BI & Reporting
Decision support through visual hierarchy, filters and business-friendly reporting in Power BI and Tableau.
Toolkit & capability
I group my skills the way work actually happens: get the data right, model and query it, visualise it for a decision, and translate the result into something a business team will act on.
Capability in depth
I use visual analytics to make patterns easy to interpret. My retail and co-op projects show how I organise information for decision support — clear hierarchy, the right chart, and self-serve filters — not just charts on a page.
Reliable reporting starts with reliable data. I focus on definitions, joins, aggregation, validation and clearly stated assumptions before any result is presented.
Python is where I go when spreadsheets stop scaling. I've used it for cleaning, exploratory analysis and a full modelling pipeline (used-car price prediction), and I keep building repeatable workflows.
This is the bridge between technical work and the decision. I developed it through team projects and life beyond university, where communication, patience and adaptability decide whether analysis actually gets used.
At a glance
Decision support through visual hierarchy, filters and business-friendly reporting in Power BI and Tableau.
Extraction, joins, aggregation, window functions and structured questioning of data.
Clean inputs, consistent definitions, stated assumptions and validation before decisions.
Analysis, validation, quick modelling and structured communication of outputs.
Pandas cleaning, EDA and regression pipelines with scikit-learn and XGBoost.
Turning technical outputs into clear business recommendations and next steps.
Every capability here shows up in a real project — from retail margins to fraud detection.