Even with a strong data background, there's real value in this program. This program goes beyond the basics and genuinely challenges you."
Prateek, UBC Certificate in Key Capabilities in Data Science student
Prateek is a senior project manager who leads large, cross functional programs in the retail sector. He began his career as a business data analyst before moving into project management where he has worked closely with cloud, data engineering and analytics teams on managing data intensive programs across media, operations and retail.
What led you to pursue Key Capabilities in Data Science?
A key part of my work involves translating insights from analytics and data engineering teams into informed business decisions. I understood their data output, trends and forecasts, but I wanted to learn how machine learning models make predictions and why certain approaches are better suited to particular problems. The UBC Certificate in Key Capabilities in Data Science gave me the opportunity to build that knowledge.
What were some key factors that helped you decide to choose this program?
I looked at a few options before choosing this program and most were formatted the same way with long lecture videos followed by a quiz that had limited opportunities to apply the material.
What stood out about this program was the accountability built into it. Every course, including Python, machine learning and data visualization using Altair, had its own project with weekly assignments and quizzes. With my existing knowledge, I was looking for a program that would continue to challenge me, and this program did.
What was a highlight for you in the program?
Introduction to Machine Learning was the hardest part of the program for me, but it was also the most impactful. Before taking the course, I assumed that if a model produced reasonable results, it was working well. However, this course challenged that assumption and helped me look beyond the output.
I came into the program with a working knowledge of data and analytics, so I expected it to feel familiar. Instead, I learned concepts that went well beyond what I had encountered before. It gave me a stronger understanding of the reasoning behind how models function and make predictions, rather than simply focusing on the outcomes they produce.
What surprised you about the program?
How often I was proven wrong, honestly. I assumed my analytics background would make the course material familiar, but I was surprised how the program revealed gaps between what I thought I understood and what I actually knew. Rather than being discouraging, the experience reinforced the value of a program that tests understanding and helps students to build on existing knowledge.
How are you applying the skills you learned in your personal or professional life?
It's changed the type of questions I ask in meetings with technical teams. Timelines and outcomes used to be almost the entire conversation for me. Now, I'll ask about the reasoning behind a machine learning model or an approach, and I can fully understand the answer instead of simply listening.
Learning Altair was especially valuable for me too because it changed the way I think about reporting. I now see opportunities to use data visualizations rather than relying on traditional slide presentations.
Who would you recommend the program to, and why?
I'd recommend this program to anyone who already works closely with data, whether through analytics, reporting or visualization tools, and wants to have a deeper understanding of how machine learning models get built in addition to interpreting their output.
Even with a strong data background, there's real value in this program. This program goes beyond the basics and genuinely challenges you.
What advice would you give to someone looking to take courses in data science?
Don't skip the projects, even when it's tempting. That's where you really build your understanding. Also, don't assume that a strong data or analytics background means you already know the material. I went in fairly confident and still found myself challenged more than once, particularly in the machine learning course. Take those moments of discomfort as a learning opportunity instead of rushing past it.