story
I'm a data scientist who cares about getting the analysis right and explaining it clearly.
My path to data science
I came to data science from mathematics. After a B.Sc. in Ankara, I moved to Canada for an M.Sc. in Business Analytics and Artificial Intelligence at Ontario Tech University, drawn the whole way by the applied questions behind the theory: what is actually true in this data, for whom, and how sure can we be? Freelancing on the side building websites and automations taught me the other half: how to gather a requirement, ship something, and explain it to someone who doesn’t care how it works, only whether it does.
So I’ve been learning the practical way, by building. Six end-to-end projects on public data, each one tested, documented, and public on GitHub, and each written up honestly: what I found, and what I couldn’t claim. I’d like to join a team where analysis is used to make real decisions, and where being careful and clear counts for more than sounding impressive.
Education
M.Sc. Business Analytics and Artificial Intelligence
Ontario Tech University, Oshawa, Canada · 2026 – present
Graduate study combining business analytics with applied AI, building on an undergraduate foundation in mathematics and statistics.
B.Sc. Mathematics, secondary field in Data Analytics
TED University, Ankara · 2021 – 2025
Relevant coursework: probability & statistics, regression, linear algebra, and data analytics.
Experience
Transferable experience counts, real roles belong here even if they weren’t data roles, with a line on what they taught. If there’s no formal experience yet, this becomes a learning timeline of courses and project milestones.
Built a five-project data-science portfolio
Self-directed · 2026
Six end-to-end projects on public data (churn, bank-subscription, and NBA prediction, demand forecasting, SQL revenue analytics, and a retail dashboard), each tested, documented, and public on GitHub.
Freelance web & AI-automation work
Upwork (self-employed) · ongoing
Building conversion-focused websites and workflow automations for small businesses using AI-assisted development, where I practice gathering requirements, shipping, and communicating with non-technical clients.
Skills at a glance
| Area | Core tools |
|---|---|
| Languages | Python, SQL |
| Analysis | pandas, NumPy, Jupyter |
| Machine Learning | scikit-learn, gradient boosting |
| Statistics | Hypothesis testing, A/B testing, regression |
| Time Series | Walk-forward backtesting, forecasting |
| Visualization | Matplotlib, Plotly, Streamlit |
| Workflow | Git, GitHub, pytest, virtual environments |
How I work
Start with the question, not the model
The best method is the simplest one that genuinely answers the question.
Baselines before complexity
A model is only impressive relative to what a simple approach achieves.
Show the caveats
An analysis that hides its limitations isn't analysis, it's advertising.
Reproducibility is respect
Work someone else can rerun is work someone else can trust.
Want the short version?
The résumé covers the same ground in one page, or reach out directly and I’ll respond quickly.