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This portfolio showcases 3 real-world data processing, and Excel reformatting projects completed with 100% data accuracy and strict attention to detail. It highlights my ability to take messy, unstructured, or scanned raw data and transform it into clean, structured, analysis-ready spreadsheets.
Included Work Samples in this Portfolio:
Employee Attendance Data Cleaning & Standardization: Reformatting raw pipe-delimited text records into clean, column-separated Excel sheets with standardized titles, capitalizations, department classifications, and timestamps.
Client & Transaction Data Restructuring: Parsing unstructured financial transaction strings into a structured financial ledger with separated Transaction IDs, Client Names, Entity Statuses, and Amounts.
Hotel Booking Data Reformatting: Converting tilde-separated (~) booking strings with inconsistent delimiters into a filterable booking table with standardized dates and status tracking.
The network for creativity
Join 1.25M professional creatives like you
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Related posts
Excel PivotTable — Sales Summary
Client had raw sales data (14 rows across UK/USA, split by quarter) and needed it turned into something actually usable. I cleaned it up and built a PivotTable that breaks total sales down by rep and country, with grand totals for both.
What I did:
Structured the raw data into a proper table (consistent headers, currency formatting)
Built a PivotTable summarizing Sum of Sales by Last Name and Country
Used SUMIFS so the totals update automatically if the source data changes — nothing hardcoded
Checked every formula for errors before delivery
Delivered as a working .xlsx file, ready to plug into a bigger report or dashboard.
If you've got messy sales/ops data sitting in a spreadsheet and need it turned into something you can actually read at a glance, this is exactly the kind of thing I can help with.
Cleaned and validated a synthetic operational dataset containing duplicate records, missing values, invalid entries, and inconsistent formatting. Documented each issue, standardized the data, removed duplicates, and created a data-quality summary to demonstrate a practical end-to-end cleaning and validation workflow.
