About

I’m a fourth-year Data Science student at SFU who’s passionate about turning data into meaningful insights that drive real-world decisions.

I love solving problems and constantly seek to strengthen my anaytical thinking through case competitions, hackathons, and research projects — whether it’s using data to uncover patterns or thinking outside the box to reframe a challenge.

What fascinates me most is how a combination of analytical reasoning and creative problem-solving with data can help uncover root causes, align stakeholder needs, and build smarter, more practical solutions.

In a world where AI puts answers at our fingertips, what truly stands out is our ability to bring real-world perspective to problems — to map knowledge across domains, identify root causes instead of surface symptoms, and ask the right questions. Whether you're talking to humans or machines, the clarity and precision of your questions often determine the quality of the solution.

Data Science is a framework that anyone can use to solve any type of problem. Every problem—whether it’s business, personal, financial, environmental, computational, or otherwise—requires you to build a unique framework tailored specifically for that problem, starting from a basic foundation.

The basic framework involves a few key components:

1. Understanding the Domain:
   You first need a solid understanding of the specific area (or niche) in which you’re solving the problem. (For example, Data Science degrees at universities often emphasize business understanding as a core part of the process.)

2. Mathematical & Statistical Foundations:
   Once the problem is well understood, the next step is to break it down into a mathematical or statistical problem that can be addressed using data.

3. Technical & Programming Skills:
   To work with data effectively—whether it's cleaning, organizing, or simulating—you need coding skills. This is essential for fetching data, which may be stored in cloud servers, company databases, or other sources.

4. Data Preparation & Exploration:
   After obtaining the data, you clean it and explore it to fully understand its structure and limitations.

5. Model Selection:
   Choosing the right statistical or machine learning model depends on your understanding of both the data and the domain. A good grasp of the niche helps you select a model that is balanced and appropriate for the specific problem you’re addressing.

6. Interpreting Results & Drawing Insights:
   Finally, to extract impactful insights or make accurate predictions, you need a deep understanding of the mathematical and statistical limitations of the model you’ve chosen. This ensures your conclusions are practical and impactful.

I like to summarize the above steps into:

Insight: Begin by exploring the available data or information. Understand the business context, identify patterns, and uncover what the data is truly telling you. This step is about asking the right questions and building a foundation based on facts, not assumptions.

Balance: Frame your hypothesis carefully and select analytical approaches or tools that address the root cause of the problem — not just the symptoms. Strive for a balance between business needs, technical feasibility, and stakeholder priorities.

Impact: Translate your findings into clear, actionable recommendations. Focus on what’s feasible, valuable, and aligned with the business goals. The goal is to deliver solutions that don’t just look good on paper — but drive real outcomes.