Business Intelligence Dashboard Development Course

Lesson 1 • What Business Intelligence Means

Defines BI, its history, and its distinction from raw reporting. Grounds the chapter by framing dashboards as decision-support tools.

Lesson 2 • The BI Workflow End to End

Maps the full pipeline from raw data ingestion to visual output. Helps students understand where dashboard development fits in the broader process.

Lesson 3 • Stakeholders and Use Cases

Identifies executive, operational, and analytical dashboard consumers and their needs. Connects audience analysis to design decisions made later in the course.

Lesson 1 • Data Quality Assessment

Teaches profiling techniques to detect missing values, duplicates, and outliers. Establishes data quality as a prerequisite for trustworthy dashboard metrics.

Lesson 2 • Data Cleaning Techniques

Covers standardization, imputation, and deduplication workflows. Directly prepares datasets for the modeling and aggregation steps that follow.

Lesson 3 • Calculated Fields and Metrics

Demonstrates building derived columns, ratios, and aggregated measures. These calculated fields become the core KPIs displayed on dashboards.

Lesson 4 • Data Refresh and Pipeline Basics

Explains scheduled refreshes, incremental loads, and basic ETL concepts. Ensures students understand how dashboards stay current after initial build.

Lesson 1 • Common Visualization Mistakes

Identifies truncated axes, dual-axis misuse, and chart junk that distort data. Equips students to critique and correct flawed visualizations.

Lesson 2 • Choosing the Right Chart Type

Maps data relationships—comparison, distribution, composition, trend—to appropriate chart forms. Prevents misrepresentation caused by poor chart selection.

Lesson 3 • Human Perception and Visual Encoding

Explains pre-attentive attributes and how the brain processes visual information. Grounds every subsequent design decision in cognitive science.

Lesson 4 • Typography and Layout Fundamentals

Addresses font hierarchy, whitespace, and grid-based layout for dashboards. Connects visual structure to readability and user navigation.

Lesson 1 • Dashboard Types and Purposes

Distinguishes strategic, operational, and analytical dashboard categories by audience and update frequency. Sets design intent before any layout work begins.

Lesson 2 • Information Architecture and Hierarchy

Teaches content prioritization, visual hierarchy, and logical grouping of metrics. Ensures users find critical information without scanning the entire dashboard.

Lesson 3 • Interactivity and User Controls

Covers filters, slicers, drill-downs, and tooltips that let users explore data. Connects interactive design to the self-service BI use cases introduced earlier.

Lesson 4 • Wireframing and Prototyping

Guides students through low-fidelity sketches to interactive prototypes before building. Reduces rework by validating layout decisions with stakeholders early.

Lesson 1 • Connecting to Data Sources

Demonstrates live and imported connections to databases, files, and cloud services. Establishes the data layer that all subsequent visualizations depend on.

Lesson 2 • Formatting and Theming

Applies consistent color themes, fonts, and branding to the dashboard canvas. Reinforces the typography and color principles from Chapter 3 in a live environment.

Lesson 3 • Building Core Visualizations

Walks through creating bar, line, map, and KPI card visuals within the platform. Translates the chart-selection principles from Chapter 3 into platform-specific actions.

Lesson 4 • Applying Filters and Interactions

Configures cross-filtering, slicers, and report-level filters within the platform. Implements the interactivity design concepts covered in Chapter 4.

Lesson 1 • Performance Optimization for Calculations

Identifies slow formulas and applies best practices to reduce query execution time. Ensures complex dashboards remain responsive under real-world data volumes.

Lesson 2 • Conditional and Logical Calculations

Uses IF, SWITCH, and nested logic to build threshold alerts and segmentation. Translates business rules directly into calculated fields that drive visual cues.

Lesson 3 • Statistical Measures and Forecasting

Introduces variance, standard deviation, percentiles, and built-in forecast functions. Adds analytical depth to dashboards beyond simple sums and averages.

Lesson 4 • Ranking and Top-N Analysis

Creates dynamic ranking measures and top-N filters driven by user selections. Supports competitive and performance-ranking dashboards common in sales and operations.

Lesson 1 • Scalability for Large Datasets

Introduces partitioning, DirectQuery mode, and composite models for big data scenarios. Prepares students to build dashboards that remain performant at enterprise scale.

Lesson 2 • Data Model Optimization

Reduces model size through column removal, data type tuning, and aggregation tables. A lean model is the single highest-impact performance improvement available.

Lesson 3 • Monitoring and Maintaining Dashboards

Sets up usage metrics, refresh monitoring, and alerting for production dashboards. Ensures dashboards remain reliable and accurate after deployment.

Lesson 4 • Diagnosing Performance Bottlenecks

Uses built-in performance analyzers and query logs to locate slow visuals and queries. Establishes a diagnostic-first mindset before applying any optimization.

Lesson 1 • Compliance and Data Privacy Practices

Applies data masking, sensitivity labels, and retention policies to meet privacy obligations. Ensures dashboards handle personal and sensitive data responsibly.

Lesson 2 • Data Governance Fundamentals

Covers data ownership, lineage tracking, certified datasets, and glossary management. Connects governance practices to dashboard trustworthiness and auditability.

Lesson 3 • Row-Level and Object-Level Security

Configures dynamic row-level security and object-level restrictions by user role. Prevents unauthorized data exposure in shared dashboard environments.

Lesson 4 • Deployment Pipelines and Version Control

Uses deployment pipelines to promote dashboards from development to production safely. Reduces deployment risk and supports collaborative development workflows.