Power BI: Connectivity, AI Integration, and Reporting Course

Lesson 1 • Navigating the Desktop Interface

Introduces the Report, Data, and Model views alongside the ribbon and panes. Students gain confidence moving between views to complete common tasks.

Lesson 2 • Connecting to the Power BI Service

Explains workspaces, datasets, and the publish workflow from Desktop to Service. Connects local development to cloud-based sharing and collaboration.

Lesson 3 • Installing and Configuring Power BI Desktop

Covers system requirements, installation steps, and initial settings. Ensures a correctly configured environment before any data work begins.

Lesson 1 • Import vs. DirectQuery vs. Live Connection

Compares the three connectivity modes across performance, freshness, and capability trade-offs. Provides decision criteria for selecting the right mode per use case.

Lesson 2 • Connecting to File-Based Sources

Demonstrates connections to flat files, Excel workbooks, and folders. Builds skills for the most common entry-level data sources encountered in practice.

Lesson 3 • Managing and Refreshing Datasets

Covers scheduled refresh, incremental refresh policies, and dataset monitoring. Students maintain data freshness without manual intervention.

Lesson 4 • Connecting to Databases and Cloud Services

Covers relational databases, cloud data warehouses, and SaaS connectors. Students configure authenticated connections to enterprise-grade sources.

Lesson 1 • Essential Data Cleaning Techniques

Covers removing duplicates, handling nulls, correcting data types, and trimming text. These operations form the baseline quality layer for every dataset.

Lesson 2 • Query Performance and Best Practices

Addresses query folding, step ordering, and diagnostic tools to optimise load time. Connects transformation efficiency to downstream model and report performance.

Lesson 3 • Introduction to the M Language

Explains M syntax, let expressions, and custom function creation. Enables students to write transformations beyond what the GUI exposes.

Lesson 4 • Power Query Editor Interface

Introduces the query editor layout, applied steps pane, and formula bar. Establishes the iterative, step-based transformation workflow central to Power Query.

Lesson 1 • Dimensional Modelling Principles

Introduces star and snowflake schemas, fact tables, and dimension tables. Provides the conceptual framework that guides all subsequent modelling decisions.

Lesson 2 • Model Optimisation and Best Practices

Addresses column data types, cardinality reduction, and hiding unused fields. An optimised model reduces file size and improves report rendering speed.

Lesson 3 • Calculated Columns and Calculated Tables

Explains when to use calculated columns versus measures and how to build calculated tables. Students extend the model without modifying source data.

Lesson 1 • DAX Syntax and Core Concepts

Introduces DAX operators, data types, and the distinction between row and filter context. Establishes the mental model required for all subsequent DAX work.

Lesson 2 • Advanced DAX Patterns

Covers RANKX, TOPN, iterators (SUMX, AVERAGEX), and virtual relationships. Students solve complex analytical requirements with composable DAX patterns.

Lesson 3 • Time Intelligence Functions

Applies DATEADD, SAMEPERIODLASTYEAR, TOTALYTD, and related functions to date-based analysis. Requires a properly marked date table from the modeling chapter.

Lesson 4 • Aggregation and Logical Functions

Covers SUM, AVERAGE, COUNT variants, and IF/SWITCH logic. These building-block functions appear in nearly every real-world measure.

Lesson 1 • Interactivity: Slicers, Filters, and Drill-Through

Configures slicers, cross-filtering, drill-through pages, and bookmarks. Interactivity transforms static charts into self-service exploration tools.

Lesson 2 • Formatting and Theming Reports

Covers conditional formatting, custom themes, and consistent visual styling. A well-formatted report builds trust and reduces cognitive load for end users.

Lesson 3 • Report Layout and Accessibility

Applies grid alignment, tab order, alt text, and color-contrast standards to reports. Accessible reports reach a wider audience and meet organizational standards.

Lesson 4 • Choosing the Right Visual Type

Maps analytical questions to appropriate chart types using data visualization principles. Prevents common mismatches between data structure and visual encoding.

Lesson 1 • Native AI Visuals in Power BI

Covers Q&A, key influencers, decomposition tree, and smart narratives as built-in AI tools. Students activate AI-driven exploration without writing any code.

Lesson 2 • Copilot and Generative AI Features

Explores Power BI Copilot for report generation, DAX suggestions, and narrative summaries. Students evaluate AI-generated content for accuracy before publishing.

Lesson 3 • Python and R Scripts in Power BI

Runs Python and R scripts for data transformation and custom visuals. Extends Power BI with the full statistical and ML libraries of both ecosystems.

Lesson 4 • AI Insights in Power Query

Applies text analytics, sentiment scoring, and image tagging via AI Insights in Power Query. Enriches datasets with ML-derived columns before modeling.

Lesson 1 • Row-Level Security Implementation

Builds static and dynamic RLS roles using DAX filter expressions. Students restrict data visibility to authorized users without creating separate reports.

Lesson 2 • Deployment Pipelines and ALM

Uses deployment pipelines to move content through development, test, and production stages. Applies application lifecycle management discipline to Power BI assets.

Lesson 3 • Workspace Roles and Permissions

Defines Admin, Member, Contributor, and Viewer roles and their permission boundaries. Correct role assignment prevents unauthorized data access and accidental edits.

Lesson 4 • Sensitivity Labels and Data Protection

Applies sensitivity labels to datasets, reports, and exports to enforce data classification. Links Power BI governance to the organization's broader information-protection policy.