Tools to Learn for Data Analysis (and the Order to Learn Them)

The short answer

Learn Excel → Power Query → Power Pivot → SQL → Power BI. Add Python later if your job needs it. The first three all live inside Excel, so you can start today with software you probably already have.

Tools at a glance

Excel

Excel is where you learn the logic of analysis: filtering, grouping, summarising and charting. Focus on tables, lookup functions, SUMIFS and COUNTIFS, PivotTables and dynamic array functions.

Useful pages: Excel functions · Excel shortcuts

Power Query

Power Query records each cleaning step: removing blank rows, splitting columns, fixing data types, combining files from a folder. Next month, when new data arrives, you press Refresh instead of repeating the work. Find it under Data › Get Data.

Power Pivot and the Data Model

Power Pivot stores data in a compressed Data Model instead of on a worksheet, so the 1,048,576-row limit no longer applies. You can link tables with relationships and write DAX measures. Learn Power Pivot in Part 3 →

SQL

Most companies store data in databases, and SQL is the common language to query them. A few commands cover most daily work: SELECT, WHERE, GROUP BY and JOIN.

SELECT region, SUM(amount) AS total_sales
FROM sales
WHERE order_date >= '2026-04-01'
GROUP BY region
ORDER BY total_sales DESC;

Power BI

Power BI Desktop combines Power Query and the same Data Model engine as Power Pivot, then adds interactive visuals. If you know Power Pivot, you already know half of Power BI.

Python

Python with pandas is useful for automation, very large files and statistical work. Many analyst roles do not require it. Learn it after the core tools, or when a job you want asks for it.

AI assistants

Copilot in Excel, ChatGPT and Claude can draft formulas, DAX and SQL and explain errors. Treat them as a fast assistant: check every answer against your data.

FAQ