After 90 min: An automated spreadsheet that calculates and analyzes data without manual work
Master Power Query for ETL
After 90 min: Automated data pipelines that extract, transform, and load data from multiple sources
Master Power Query for ETL is a technical skill that opens real doors once you have it. This 90-minute plan is designed for those with solid prior experience — you can complete it from the comfort of home with the materials listed above, no special background required. The goal is not to leave you with theoretical knowledge but with a tangible, lived experience: by the end of this session, you will automated data pipelines that extract, transform, and load data from multiple sources. That concrete outcome is what separates structured plans from casual self-study — you always know what you're working toward and whether you've arrived.
The session moves through 5 carefully ordered steps, covering explore power query, extract data, transform data, and write custom m code. Each block has a specific time window so you know exactly how long to spend before moving on. The sequencing is intentional: early steps build foundational awareness and muscle memory, while later steps apply those fundamentals under slightly more demanding conditions — the same way a skilled instructor would structure a first lesson. By the time you reach the final step, you will have touched every core element of master power query for etl at least once.
One thing most beginners miss: Test transformations step-by-step. Keep queries modular and reusable. Keeping that in mind throughout the session will dramatically improve your results. After this 90-minute foundation session, you'll have a clear picture of which aspects of spreadsheets feel natural and which need more deliberate practice. That self-knowledge is the most valuable thing you take away — it turns a one-off session into the start of a genuine learning path.
What you need
The 90-Minute Plan
Open Power Query editor. Understand M language basics.
Connect to multiple sources (CSV, SQL, APIs). Import data.
Clean, filter, merge, and pivot data using Power Query transformations.
Create functions and complex transformations using M language.
Automate refresh cycles. Build dashboard. Next: integrate with Power BI.
Test transformations step-by-step. Keep queries modular and reusable.
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