AIFEDERS LEARNING GUIDE
Excel to Power BI: A Practical Data Analysis Learning Path
Start a data analysis course with a question you want a report to answer. For example: which products received the most orders this month, and how does that compare with last month? Build a small, explainable answer in Excel, then use Power Query and Power BI when you need repeatable preparation, relationships and interactive reporting.
By Aifeders
Learn clean tables before complex dashboards
Begin with consistent column headings, one row per observation and a clear definition of what a row represents. Practice sorting, filtering, simple formulas and a PivotTable. Keep IDs as identifiers, and decide whether blanks mean unknown, not applicable or zero. Those choices affect your totals before any chart is drawn.
Compare courses by their exercises: can you inspect the input data, repeat the steps and explain the answer? Check which Excel edition, operating system and Power BI tools the lessons use. Confirm any software or account requirements separately from course access; a listing does not establish that every required tool or sharing feature is included.
Handle dates and currencies across regions
For a practice dataset, create fictional orders in USD, GBP, CAD and AUD. A dollar sign alone does not identify a currency. Keep a separate currency-code column and calculate totals within each currency. Changing a number’s display format does not convert its value. If you practise conversion later, document the example exchange rate, its date and the calculation.
In Power Query, locale affects how text becomes a date or number. A day/month/year source can be misread under month/day/year settings. Use the source’s documented convention when changing the column type with a locale, then check extracted day, month and year values. English files from different teams, including Canadian and Australian teams, still need their formats checked rather than guessed from the country.
Move from preparation to a clear model
Use Power Query to record repeatable preparation steps: set types, remove genuinely invalid records, standardize labels and preserve enough information to investigate errors. Keep a count of rejected records. Try refreshing with an extra month of data; needing to repair every row manually is a useful sign that the preparation needs improvement.
Then learn how a fact table of orders relates to dimensions such as products and dates. Microsoft’s star-schema guidance separates tables used to filter and group from tables used to summarize. Practice one-to-many relationships and simple measures before adding complicated calculations. Verify that a product filter changes the expected rows and does not double-count an order.
Practice project: an explainable monthly report
Build a one-page report with order count, units sold and monthly trends, plus a currency selector for monetary totals. Include a short note naming the date range, the meaning of each measure and the last successful refresh. Make the report readable without relying on colour alone.
- Reconcile the cleaned row count with the source and the rejection log.
- Compare one month’s totals with a simple Excel calculation.
- Check a missing product ID, a duplicate order and a month with no orders.
- Label monetary values with currency codes and explain any conversion.
- Refresh using a new input file and confirm that filters still work.
Choose lessons that close a specific gap
If the report is correct but difficult to update, prioritize Power Query. If filters give confusing results, study relationships and measures. If the answer is hard to read, practise chart choice and explanation. Use the catalog’s listed topics, editions and previews to compare those needs. Keep a short record of what you built and verified; it makes your next learning decision concrete.