Rotman School of Management, University of Toronto

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Professor Krass, Academic Director

We spoke to Professor Dmitry Krass, Academic Director of the Master of Management Analytics program, to look back over the first year of the program, and to discuss how the program will evolve moving forward to best prepare our graduates for careers in the analytics industry.

This is the first of three posts.

Hi Dmitry. In your view, how successful was the first year of the program?

The first year went surprisingly smoothly, given that it is a brand new program.

This program was very ambitious from the start; there were many new and unique things that we were trying to do.  I feel we delivered on every single promise we made to students, in terms of skills development, the courses, the practicum projects, program delivery, and career support. The feedback from the students has been quite positive. Overall, it has been a very good first year for the program. 

We also learned a lot about the key skills the market is looking for, at a very detailed level.  These learnings will be incorporated into the program for the coming year. 

How can a student prepare for the MMA?

Students should brush up on their programming languages, mathematics and statistics.  It would also be useful to learn about what project management is, and how to structure an analytical project.

It is hard to over-emphasize how important the project structuring skills are - we are finding related questions up a lot in interviews: here is an unstructured business problem, how would you structure an analytics project focused on it? A student also should be able to talk about how would they measure impact of the proposed solution, and how to validate the results. It’s not just about being able to build models.

The program reflects the real-world of analytics

Many of the challenges students face in the program are realistic, it is what they are likely to encounter in the real world. You will often find student groups working quite late in one of the many study rooms in the building. They have gathered the data, processed and formatted it, built models and started writing up the results, when they realize it doesn’t make sense. They start digging and realize the error is in the data, and everything needs to be re-done.  This is a very typical cycle in an analytics project, and is a valuable, though frustrating, experience to go through.

A rookie mistake is that many students make is being too focused on building the most accurate model, and that’s not always what is needed. Taking the example of a consultant, you don’t just want your consultant to build a fantastic model, that’s not enough. You want them to tell you how to improve your business and solve the fundamental underlying business problem. What actions can be taken?

A highlight for me was when I saw a project team that were given a rather abstract practicum problem. I spoke to the group as they approached the final project presentation, and they were talking a lot about technical details, the background, the things the decision makers, the business people in the room don’t need to know. I suggested that they must re-do their presentation, focusing on actions and learnings, rather than on the model description – a message that, I am sure, was hard to take with less than 24 hours left until the company presentation. While the team had to work quite late, the material they prepared for the actual presentation was wonderful. Everything was topical, actionable and insightful. Everyone at the host organization was extremely pleased with the results. All members of that team ended up interviewing with that company; several are now starting their careers there.

I think everyone – the faculty coaches assigned to various teams, I, in my capacity as the practicum course coordinator, the project hosts – learned a great deal about what makes a successful project, what pitfalls to expect, and how to steer a student team around them.  We will certainly apply these learnings next year. 

The Master of Management Analytics is designed to give students the advanced data management, analytics and communication skills needed to become an analytics professional in 9 months. 

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