Business Intelligence Analyst Course

Lesson 1 • The BI Analyst Role

Maps the responsibilities, skills, and stakeholder relationships of a BI analyst. Clarifies career expectations and daily workflows.

Lesson 2 • What Business Intelligence Is

Defines BI, its purpose, and how it differs from raw data reporting. Establishes the conceptual baseline for every subsequent chapter.

Lesson 3 • The BI Project Lifecycle

Covers requirements gathering, development, testing, and deployment phases. Provides a repeatable framework students apply throughout the course.

Lesson 1 • Data Governance Essentials

Introduces ownership, stewardship, metadata, and data dictionaries. Connects governance practices to reliable, trustworthy BI outputs.

Lesson 2 • Data Quality Assessment

Identifies completeness, accuracy, consistency, and timeliness as quality dimensions. Students learn to profile data and document quality issues.

Lesson 3 • Relational Data Concepts

Explains tables, keys, relationships, and normalisation. Provides the structural knowledge needed to query and model data correctly.

Lesson 1 • Window Functions and Analytics

Covers RANK, ROW_NUMBER, LAG, LEAD, and running totals using OVER. Enables time-series and ranking analyses directly in SQL.

Lesson 2 • Core SQL Query Syntax

Teaches SELECT, FROM, WHERE, ORDER BY, and LIMIT clauses. Forms the foundation for all subsequent SQL topics in this chapter.

Lesson 3 • Joins and Set Operations

Explains INNER, LEFT, RIGHT, and FULL joins plus UNION and INTERSECT. Allows analysts to combine data from multiple tables accurately.

Lesson 4 • Aggregations and Grouping

Covers GROUP BY, HAVING, and aggregate functions for summarising data. Enables analysts to produce business metrics from raw tables.

Lesson 1 • Dimensional Modeling Concepts

Introduces facts, dimensions, grain, and the Kimball methodology. Establishes the vocabulary and philosophy behind analytical data models.

Lesson 2 • Star and Snowflake Schemas

Compares star and snowflake schema designs with practical trade-offs. Students choose the right schema for a given analytical use case.

Lesson 3 • Fact Table Design Patterns

Explores transaction, periodic snapshot, and accumulating snapshot fact tables. Matches fact table type to the business process being measured.

Lesson 4 • Data Model Documentation

Teaches bus matrix creation, data lineage diagrams, and model versioning. Produces artefacts that support team collaboration and governance.

Lesson 1 • Data Extraction Techniques

Covers full, incremental, and change-data-capture extraction methods. Minimises pipeline load while ensuring data completeness.

Lesson 2 • Data Transformation Logic

Applies cleaning, deduplication, type casting, and business rule encoding. Produces consistent, analysis-ready datasets from raw sources.

Lesson 3 • Loading Strategies and Patterns

Explains truncate-and-reload, upsert, and append loading patterns. Matches loading strategy to data freshness and warehouse constraints.

Lesson 4 • ETL vs. ELT Architecture

Contrasts ETL and ELT patterns, use cases, and tool categories. Guides architecture decisions based on data volume and platform capabilities.

Lesson 1 • Metrics Governance and Change Management

Establishes review cycles, deprecation policies, and change communication for metrics. Keeps the metrics framework accurate as the business evolves.

Lesson 2 • Common Business Metrics by Domain

Covers revenue, customer, operational, and marketing metrics with formulas. Gives analysts a ready reference for cross-functional BI work.

Lesson 3 • Translating Strategy into Metrics

Connects business objectives to measurable KPIs using goal-question-metric frameworks. Ensures metrics reflect actual strategic priorities.

Lesson 4 • Metric Validation and Testing

Applies unit testing, cross-system reconciliation, and trend sanity checks to metrics. Builds confidence in numbers before stakeholder delivery.

Lesson 1 • Interactivity and Filters

Designs filters, drill-downs, parameters, and tooltips for user-driven exploration. Balances flexibility with dashboard performance.

Lesson 2 • Dashboard Performance Optimisation

Addresses extract caching, query optimisation, and data source best practices. Delivers fast-loading dashboards at scale.

Lesson 3 • Dashboard Layout and UX

Applies grid layouts, visual hierarchy, and whitespace to dashboard design. Ensures users find key metrics quickly without confusion.

Lesson 4 • Choosing the Right Chart Type

Maps analytical questions to bar, line, scatter, map, and other chart types. Prevents common chart selection mistakes that obscure insights.

Lesson 1 • Exploratory Data Analysis

Uses descriptive statistics, distributions, and correlation analysis to surface patterns. Builds the analytical foundation before drawing conclusions.

Lesson 2 • Trend Analysis and Forecasting

Covers moving averages, seasonality decomposition, and basic forecasting models. Enables forward-looking analysis for planning and budgeting.

Lesson 3 • A/B Testing and Experimentation

Designs and analyses controlled experiments using statistical significance testing. Provides a rigorous method for evaluating business changes.

Lesson 4 • Cohort and Segmentation Analysis

Applies cohort tracking and customer segmentation to behavioural data. Reveals performance differences across groups over time.