Michael Oyatsi

Michael Oyatsi’s interest in the field of data science was borne out of his desire to pursue a career that is technical in nature and requires analytical skills to derive insights that inform business decisions.
Oyatsi, who was working a project manager at a mechanical contracting company, was seeking a career pivot. 

Oyatsi looked at other data science master’s programs in Canada but discovered that most of these programs required a background in Computer Science which he did not. Oyatsi studied mechanical engineering.

This led Oyatsi to UBC’s Master of Data Science (MDS) program in Vancouver.

“The accelerated 10-month program was a key factor in my decision making. My alternative was an online master’s which I was not too keen on as I wanted to immerse myself fully in an in-person program. As well, the openness of the program admission to any field and not just computer science was an additional factor that helped influence my decision,” he explained. 

In addition to reasons Oyatsi listed above, he liked how MDS gave him “the opportunity for a hands-on learning experience in a short period to aid my professional career pivot.”

Once Oyatsi was in the program, he discovered that his favourite MDS course was DSCI 553 (Statistical Inference and Computation II).

“It was my first exposure to Bayesian statistics, and since the majority of the program (and life in general) is based on frequentist approaches, it was intriguing to learn where frequentist approaches fail and how Bayesian statistics can be an alternative,” he added.

For those like him that did come from a non-traditional background, while he found the transition difficult at first, he said that the program structure really helps to lay the foundation for success. “A lot of class time was spent on the foundational concepts, with the opportunity to dive deeper in the assignments. This focus on core concepts in class helped inform the after-school work that I did in trying to elevate my understanding into the tougher assignments and challenges.” 

Throughout MDS, Oyatsi also felt the program helped address the rise of LLMs and AI through curriculum, in particular, DSCI 575 (Advanced Machine Learning). He noted that the course provided a foundational intuition of how LLMs work that will help him have more informed judgement as to how he can use it at work and also understand its limitations and risks. 

As well, Oyatsi really appreciated how MDS’ faculty always had their finger on the pulse throughout the program to help adjust the course to the student’s need as needed.

“Most of all, they have been available throughout, both inside and outside the classroom, to make sure that students understand the concepts and are supported academically,” he added.

Another thing that Oyatsi appreciated was the diversity of backgrounds of his cohort both culturally and academically. 

“This diversity meant that I can lean on classmates to help fill the gaps in my own knowledge base and also learn and share the career paths post-MDS,” he said.

As for post-program, Oyatsi is looking at Data Science and Data Analyst roles within Finance. “These types of roles are a healthy blend for me to use the skills I have learnt in the program with my career goals before I started MDS.”

Michael’s Top 3 Tips on Succeeding in the MDS Program:

1. Show up to class
2. Dedicate time to working through the labs and assignments    
3. Ask questions to your instructors and classmates 

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