Power BI Project · Advanced · 4 to 6 hours · VIP
Time intelligence measures in Power BI
A proper date table, then YoY, YTD, margin, and a moving average in DAX. The measures every analyst is expected to write, and the filter-context reasoning behind them.
The brief
A coffee retail chain runs five stores and sells across four categories, two full years of daily sales. Leadership wants a report that answers time questions on demand: how are we tracking year over year, what is year-to-date, what is margin, and what does the trend look like once the December spike is smoothed. This is a DAX time-intelligence project, and it starts where most beginners stumble: without a dedicated, marked date table, functions like SAMEPERIODLASTYEAR and DATESYTD quietly return the wrong answer.
Your role
You are the BI analyst. Deliverable: a one-page Power BI report with correct time-intelligence measures and a short note on the trend behind the seasonality.
The dataset
731 days (Jan 2024 to Dec 2025) of daily sales for 5 stores and 4 categories
powerbi-daily-sales.csv · 14,620 rows
Columns: date, store, category, units, revenue, cost
Setup
- Open Power BI Desktop (free) and load powerbi-daily-sales.csv.
- Create a separate Date table covering the full range and mark it as the date table.
- Relate the Date table to the sales table on the date column.
VIP project
The full brief for this Power BI project is for VIP members
The first Power BI project is free for every member. The dataset, the tasks, the answer key, and the graded skills check for this one are part of the VIP tier, along with the other 8 locked projects. 5 projects stay free, one for each tool, so you can try every tool before you decide.
VIP access comes with the bootcamp. Enroll once, and every locked project opens, alongside the live sessions, the replays, and the certificate.
Work like an AI-powered analyst
The modern analyst uses AI as a thinking partner, not a shortcut that skips the learning. Try these on this project.
- Ask ChatGPT or Claude why SAMEPERIODLASTYEAR needs a marked, gap-free date table, and what breaks without one.
- Paste a measure that returns the wrong number and have the AI walk the filter context with you rather than just fixing it.
- Have the AI explain why margin must be a measure, not a calculated column averaged across rows.
Finished it? Put it in your portfolio.
This is exactly the kind of output the bootcamp builds with you live, with mentor feedback and an AI badge and certificate of completion at the end.