MFRM is a quantitative-oriented program at Rotman. As the first semester came to an end, there are many lessons we all learned. I noticed that students' programming skills improved considerably as they became much more proficient at solving real-world financial issues by using Python. Such improvements I believe are due to the well-tailored assignments and the different teaching styles provided by professors.

Thanks to the efforts of professors, TAs, and program faculty, our virtual learning experience was in some ways even better than onsite learning.

But it wasn't always like this... 

At the start of the semester, the workload involved in programming was heavy and came with some challenges. Unlike text-based assignments, group programming projects are difficult to collaborate and distribute evenly due to the lack of useful collaborative coding platforms. Also, not everyone in the group is highly skilled at programming, which added to the difficulty in collaboration along with different time zones during virtual meetings 

One of the courses involving programming was Innovatives in Financial Technology taught by Professor Raymond Kan. At first, the content of this course was very challenging for most students who lack machine learning knowledge. The professor realized this, so to help, he used sample code in the lectures to explain the concepts of Fintech and machine learning. For example, when covering clustering and classification, the professor provided codes for each classifier to demonstrate the complex principles behind it.

By analyzing the assumptions, parameters, and outputs of each classifier, students were helped to understand the practical applications of these machine learning methodologies. In addition, professor Kan demonstrated advanced data visualization in his code, although this was not a required part of the assignment.

The purpose of providing data visualization was to help students in their future careers to be able to better present analytical results to their future employers who are not familiar with the technical tools, which is one of the professional skills we need to acquire.

Since the assignments were relatively standardized, professor Kan also encouraged students to interact with peers to learn various problem-solving approaches.

Surprisingly, another course that highly involved in coding was Advanced Investments, and Professor Bin Han trained students' programming skills in a completely different way.

All the group projects were required to use Python or other programming languages to solve or analyze financial issues, and professor Han only provided concept clarification rather than offering any helps in coding. Unlike professor Raymond's projects, since the way that each group processed, filtered, and cleansed their data might be different, the final results could vary widely. There was a great deal of flexibility in processing the data and determining the parameters and assumptions, which requires students to choose the appropriate variables on their own through research, discussion, and previous experience.

Since two courses involving programming were closely related, students gained a solid foundation in programming in Professor Kan's projects. Hence, they were able to leverage the code and theoretical strategies in real-world investments and portfolio analysis in Professor Han's projects.

Overall, the COVID-19 epidemic posed challenges to MFRM students.  The workload for the faculty had definitely increased a lot because everyone was doing their best to help the students to achieve their academic and professional pursuits. It was a fantastic semester, and I'm looking forward to my industrial project and remaining courses next semester!

The Master of Financial Risk Management is a full time program designed to prepare ambitious young professionals for careers in risk management and finance.