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MMA Datathon 2021 participant stories - Vedang, 1st place winner

The 4th Annual Rotman MMA Online Datathon brought in student perspectives and insight from all around the world! Participants took part in this event for a chance to test their analytical skills in solving an interesting challenge on the mortgage loan market. Data is most powerful when interpreted and applied to solve a specific problem, and participants this year we're able to do just that.

It was also a chance to experience what the Rotman Master of Management Analytics program has to offer from the comfort of being at home while creating new friendships globally.

Every year, there is a growing interest and participation in the Datathon competition. Participants from around the world are tasked to apply their managerial, analytical, and communication skills to develop a strategy for a managerial problem set by one of Rotman’s industry partners.

This year’s Datathon was held from September 30-October 16, 2021, and included 250 participants from 21 countries. Rotman hosted 53 teams of the brightest management problem solvers to dive into real-life data set and come up with a managerial solution to a business case surrounding the data.

The online event was an excellent opportunity for those passionate about analyzing data and applying insights into real-world challenges. Working in teams of four to five, participants took full advantage of their computational skills by using software tools and program languages such as SAS, Python, R, and SQL.

Participants were able to showcase their potential, explore data sets virtually, and improve their business applications of data. One of the major challenges involved in this competition was to summarize key insights within a strict time frame and decide how to best clean, sort, and analyze real data from the organization based on the challenge provided.

This blog post details the experience of Vedang, a 1st place winner in this year's MMA Datathon.

Quick info

  • Name: Vedang Vadalkar
  • Current position: Cost Engineer at California High Speed Rail Program
  • Educational background: M.Sc. in Civil and Environmental Engineering from Stanford University.

1. What made you decide to sign up for the Rotman MMA Datathon 2021?

I was interested in the MMA program for a while and was thinking of applying to the program. One day while exploring the Rotman website, I came across the Rotman events page. That’s when I signed up for the program updates while downloading the program brochure. Some days later in one of the emails, I found out about the MMA Datathon. I found the MMA program fascinating, so I wanted to get a sneak peek and gain some knowledge of what the program was about. I decided to apply for the Datathon and give it my best shot. As the Datathon was completely online and the fact that we were going to compete in teams, it seemed feasible to participate while working full-time.

I hadn’t participated in a Datathon before so I thought the experience would give me a taste of the MMA program itself.

2. What was the challenge presented this year? 

The Datathon challenge was not revealed until the start of the event. This year we worked on a mortgage dataset.

The challenge was to identify market segments and market entry strategies for a regional bank entering a new mortgage market. This was an intriguing problem for me as I am interested in the real estate market and know the basics.

The mortgage data was a whole other story as there are many factors that go into determining if a person should be given a mortgage. The banks must take into account several factors on the mortgage application and test them against the historic data. I think having a large dataset of past mortgages and their performances is beneficial for predicting future mortgages. For the first couple of days, all the team members spent time observing/studying the dataset and reading more about each of the individual columns and their significance on the mortgage application. Digesting the dataset in this way helped us identify the important metrics and made communication among the team much smoother as we all understood each other very well.

3. Was the event what you expected? What were your fellow participants like?

I was hoping that the Datathon would give me an opportunity to learn more about the MMA program and it delivered well above my expectation. My team was a diverse group of individuals with different backgrounds. All of them were equally involved and excited about the Datathon. I think a great team goes a long way in succeeding at the task at hand too. I don’t think we would be able to win the competition if everyone hadn’t shown the amount of enthusiasm and dedication.

4. What are some lessons/takeaways that you’ve gained through this Datathon?

It was great working with such a mix of individuals. It opened me up to newer ways to approach a problem and reach a solution that is backed by observations and data. All the estimates and forecasts we presented had a solid foundation and could be easily explained by the data available. It also helped me realize that a single dataset can be interpreted in multiple ways and there isn’t just one approach to solving a problem!

5. What was your highlight of the event?

The highlight would be the moment we were picked as winners and all the feedback from judges about why they picked our team as the winners.

It was great to see all that hard work everyone on the team put into the project pay off.

6. How much of an impact did it make in your decision to choose the MMA program?

I am very happy I participated in this year’s Datathon as it provided a glimpse into the MMA program. It cemented my decision of joining the MMA program at Rotman.

This program would be a step in the right direction as it aligns closely with the types of problems I want to work on and provide data-driven business solutions.

7. What advice/tips would you give to someone taking part in our Datathon in 2022?

The following will seem obvious, but it goes a long way.

1. Get to know the problem in-depth and read as much as you can about the problem presented. Take as much time as possible in understanding the problem statement and what is asked. If you don’t know the problem, it is very difficult to provide any insights.

2. I would suggest breaking the problem down into small sections. It is easier in grasping the entire problem when it is broken down in sections.

3. Work with your team and maintain proper communication. You don’t want to double the efforts, someone, on your team already put in.

4. The dataset and the solution take you only half the way. Presenting the problem in a clear and easy way is the other half. A clear and concise presentation is the best way to communicate your solution to your audience.

 The Master of Management Analytics is designed to give students the advanced data management, analytics and communication skills needed to become an analytics professional.

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