I read your data
before it becomes a decision.
Data Analyst working across Excel, SQL, Python and Power BI — cleaning messy datasets, finding the patterns hiding in them, and building dashboards people actually use to make calls.
Turning raw, messy datasets into decisions people can act on.
I'm a Data Analyst who works across the full lifecycle of a dataset — from the first messy CSV export to the final dashboard someone opens every Monday morning. Most of my time isn't spent making charts; it's spent cleaning data, fixing inconsistent formats, removing duplicates, and making sure the numbers can actually be trusted before anyone builds a decision on top of them.
Once the data is reliable, I look for the patterns that matter — trends over time, customer segments, peak periods, underperforming products — and translate them into a small number of clear insights instead of a wall of numbers. I build that into interactive Power BI dashboards and Excel reports that let non-technical stakeholders explore the data themselves.
My background in Computer Science shaped how I approach this: I query with SQL, script the heavier analysis in Python with Pandas and NumPy, and always keep one question in front of me — what decision does this number need to support?
What I actually do with data.
Grouped by how each skill gets used in a real project, not just listed as buzzwords.
Data Analysis
Business Intelligence
Data Visualization
Programming & Querying
Tools
Approach
Case studies, not just dashboards.
Each project below moved from a raw, unreliable dataset to a specific business question being answered.
E-Commerce Sales & Customer Analysis
End-to-end analysis of an e-commerce transactional dataset, from raw export to a Power BI report on revenue and customer behavior.
- Cleaned and prepared the raw dataset to build a reliable analysis base.
- Wrote SQL queries to aggregate revenue, order volume, and customer segments.
- Identified top-performing products and categories.
Pizza Sales Performance Dashboard
Analysis of restaurant order data to uncover demand patterns across time, day and product size.
- Cleaned and structured order data using Excel PivotTables and VLOOKUP.
- Used SQL to analyze order trends by time of day, day of week, and pizza category/size.
- Designed a Power BI dashboard visualizing revenue, order volume and product mix.
Online Sales Performance Analysis
Statistical and exploratory analysis of online store sales data to identify trends, seasonality and outliers.
- Performed exploratory and statistical analysis in Python to identify sales trends, seasonality and outliers.
- Queried and joined multiple SQL tables (orders, products, customers) into a consolidated dataset.
- Visualized key metrics in Matplotlib during exploration, then built a final Power BI report.
A Power BI–style dashboard you can actually filter.
Sample retail dataset — change the year range or toggle a group below and every card, chart and KPI recalculates live.
Business Performance Overview
Total Sales
Total Sales
Sum of Cost
Sales vs Target
How I can help your team or business.
Practical, scoped work — you'll always know exactly what you're getting.
Data Cleaning
I take messy, duplicated or inconsistent raw data and turn it into a dataset you can actually trust and build on.
Excel Analysis
PivotTables, formulas and structured reporting for teams that need fast, reliable answers without a full BI setup.
Power BI Dashboards
Interactive dashboards built on a proper data model, so you can filter, drill down and explore your own numbers.
SQL Analysis
Writing and optimizing queries to pull the exact answers you need out of your database — no more manual spreadsheet exports.
Python Data Analysis
For deeper or repeatable analysis — exploratory work, statistical checks and automated cleaning scripts using Pandas and NumPy.
KPI Reporting
Defining the metrics that actually reflect your business, then building the reporting that tracks them consistently.
How I handle a data analysis project.
The same six steps, every time — it's what keeps the results reliable.
Understand
Getting clear on the actual business question before opening a single spreadsheet.
Clean
Removing duplicates, fixing formats and handling missing values so the data can be trusted.
Explore
Profiling the dataset to understand its shape, distributions and early red flags.
Analyze
Running the SQL queries and Python analysis needed to actually answer the question.
Visualize
Building the dashboard or report so the findings are easy to explore, not just read once.
Deliver
Walking through the insights and making sure the people using the dashboard know how to read it.
The stack behind the analysis.
What the analysis actually looks like.
Illustrative snippets in the style of my real workflow — cleaning in Python, aggregating in SQL, reporting in Excel.
| order_date | revenue |
|---|---|
| 2024-08 | 184,320 |
| 2024-09 | 201,760 |
| 2024-10 | 179,940 |
| 2024-11 | 228,510 |
| category_name | total_sales |
|---|---|
| Beverages | 96,410 |
| Snacks | 81,225 |
| Dairy | 67,940 |
| A | B | C | |
|---|---|---|---|
| 1 | Category | Revenue | Growth |
| 2 | Beverages | 96,410 | +12% |
| 3 | Snacks | 81,225 | +6% |
| 4 | Dairy | 67,940 | -3% |
Want the full breakdown of my experience?
My CV covers my technical skills, certifications and project details in full. Download the PDF directly, or email me if you'd prefer I send it over.
Where the skills come from.
Have a dataset that needs a second look?
Whether it's a one-off analysis, a recurring dashboard, or a full-time role — I reply within a day. Reach me directly, no forms in between.