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Report involved checking of data from various other reports. Excel tools like x-look up, sorting, filtering, data cleaning etc. is involved
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BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
Crystal Homes: Real Estate Sales and Rental Analytics Dashboard
End-to-end analytics project for a fictional Nigerian real estate agency. Raw data from Google Sheets went into MySQL, where I built 21 SQL views, then into a 4-page Power BI dashboard covering sales performance, active rental income, lease expiry tracking, portfolio health, and agent/branch performance. It surfaced findings like ₦468M in tracked revenue and cross-branch sales attribution.
Tools: MySQL, Power BI, SQL, Google Sheets
Behind every clean chart is a messy spreadsheet 🧹
This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
