Microsoft Power BI
Build reports people actually trust — fast, governed, and backed by a data model that holds up when someone questions the number. Anyone can drag a field onto a canvas; the people who get hired build the data model underneath it. For fresh graduates from any degree background and working professionals alike — no programming background required.
There are thousands of Power BI courses, and most of them teach the same thing: connect data, make charts, publish a dashboard. This is not one of them. Anyone can drag a field onto a canvas. The people who get hired build the data model underneath it — and can explain why the number is right. That claim is the whole course: star schema design, DAX filter context, performance and governance get the teaching time; visuals are the last mile, not the curriculum.
Four things set this program apart from the commodity syllabus. Modelling and DAX depth — relationships, filter context and a proper date table are the actual skill. Performance — slow reports are the number one reason deployments lose trust, so Import vs DirectQuery vs Direct Lake, cardinality and measure optimisation get real teaching time. Enterprise delivery — row-level security, deployment pipelines and certified semantic models: the difference between an analyst who makes a report and a BI developer a company depends on. And Fabric and Copilot, handled honestly — Copilot accelerates a good semantic model and amplifies the errors in a bad one, so modelling matters more now, not less.
It is designed for two audiences at once. If you are a fresh graduate — from any degree background, computer science or not — this is one of the few technical careers reachable without a programming background, and you graduate with a portfolio of real reports. If you are a working professional — in finance, operations, marketing, HR or supply chain, or an engineer pivoting to data — your domain knowledge plus real modelling skill is exactly the combination hiring managers struggle to find. The program runs 8 weeks (~60 hours), live and build-along, closing with a governed capstone you defend in a stakeholder walkthrough.
Where This Fits in Our Catalogue
- Microsoft Power BI (this course) — the analytics entry point: turn governed data into decisions people trust.
- Snowflake Data Warehouse — move upstream: build and govern the platform your reports read from.
- Apache Spark & PySpark — move from consuming data to engineering it at scale.
Course at a Glance
- Duration: 8 weeks | ~60 hours | live + hands-on
- Format: Live online · build-along · portfolio of real reports
- Prerequisite: Comfort with spreadsheets. Basic SQL helpful but taught as needed. No programming background required.
- Curriculum: 9 modules, from the Power BI landscape to Fabric, Copilot and capstone
- Certification: Aligned to PL-300: Microsoft Power BI Data Analyst
- Curriculum reviewed: July 2026 — the Power BI and Fabric stack moves fast; this syllabus tracks the current product
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Modelling first: star schema, filter context and a proper date table before any dashboard
- DAX to real depth — including debugging a measure that returns the wrong number
- Performance as a discipline: Import vs DirectQuery vs Direct Lake, VertiPaq, DAX Studio
- Enterprise delivery: RLS, deployment pipelines, certified semantic models, governance
- Fabric and Copilot handled honestly — AI amplifies your model, good or bad
- Analytical communication: design for a decision, then defend your numbers
Beyond the Dashboard
The last skill most courses never teach: analytical communication. Choosing the right visual for the question being asked, designing a page for a decision rather than for decoration, and standing in front of stakeholders and defending your numbers when someone asks "are you sure that's right?" — every project in this course ends with that conversation, and the capstone is defended live.
Hands-on Projects & Portfolio
Star-schema-first, with YoY, margin and rolling-average measures and a proper fiscal calendar.
Inconsistent files and systems cleaned entirely in Power Query with folding preserved.
Designed for a specific decision, with drillthrough to detail.
Cut a slow, bloated model's load time, with before/after evidence from Performance Analyzer and DAX Studio.
Row-level security by region, an app with audiences, and a dev/test/prod deployment pipeline.
A Direct Lake report over Fabric lakehouse data, compared against an Import model.
End-to-end governed report plus a stakeholder presentation defending the numbers.
Tools & Stack
Power BI Desktop Power BI Service Power Query / M DAX Microsoft Fabric OneLake Direct Lake Copilot in Power BI Tabular Editor DAX Studio Power BI Gateway Excel SQL Server Azure SQL SharePoint Dataverse
Certification Alignment
- Syllabus mapped to PL-300: Microsoft Power BI Data Analyst across its published skill areas — preparing data, modelling data, visualising and analysing data, and managing and securing Power BI
- DP-600 (Fabric Analytics Engineer) is the natural next step toward enterprise-scale analytics engineering
- Presented as preparation and alignment — no official partnership or accreditation implied, and no exam outcome guaranteed
Career Outcomes
Power BI is the default analytics layer in organisations standardised on Microsoft 365, which makes the skill broadly employable across industries and functions rather than concentrated in tech companies — and it is one of the few technical roles reachable from a non-engineering background. The durable differentiator is modelling and DAX, not visual design: that is what separates a report builder from a BI developer. This course targets:
- Power BI Developer
- Data Analyst
- Business Intelligence Analyst
- BI Developer
- Analytics Engineer
- Reporting Analyst
- Data Visualisation Specialist
- Fabric Analytics Engineer (with DP-600)
Who This Course Is For
- Final-year students and fresh graduates targeting analyst roles, from any degree background
- Excel-heavy finance, operations, marketing, HR and supply chain professionals moving into analytics
- Software engineers, testers and support engineers pivoting toward data roles
- SQL developers and report writers modernising from legacy BI tools
- Team leads and managers who commission dashboards and need to judge whether they're any good
- Anyone preparing for PL-300
Material Includes
- Class recordings
- Practice datasets & starter model files
- DAX pattern library & model-review checklist
- Certificate
Prerequisites
- Comfort with spreadsheets
- Basic SQL helpful, taught as needed
- No programming background required
Estimated Time: 5 Hours
You ship:- Your first published report — and an annotated map of the end-to-end path a number takes from source system to executive slide
- Desktop, Service and Mobile; how Power BI sits inside Microsoft Fabric
- Workspaces and capacities; semantic models as a shared asset
- The end-to-end path from source system to executive slide
Estimated Time: 7 Hours
You ship:- A messy-source rescue: multiple inconsistent files and systems cleaned entirely in Power Query, with query folding preserved and refresh time measured
- Connecting to files, databases, cloud services and APIs; the M language where the UI runs out
- Shaping and cleaning; merges and appends; pivot/unpivot; parameters and functions
- Incremental refresh; query folding and why it decides your refresh time; dataflows for reusable prep
Estimated Time: 8 Hours
You ship:- A star schema rebuilt from a flat-table mess — facts, dimensions, a proper date table, deliberate cardinality — the model every later module builds on
- Star schema versus flat tables and why it isn't optional; fact and dimension design
- Relationships and cardinality; filter direction and ambiguity; role-playing dimensions
- A proper date table; many-to-many handling; composite models
Estimated Time: 7 Hours
You ship:- A measure library for the sales model — readable, variable-structured, and correct under every filter combination you can throw at it
- Calculated columns versus measures and when each is wrong; row context versus filter context
- CALCULATE as the centre of the language; FILTER, ALL, VALUES, RELATED
- Iterators like SUMX and AVERAGEX; variables for readable measures
Estimated Time: 7 Hours
You ship:- Time intelligence on a custom fiscal calendar — YoY, rolling averages, top-N with dynamic segmentation — plus a debugged fix for a measure that returned the wrong number
- Time intelligence: year-to-date, prior period, rolling averages, custom fiscal calendars
- Ranking and top-N; dynamic segmentation; what-if parameters; calculation groups
- Debugging a measure that returns the wrong number
Estimated Time: 7 Hours
You ship:- An executive one-page report designed for a specific decision — drillthrough to detail, accessible colour, mobile layout — then defended in a design critique
- Choosing visuals for the question being asked; layout and hierarchy
- Bookmarks, drillthrough, tooltips, field parameters, conditional formatting
- Accessibility and colour; mobile layouts; designing for a decision rather than for density
Estimated Time: 7 Hours
You ship:- A performance rescue: a slow, bloated model diagnosed and cut down, with before/after evidence from Performance Analyzer and DAX Studio
- Import vs DirectQuery vs Live vs Direct Lake; VertiPaq compression and cardinality
- Reducing model size; aggregations; Performance Analyzer; DAX Studio and Tabular Editor
- Capacity metrics; diagnosing why a report takes 40 seconds to load
Estimated Time: 7 Hours
You ship:- A secured deployment: row-level security by region, an app with audiences, and a dev/test/prod deployment pipeline with scheduled refresh through a gateway
- Workspace roles and strategy; apps and audiences; scheduled and incremental refresh; gateways
- Row-level and object-level security; sensitivity labels; certified and promoted semantic models
- Deployment pipelines across dev/test/prod; version control; usage monitoring
Estimated Time: 5 Hours
You ship:- A Direct Lake report over Fabric lakehouse data compared against an Import model — and your capstone: one end-to-end governed report from raw source to a stakeholder walkthrough where you defend the numbers
- OneLake and lakehouses; Direct Lake semantic models; connecting Power BI to Fabric workloads
- Copilot for report and measure assistance — and where it misleads you; semantic model descriptions that make AI features work
- Capstone build and stakeholder defense
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