Excel vs Power BI: Which Should You Learn First?

If you are new to data analysis, learn Excel first, but not all of Excel, and not for long. Get comfortable with tables, formulas and PivotTables, then learn Power Query and the data model inside Excel, and move to Power BI from there. That order works because the two products share more than many comparisons suggest: the skills you build in the second half of your Excel learning are Power BI skills. Start with Power BI instead if you already use Excel confidently, or if your target job is building reports and dashboards.

This article is about learning order. If you are choosing which tool to use for a particular task at work, our Power BI vs Excel comparison covers that question.

Why the choice is smaller than it looks

Excel and Power BI share two engines:

• Power Query, the tool for connecting to, cleaning and reshaping data. It is the same engine, with the same M formula language, in Excel for Windows, Excel for Mac and Power BI, according to Microsoft's Power Query overview.
• The data model and DAX, the tables, relationships and measures that power a report. In Excel this is the Data Model, managed with Power Pivot, which Microsoft lists for Excel 2016 and later versions, including Microsoft 365.

The clearest evidence is what happens when you move work between them. Power BI Desktop can import an Excel workbook that uses Power Query and Power Pivot. The queries, relationships, calculated columns and measures come across as their Power BI equivalents. Skills learned in that layer of Excel are not "Excel skills" you later replace. You carry them straight into Power BI.

What each one teaches that the other doesn't

Both columns matter in analyst jobs. The left-hand column is where many people start, because it gives immediate, visible feedback: type a formula, see a number. The right-hand column assumes you already think in tables and measures rather than in cells.

Which to start with, by situation

A learning order that avoids learning things twice

1. Excel foundations. Tables (Insert > Table), sorting and filtering, core formulas (SUMIFS, COUNTIFS, IF, and a lookup function), PivotTables and basic charts. Our guides to Excel formulas for data analysis and PivotTables cover this stage.
2. Power Query in Excel. Import a messy CSV, clean it, combine a folder of monthly files into one table, and refresh it when a new file arrives. The steps you learn here work the same way in Power BI's Power Query Editor. See our Power Query in Excel guide.
3. The data model. Load two or three related tables, create relationships, and write your first measures. You can do this in Excel with Power Pivot, or treat it as the moment to switch to Power BI Desktop. The concepts, which are star schemas, relationships and measures, are identical. Our article on the star schema in Power BI explains the model you are aiming for.
4. Power BI Desktop. Import the workbook from step 3, or rebuild it, and design an interactive report around a question someone actually asks. Our first Power BI report walkthrough is a gentle start.
5. The Power BI service. Publish, set up a refresh, and share. Plan for licensing here. Power BI Desktop is free, and a free licence lets you create content for your own use in the service. Sharing and collaborating with others requires a Pro or Premium Per User licence, unless your organisation hosts the content on Premium or Fabric capacity (licence types).

Steps 2 and 3 are the ones people skip, and they are the ones that make the switch to Power BI easy. Someone who jumps from Excel formulas straight to Power BI visuals often builds reports that mirror a spreadsheet: one flat table and many calculated columns. Someone who learned the data model first builds a proper star schema.

Mistakes that slow people down

• Learning both interfaces at a surface level. Clicking through two tools' menus teaches little. Depth in the shared layer, Power Query and modelling, transfers; menu knowledge doesn't.
• Treating Power BI as "Excel with nicer charts". The visuals are the smallest part of Power BI. The model underneath decides whether a report is correct and fast.
• Staying in Excel because the data got bigger. A worksheet holds at most 1,048,576 rows (Excel specifications). When you reach for workarounds to handle more data, load it into the data model or into Power BI instead of splitting it across sheets.
• Skipping SQL. Whichever tool you start with, analyst roles commonly expect SQL too, because that is how you get data out of company databases.

The short version

Excel first for most beginners, because it teaches aggregation with instant feedback. Move quickly to Power Query and the data model, because they are shared with Power BI. Then switch to Power BI for modelling, report design and sharing. If you already use Excel comfortably, start with Power BI now. For the full set of skills around these tools, see the data analyst roadmap. Both tools, together with Power Query and SQL, are part of the GloryTecks Data Analytics course.