If data is the language of the modern world, Excel is still its alphabet.
Excel remains the foundation of analytics, where most data professionals first learn to organise, process, and visualise data. In 2025, it's more powerful than ever: cloud-connected, powered by Power Query and DAX, and even capable of natively running Python. Whether you're a student, a job changer, or an ambitious data analyst, Excel will teach you to think in terms of data.
One of the finest methods to learn Excel for free today is Luke Barousse's 10-hour YouTube tutorial, which takes you from complete beginner to analytics-ready professional. Below is a synopsis of what you'll study in each level, why it's important for anyone pursuing a data career, and how to continue learning after you finish the course.
1. Getting Started — Building Confidence in Spreadsheets
The trip begins with the fundamentals: learning how worksheets, workbooks, and the Excel interface interact. These are the digital equivalent of a carpenter learning their tools: the grid that houses all data. Understanding how to navigate, format, and structure information appropriately lays the groundwork for everything that follows.
2. Formulas and Functions — Speaking the Language of Data
Formulas are Excel's grammar, transforming raw data into insight. You will master logical, mathematical, statistical, and text-based functions for converting static tables into dynamic models. Most individuals begin to "think analytically" at this point, because you're doing more than just typing numbers; you're also connecting them. Once learnt, formulas provide you with the ability to analyse patterns, validate data, and automate repetitive calculations.
3. Visualization — Turning Numbers into Stories
Charts and pictures are what make data understandable. Excel allows you to convert thousands of data points into understandable visualisations, ranging from basic bar charts to complex dashboards. You'll learn how to create graphics that emphasise patterns, track progress, and show others what you see in the data. Great analysts do more than just crunch numbers; they also communicate eloquently, which is where that expertise begins.
4. Advanced Spreadsheet Skills — Efficiency and Professionalism
Once you've learnt the fundamentals, the next step is to discover how experts manage data at scale. Tables, formatting, and collaboration tools enable you to structure enormous datasets while keeping everything clear and consistent. These features also help you prepare for cooperation, which involves sharing, updating, and presenting material across departments. These features distinguish a casual Excel user from an expert.
5. Building Your First Dashboard — Applying What You’ve Learned
The greatest approach to understand analytics is to make something. In Luke's first project, you'll develop an interactive salary dashboard using real-world techniques, integrating calculations, charts, and formatting to produce something both visual and functional. Projects like this translate theory into muscle memory and exhibit real understanding.
6. PivotTables — Analysing Data at Scale
PivotTables are among Excel's most powerful analytical tools. They enable dynamic summarisation, grouping, and exploration of data without the need for coding. Mastering them allows you to respond promptly to enquiries such as "What region performed best?" or "Which product drove the most revenue?" It's a crucial ability for anyone working with massive datasets or creating frequent reports.
7. Advanced Data Analysis — Thinking Statistically
Once you've mastered formulae and PivotTables, you may begin to investigate Excel's analytical add-ins. These tools enable you to do regressions, simulations, and sensitivity analyses, bridging the gap between spreadsheets and statistical modelling. You'll also learn how to develop "what-if" scenarios, which are an essential component of any organization's decision analysis.
8. Power Query — Automating Data Cleaning
Data rarely arrives prepared for analysis. Excel's built-in data transformation engine, Power Query, allows you to clean, integrate, and rearrange data without having to repeat it manually. It's one of the most significant current Excel tools for data analytics, teaching you how to think like a data engineer and create repeatable, dependable workflows. Once you have mastered Power Query, you will be able to automate processes that previously took hours.
9. Power Pivot and DAX — Modelling Relationships and Metrics
Power Pivot takes Excel beyond the spreadsheet, allowing you to connect numerous tables and establish relationships across information. Using DAX (Data Analysis Expressions), you'll learn how to create dynamic calculations such as "year-over-year growth" and "rolling averages." This turns Excel into a lightweight data warehouse, getting you ready for products like Power BI.
10. Portfolio Project — Sharing and Documenting Your Work
In the final project, you will use all of your skills to analyse pay data and present your findings publically. Luke presents version control using GitHub and demonstrates how to document projects with a README file. This may appear insignificant, but it is a vital professional habit: showing others (and potential employers) how you tackled a problem.
🎥 Watch the Full Course
You can follow the complete 10-hour learning path for free here:
Luke Barousse Excel Course - 10 Hours
This post is not associated with Luke Barousse; all credit for the video material belongs to him. This guide merely curates his course as part of a free educational path for aspiring data professionals.
Expanding Your Skill Set
After finishing the course, continue by examining real datasets on platforms such as Kaggle or Data.gov. Try constructing the dashboards with new data or reproducing visualisations found online. From there, you can use Excel's Python interface or link it to Power BI to test end-to-end analytics workflows.