Before coming to Canada, and later during my first year at Fraser International College, I was not very strong at programming. I had learned the basics of Python and C, but mainly to pass classes. I had never built real projects or understood how programming could fit into a future career.
SFU — Computer Science + Math
After transferring to SFU, even during my first semester there, I completed assignments without really seeing the bigger picture. I was focused on finishing tasks rather than understanding how they connected to real-world software or engineering roles.
During that period, I also started working as a Data Analyst at Synkron. That role gave me valuable experience working with structured data and real business problems. Through my work at Synkron, I was also exposed to machine learning systems in practice. However, I interacted with them only as input–output tools: data went in, predictions came out. I didn’t know how models were trained, how they were tuned, or how they were built from scratch. It was a data-focused role, not a software or machine learning engineering one.
Everything started to change in the summer of 2025, when I took my Introduction to Software Engineering course at SFU. It was the first time I worked on a real project from start to finish. Around the same period, I started meeting more people through workshops and student communities, which made me clearly aware of how far behind I felt. Many already had strong programming backgrounds, large personal projects, or internship experience.
There was a moment of real self-doubt. But instead of trying to compete with others, I shifted my focus toward becoming better than who I was before. That change in mindset marked the beginning of a more intentional and disciplined approach to learning.
SFU became the point where my interest in technology turned structured and deliberate. What started as curiosity gradually evolved into building, breaking, and refining real systems, from software engineering workflows to data-driven projects and machine learning pipelines. This stage is about understanding how things work under the hood and applying that knowledge in practical, collaborative settings.
Focus at SFU
- Computer Science foundations
Systems, databases, software engineering, and probability — learning how software actually runs, scales, and fails. - Building real projects
Frontend apps, ML pipelines, and team-based projects where ideas turn into working systems. - Direction toward ML / AI
Applying math, statistics, and programming to data-driven decision making.
Milestones
- First full software engineering workflow
Working with Git branches, issues, pull requests, and structured planning in a team environment. - First end-to-end ML pipeline
From exploratory data analysis to model training, evaluation, and iteration. - Frontend + API integration
Building interfaces that connect user input to real logic and data processing. - Hackathons & applied projects
Fast-paced problem solving under constraints, similar to real-world engineering scenarios.
This stage isn’t about mastery yet — it’s about building strong foundations, learning how to learn, and developing habits that scale as projects become more complex.