Note: I graduated in June 2024. The program is still in its development so the information here may quickly become outdated.

Read time: Around 10m

TL;DR

• This MSc. focuses on mathematical and statistical modelling to tackle real-world problems using data.
• Courses cover a range of topics including mathematical modelling, Bayesian statistics, frequentist statistics, and machine learning.
• The degree includes a work or research term. I did a work term at the Workers’ Compensation Board of Alberta.
• I recommend coming into the program with at least a minors worth of experience in math and in stats, and some programming experience.
• Graduates of the MDP program can expect diverse career opportunities in data science, with potential earnings around CAD 90k.

As I write this, I am reflecting back on my newly completed master’s degree at the University of Alberta, a Master of Science in Mathematics and Statistics with a specialization in Modelling, Data, and Predictions. I chose it, of course, because it has the longest name. From here on, I’ll spare you that title and call it the MDP program. I’ve fielded many questions about this degree from curious family, friends, and incoming students, so I figured I’d collect my thoughts formally and publish this overview. While the math department has just rebuilt the program’s website, which should address many questions, I’ll add my opinions about courses, program content and who should take the MDP degree below.

Part 1: What is the MDP degree?

The MDP program can be considered a Data Science degree. However, given that data science is an extremely broad term that covers several entirely separate fields of study, saying ‘data science’ doesn’t provide much insight. Instead, I’d call it a modelling degree.

The MDP degree aims to answer one set of questions: “Given zero, small, or big data, what approaches can be taken to provide inferences about a problem?”. To answer this question the MDP program teaches the mathematical underpinnings of an array of toolsets, so the practitioner has enough background to decide on the relevant method and apply it to a given scenario. The degree includes a research or work term (I chose work) so you can practice these techniques in the field. I’d describe the MDP curriculum’s focus as mathematical and statistical modelling; the use of math and stats to represent, analyze, and make predictions about real-world systems (or occasionally imaginary systems – much to the bemusement of my friends, I built a statistical model for a Dungeons and Dragons game to predict my party’s likelihood of winning a boss fight). With its coursework shared between math and stats, I’d consider it a statistics degree enriched with mathematical modelling coursework and with some numerical methods thrown in for good measure.

“Zero Data?” I hear you ask. Techniques for zero or limited data are often left out of data science adjacent degrees, so I’ll start there.

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To read on, check out the full blogpost on Kent’s personal website