How to learn Power BI for free in 2026 (real path, no fluff)

TL;DR

• Microsoft Learn is the best free resource (official, updated, with labs)
• Don’t start with 40-hour courses. Start with a real project
• PL-300 certification is worth it only if your company pays
• 80% of your value is in understanding the data, not making pretty charts
• After basics: DAX and Power Query are what set you apart

---

Let me be direct: there are way too many Power BI courses that teach you to make pretty charts and leave you just as lost when your boss asks for “a sales dashboard.”

This guide is what I wish I had when I started. No fluff, no BS, just what works.

Why Power BI in 2026

Power BI remains the most used BI tool in enterprises. Not because it’s the best at everything, but because:

1. It’s in the Microsoft ecosystem (where 90% of the corporate world lives)
2. Desktop version is free (you only pay for cloud sharing)
3. There’s real job demand (check LinkedIn job postings)

If you work with data in a company that uses Excel, Outlook, or Teams, Power BI is the next tool you should master. No debate.

The learning path (without paying anything)

Phase 1: Fundamentals (2-3 weeks)

Microsoft Learn - Power BI Path learn.microsoft.com/training/powerplatform/power-bi

It’s free, official, updated, and has hands-on labs. You don’t need more to start.

Key modules:

• Introduction to Power BI
• Data modeling
• Data visualization
• Basic DAX

Why this and not YouTube: YouTube courses are fine for seeing “what’s possible,” but Microsoft Learn forces you to do exercises. You learn by doing, not watching.

Phase 2: Your first real project (1-2 weeks)

This is where 90% of people get stuck. They’ve watched 20 hours of tutorials but never built anything of their own.

My recommendation: Get data you actually care about.

Ideas:

• Your personal expenses (export from your bank)
• Data from your job (if you can)
• Public dataset you’re interested in (sports, music, whatever)

The goal isn’t to make something pretty. It’s to face real problems:

• Dirty data that needs cleaning
• Table relationships that don’t work
• Metrics you don’t know how to calculate

When you get stuck (and you will), search for the solution. That’s real learning.

Phase 3: Power Query and DAX (ongoing)

Once you have the basics, the next level is:

Power Query: Transform and clean data. 80% of real work is here, not in charts.

DAX: The formula language. It’s what lets you calculate things that don’t come directly from the data.

Don’t try to learn everything at once. Learn what you need when you need it.

Need to calculate last year’s sales? Search “DAX same period last year”. Need to combine two tables? Search “Power Query merge”. That’s how you really learn.

Free resources I recommend

Official

• Microsoft Learn - Already mentioned, but it’s the best starting point
• Official DAX documentation - docs.microsoft.com/dax - Reference when you need a specific function

YouTube (to complement, not as foundation)

• Guy in a Cube - Two Microsoft guys who explain well
• SQLBI - Marco Russo and Alberto Ferrari, the DAX references (more advanced)
• Curbal - Ruth Pozuelo, very didactic

Communities

• Reddit r/PowerBI - Real questions and answers
• Power BI Community - Microsoft’s official forum
• LinkedIn - Follow people who post useful content, not those who just sell courses

Datasets to practice

• Kaggle - Thousands of free datasets
• data.gov - US public data
• Maven Analytics - Challenges with prepared datasets

PL-300 certification: is it worth it?

The honest answer: it depends.

Worth it if:

• Your company pays for it
• You’re job hunting and need to pass CV filters
• It forces you to study things you’d otherwise ignore

Not worth it if:

• You have to pay out of pocket ($200+)
• You already have a job and your company doesn’t value it
• You think it’ll make you an “expert” (it won’t)

The certification proves you know the basics. It doesn’t prove you can solve real problems. Only your work proves that.

If you decide to take it, Microsoft Learn has all exam content for free. You don’t need to pay for prep courses.

Common mistakes (that I made)

1. Obsessing over charts

Pretty dashboards are useless if the data is wrong. Prioritize: clean data > correct model > visualization.

2. Not understanding the data model

Power BI is not Excel. Table relationships matter. If you don’t understand this, everything else fails. Read about relationships and why you sometimes need USERELATIONSHIP.

3. Copying formulas without understanding them

DAX has a learning curve. If you copy formulas from the internet without understanding what they do, when they fail (and they will) you won’t know why. I have a debugging guide for these cases.

4. Ignoring Power Query

Many jump straight to DAX and do acrobatics to clean data with formulas. Mistake. Power Query exists for that and is more efficient.

5. Learning without a project

Watching tutorials is comfortable. Building your own project is uncomfortable. But only the latter teaches you for real.

My final advice

You don’t need paid courses to learn Power BI. You need:

1. A real project you care about
2. Ability to search when you get stuck
3. Patience to make mistakes and learn

The value of someone who knows Power BI isn’t in making charts. It’s in understanding what questions to ask the data and knowing if the answers make sense.

No course teaches that. You learn it by working with real data, making mistakes, and asking yourself “does this make sense?” before showing any number to your boss.

---

Next step

If you already master the basics and want to go beyond dashboards:

• DAX in depth - For calculations you don’t know how to do
• Power Query complete - To transform data like a pro
• Why 90% of your data is garbage - The reality nobody tells you

The natural path after Power BI is Data Engineering: data pipelines, automation, data quality. If you’re interested in that leap, I wrote about my transition from analyst to Data Engineer and why it’s worth it.

And if you want to understand where analytics is heading with AI, read about GenBI and the future of data analysts. Spoiler: knowing how to model semantic layers will be more important than writing SQL.

---

Have questions about your learning path? Stuck on something specific?