Rotman School of Management, University of Toronto

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About Me: Xiaowei, MMA Class of 2022

January, the starting point of our new year, is also a new starting point for MMA students.  

We began our sprint 1 for our practicum project. We have learned so many programming languages and tools like Python, SQL and R in the previous tool term, but do we actually know how to apply these in real life? Here comes the practicum project – a chance for us to conquer some real business problems! 

Back from a short break, both mentally and physically prepared for a new journey, we started a new chapter in the program – Practicum at Scotiabank. Our project is on updating an existing holistic metric through addition of new data sources, enhanced modelling methodologies and the financial implications behind these metrics.  

As our project involved elaborating historical works, the crucial part was fully understanding the old metrics and their approaches. My teammates and I spent a lot of time understanding the business problem behind these metrics and started exploring the data using tools we learned to generate some insights. This provided us a chance to learn how data are being used to generate useful insights about customers.  

Different from the school setting, communication can be the crucial part while trying to solve real life problems. Both understanding their work and exploring new tools required a lot of communication with different data scientists. We also needed to make sure that we can meet their expectations. Therefore, we had daily stand-ups and met with data scientists in our team to ensure we were on the right track.  

Along with intense communication with different people, we also needed to set the scope and plan out our project. To prepare for practicum in June, we tried to understand the current metric model, did some exploratory data analysis for new data sources, and brainstormed potential improvements from the current model. We spent some time practicing tools that we’d learned in the tools term – such as Python and SQL in the data exploratory process. Nonetheless, additional different techniques and platforms were used in their modelling process (such as hive and datameer) compared with school settings, and the learning curve can be steep which gave us a hard time to understand the current model.  

Our practicum TA was very supportive in the whole brainstorming process, especially when we had vague direction when targeting some specific features. She always had an engaging conversations with us, providing some useful insights and literatures to review.  

The last week of this sprint was the exciting presentation week. We had a half-hour presentation at Scotiabank earlier in the week. In this presentation, we illustrated details about our data exploration and potential improvements in the modelling to the whole team. As some data scientists from other team came as well, some fresh insights were obtained, and these new perspectives will be discussed and implemented in the following two sprints. The second presentation was a 5-minute debrief about our projects, attended by other practicum teams. This provided us a chance to learn what other teams had done which was very exciting but also made us feel nervous at the same time. 

Looking back into sprint 1, it was definitely hectic and challenging, but worthy as we moved from theory to real life. It also provided us a chance to explore our interests and challenge ourselves. Now, we will move on to the application term which will further improve ourselves after this intense but precious sprint.


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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