This is the second part of our discussion with 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 second of three posts, and focuses on some adjustments to the program.
How will the program evolve in future years?
One thing we realized within a nine-month program, some skills are easier to train than others. In particular, the skills that are really hard to train from scratch are related to computer programming, and computational ability. If someone is coming in with limited programming skills it’s very hard for them, given the volume of courses and everything else that is going on, to train up to be a comfortable programmer over nine months. They need more help early on.
Thus, during the introductory term we will be offering Python for Computer Science, which focuses on the fundamental programming skills. This mini-course will consist of six sessions, interspersed with tutorials.
Students who already can demonstrate strong programming skills will not need to take this mini-course, they will be given an interesting “opt-out” assignment to work on to demonstrate and test their ability.
Curriculum and colloquium
Last year our faculty came up with brand new courses for this program. This, by the way, is quite a unique feature of the program. In many similar programs, only a few “core” courses specific to the program, with most of the remaining coursework drawn from a set of pre-existing elective courses open to students from other programs. In the Rotman program, all courses are MMA-specific, and are designed to logically interconnect with each other. These connections were hard to achieve in the first year when each instructor only had an outline of what other instructors intended to do in their courses.
Now, of course, we have much more detailed information about the contents and level of coverage in each course. We have reviewed all of the courses to make sure there are more inter-connections between them, so we know where to set expectations, and what students should be reasonably expected to accomplish in each course. In particular, we learned that tutorials, with strong teaching assistant support, are really important – all the skills the students are trying to learn must be reinforced repeatedly; this is definitely a very hands-on program.
Another important change comes from the colloquium.
The colloquium course was designed, from the start, to be a vehicle to bring new emerging topics to the curriculum, and to explore how those topics would fit into the rest of the program.
It also gives the students a way to be exposed to these topics in a relatively pressure free way, as this is a pass/fail course. We’ve learned that topics that are related to skills, the ones that the market especially demands, are very important. I would say we had our most successful colloquia were on specific skills-based topics like Hadoop, API, neural networks, deep learning, and fraud detection. A few students got jobs that could be directly traced to the material they learned in the colloquium. We have also learned that some of these skills are now viewed as “core” by the industry, and deserve more coverage than what can be fitted into a 3-session colloquium module.
Thus, for the second year of the program we are going to integrate neural networks and deep learning into a regular course within the program. This opens space for a new colloquium on probabilistic networks and network analytics. We are also adding cloud computing content to the Hadoop module.
The colloquium will keep changing as new topics emerge, and this is how the colloquium course was designed to be. It is the most flexible part of the program.
The Master of Management Analytics is designed to give students the advanced data management, analytics and communication skills needed to become an analytics professional.