"I Developed A SQL-Driven Monitoring System For A Global Supply Chain Network. By Writing Complex Queries To Analyze Inventory Levels Across Multiple International Warehouses, I Created An Automated Flagging System For Low-Stock Items. This Project Demonstrates My Ability To Handle Large-Scale Relational Databases And Provide Actionable Logistics Insights."
YOUR DASHBOARD MIGHT BE LYING TO YOU.
Not because Power BI is wrong.
Because the data underneath it is.
A beautiful dashboard built on messy data doesn't create better decisions.
It creates confident mistakes.
Then someone adds a few colorful charts, calls it "analytics," and moves on.
I don't work that way.
I take the mess first.
RAW DATA → CLEAN → TRANSFORM → MODEL → ANALYZE → POWER BI → INSIGHTS
I work with Excel, CSV and business datasets to:
→ Clean and validate messy data
→ Transform data using Power Query
→ Combine multiple files and sources
→ Build data models and DAX measures
→ Create interactive Power BI dashboards
→ Identify trends, KPIs and business insights
→ Build reporting workflows that are easier to refresh and maintain
Because a dashboard shouldn't just look impressive.
Every KPI should answer a question.
Every visual should have a purpose.
And every number should be trustworthy.
That's the service I'm offering.
If your business data is sitting across messy Excel files, CSVs or scattered spreadsheets, I can turn it into:
Clean Data → Clear Analysis → Interactive Power BI → Better Decisions
You bring the data.
I'll find what it's trying to say.
📊 Power BI Dashboard & Data Analytics
Now available for freelance projects on Contra.
I actually 99.99% agree,
Furthermore Outliers removal is part of cleaning datasets we choose to begin a project, Descriptive statistics is a key for detection
What makes an A/B test readout useful to a product team?
My preferred first page answers four questions:
What changed, and by how much?
How uncertain is the estimate?
Did an important guardrail get worse?
What decision does the evidence support, and what remains unresolved?
A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.
This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.
I recently worked on a retail sales dataset to demonstrate how raw business data can be transformed into useful insights.
My process included:
✅ Data cleaning and validation
✅ Revenue calculations
✅ Sales performance analysis
✅ Product and store analysis
✅ Excel/Power BI visualization
✅ Identifying actionable business insights
The analysis answers questions such as:
• Which store generates the most revenue?
• Which products drive sales?
• Which products sell the most units?
• Which salesperson performs best?
• How do sales change over time?
Tools: Excel | Power BI | Python/Pandas | SQL
Clean data is the foundation. Good analysis turns that data into decisions.