After the intro term, we began the tools term in November. For the past two months, we have been learning the theory and application of various analytical learning methods. In addition to all of this, we started working on our practicum projects which have been an eye-opening experience.
This assignment summarizes exactly why I wanted to join the MMA program; to learn best practices and tackle complex problems.
Big data: where do I even begin?
One of my personal highlights so far was our assignment for RSM8413 Big Data Analytics which involved working with messy, real-world data.
Coming from a life sciences background, I am used to analyzing experimental data with a well-defined hypothesis. Uncovering insights using found data that has hundreds of thousands of imperfect records (missing values, erroneous values, etc.) with no prior hypothesis is a whole different ball game. It is like walking into a dark cave with no map, no flashlight, and trying to find buried treasure. By day 3, you will begin to wonder if there is anything to find at all and question why you embarked upon this endeavor.
Luckily, this task was manageable because if you lose your way, you can ask the professor. We are quickly learning that it is easy to come to the wrong conclusion so understanding the problem and critically assessing your model(s) is key.
Our assignments and experiences have helped build my confidence for the practicum project where the training wheels are off – there is no pre-defined answer, you get to explore on your own. This freedom was overwhelming at first (so! many! possibilities!) but after working with my group to define the project scope, it feels much more feasible.
Academics overall and tips for success
Another highlight for me is how the courses fit together. For example, RSM8512 focuses on the theory behind different analytical models (conceptual). RSM8413 builds upon this knowledge and gives students an opportunity to pre-process data and run the algorithms (application). One course uses R and the other uses Python, so we get a complete picture of how the math translates into code along with familiarity in two widely used languages.
The MMA is an intensive program but the work never feels pointless. Personally, I cannot say I find studying fun but it does feel purposeful because the material is relevant for technical interviews and eventually, work. That being said, it is easy to fall behind. My recommendation is to do some review prior to the program to set yourself up for success. It is a Master’s program so for the most part, what you put in is what you get – as we have learned for most algorithms, “garbage in garbage out”.
Extracurriculars and socials
One of the best parts of the program in the community.
Corny, but true. I was worried that it would be difficult to get to know the other students with classes being virtual but fortunately, that has not been true. If you need help or want to commiserate over deadlines, the group Discord is always active.
Our student representatives, career services, and program team provide lots of opportunities to connect with alumni, companies, and other students. Examples off the top of my head include weekly coffee chats with the program services team, family feud with the MFRM program, and networking night with the Faculty Advisory Board.
After a busy December, it is finally time for the winter break! I am looking forward to exploring Toronto and supporting local restaurants. Toronto is a great city for food, check out these lists if you’re looking for inspiration: critically acclaimed, cheap eats, most photogenic.
The Master of Management Analytics is designed to give students the advanced data management, analytics, and communication skills needed to become an analytics professional.