Meet Babak

UBC Certificate in Key Capabilities in Data Science

Trained as a physician and later working in clinical research, Babak became interested in how data can help people understand complicated problems and support better decisions, especially in healthcare. After immigrating to Canada, he decided to build a new career in data science by combining his medical knowledge, research experience and interest in statistics and technology.

What led you to pursue Key Capabilities in Data Science? 

My move from medicine to data science did not happen suddenly. In clinical research, I was already working with data, statistical analysis and evidence-based decision-making. However, I wanted to go beyond using separate tools and develop a more complete and professional approach to data science.

What were some key factors that helped you decide to choose this program?

UBC’s reputation was important to me, but I was also attracted to the practical structure of the UBC Certificate in Key Capabilities in Data Science. The combination of Python, machine learning, data science tools and visualization was closely connected to the skills I wanted to improve. The StrongerBC future skills grant also made the program more accessible to me at an important point in my career transition.

What was a highlight for you in the program?

One highlight was completing my final Python project. The most valuable part was going through the complete process: preparing the data, writing reusable functions, testing the code, creating visualizations with Altair and explaining the results.

Another highlight was the support I received from my instructors. They were generous with their time and guidance. I appreciated being able to ask questions, receive honest feedback, correct my mistakes and continue improving.

What surprised you about the program?

I already had some experience with Python and data science, so I expected the main challenge to be learning new technical concepts. What surprised me was how much I learned about the quality of the work itself. Writing code that runs is not the same as writing code that is organized, tested, reproducible and clear. The program taught me to prioritize these professional habits while highlighting areas for growth.

How are you applying the skills you learned in your personal or professional life?

I use these skills in healthcare and research projects involving data cleaning, statistical analysis, network analysis, machine learning and visualization. I now approach projects more systematically. I try to define the question clearly, examine the quality of the data, document my decisions, test my work and communicate the results in a way that both technical and non-technical people can understand.

The program also changed how I think about my work. I now ask myself whether another person would be able to understand my reasoning, reproduce my analysis and use the results to make a meaningful decision.

Who would you recommend the program to, and why?

People who are curious about data science, but need a structured and practical path into the field. It can be especially helpful for researchers, analysts, career changers and people who have learned some tools independently but want to connect those skills more systematically.

What advice would you give to someone looking to take courses in data science?

Do not wait until you feel completely ready. There is no perfect time to begin. Data science is too broad for anyone to know everything. Be active in your learning: attend the sessions, ask questions, participate in discussions and build projects connected to subjects you genuinely care about.

Also, do not treat mistakes as evidence that you are not capable. A mistake often shows you exactly what you need to learn next.

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