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Completed Google's Foundations of Business Intelligence course through Coursera, covering core BI concepts, data-driven decision making, and how businesses turn raw data into actionable insights. This builds directly into my data analysis work with Python and R, helping me structure reports and dashboards the way real business teams actually use them.
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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.
Recently I completed a very interesting - Quantile, an AI backed Business Analytics Software. Chat with your data - and build dashboards through code. Check it out!
quantile is a fun name. chat to dashboards is a cool idea
Built an automated weather monitoring system that logs conditions to Google Sheets based on temperature thresholds.
Instead of manually checking weather data and logging it, I built a Make.com scenario that:
Pulls current weather data on a schedule (Weather module)
Routes the data based on temperature conditions
Logs the results into different Google Sheets tabs/rows depending on which condition is met
Result: Weather data is now tracked automatically and organized by condition — no manual checking or data entry required, and the log stays consistently up to date.
