with plain language.
The key reminder, Zhijain states, is that we are data scientists instead of data analyzing machines.
“We have the responsibility to convert these numbers into meaningful, trustworthy but straightforward insights.”
The Master of Management Analytics is designed to give students the advanced data management, analytics and communication skills needed to become an analytics professional.