I am an aspiring data analyst from Kathmandu with a growing focus on data science. I enjoy cleaning data, exploring patterns, building dashboards, and turning raw numbers into clear insights using SQL, Python, and practical forecasting workflows.
Desktop mode recommended for the full terminal experience.
I'm Brishav Rajbahak — an aspiring data analyst from Kathmandu, Nepal, building a strong foundation in analytics, reporting, and data science.
My current focus is learning how to clean data, write better SQL, explore trends with Python, and present findings clearly through dashboards, charts, and structured analysis.
Data Philosophy: clean structure before cleverness, useful signal before visual noise, and every model or chart should end with a decision someone can actually act on.
This portfolio highlights the projects and practice work I am using to grow into data analyst, BI, and data science roles.
A loan default analysis project built with Python, Pandas, SQL, and SQLite, featuring a Power BI dashboard and a final measured default rate of 19.98% for outcome-focused risk reporting.
Business impact: clarified the top repayment-pressure signals behind a 19.98% final-outcome default rate and translated them into dashboard-ready risk reporting.
What I delivered: borrower segmentation, SQL-backed preparation, and a Power BI story layer for risk communication.
An in-progress analytics project focused on exploring access gaps in financial inclusion, identifying underserved segments, and building a reporting workflow around disparity trends and actionable insights.
Business impact: sharpens which underserved groups should be prioritized first when access, trust, and reporting quality diverge.
What I delivered: a clean gap-analysis framing, reporting structure, and a clearer decision path for inclusion monitoring.
Arrival counts alone can flatter demand while hiding whether local spend is actually improving.
Gandaki-style performance becomes more attractive when longer stays and solid occupancy move with arrivals instead of lagging behind them.
The better tourism KPI is not “who arrived”, but “who stayed, spent, and filled rooms at the same time”.
Risk teams often review default outcomes too late, after delinquency has already stretched across a segment.
Microbusiness and informal borrower patterns become more useful when delinquency is treated as an early signal rather than a lagging result.
The best intervention metric is the one that rises before the headline risk number does.
Higher transfer values can look healthy while the reliability of formal transfer channels quietly weakens.
Provinces with high household dependence but low formal-channel use deserve more attention than provinces with similar transfer volume but stronger systems.
Resilience is not just about how much money arrives. It is also about how safely and consistently it arrives.
If you are hiring for an internship, trainee role, or entry-level position in data analysis, BI, or data science, I would be glad to connect. You can use the form below or reach out directly through email or LinkedIn.
