Business Analytics and intelligence Course

Lesson 1 • What Business Analytics Means

Defines analytics, business intelligence, and data science distinctions. Establishes shared vocabulary used throughout the course.

Lesson 2 • Aligning Analytics with Business Goals

Translates business questions into analytical problems. Grounds technical work in measurable organisational outcomes.

Lesson 3 • The Analytics Value Chain

Maps the flow from raw data to business decisions. Shows how each stage adds measurable value to the organisation.

Lesson 4 • Roles and Responsibilities in Analytics

Profiles data analysts, engineers, scientists, and BI developers. Clarifies collaboration patterns learners will encounter in practice.

Lesson 1 • Database Fundamentals

Introduces relational and non-relational database concepts. Provides the storage knowledge needed for later querying and modelling.

Lesson 2 • Data Warehouses and Data Lakes

Contrasts warehouse, lake, and lakehouse architectures. Prepares learners to choose the right storage pattern for analytics workloads.

Lesson 3 • Data Quality and Governance

Defines dimensions of data quality and governance frameworks. Ensures learners can audit and improve data reliability before analysis.

Lesson 4 • Data Privacy and Compliance Principles

Covers privacy-by-design and regulatory compliance concepts. Equips learners to handle sensitive data responsibly across industries.

Lesson 1 • SQL Basics and SELECT Statements

Introduces SQL syntax, data retrieval, and filtering logic. Forms the query foundation for all subsequent analytical work.

Lesson 2 • Subqueries and CTEs

Introduces subqueries and common table expressions for modular query design. Builds readable, maintainable SQL for complex analytical tasks.

Lesson 3 • Window Functions and Advanced SQL

Covers ranking, running totals, and partitioned calculations using window functions. Enables sophisticated time-series and comparative analysis.

Lesson 4 • Joining Multiple Tables

Explains INNER, LEFT, RIGHT, and FULL joins for combining datasets. Unlocks multi-table analysis essential for business reporting.

Lesson 1 • Multivariate Exploration Techniques

Extends analysis to interactions among three or more variables. Reveals complex patterns that bivariate analysis misses.

Lesson 2 • Univariate and Bivariate Analysis

Examines single-variable distributions and two-variable relationships. Builds intuition for variable behaviour and potential predictors.

Lesson 3 • Descriptive Statistics Essentials

Covers measures of central tendency, spread, and shape. Provides the statistical language needed to summarise any dataset.

Lesson 4 • Communicating EDA Findings

Structures EDA outputs into a coherent analytical narrative. Connects exploratory insights to actionable business hypotheses.

Lesson 1 • Chart Types and Their Use Cases

Maps specific chart types to analytical purposes and data structures. Enables confident chart selection for any business scenario.

Lesson 2 • Building Interactive Dashboards

Covers layout, filters, and interactivity for BI dashboards. Teaches learners to design dashboards that answer multiple user questions.

Lesson 3 • Principles of Effective Visualisation

Establishes perceptual and design principles for honest, clear charts. Prevents common visualisation errors that mislead stakeholders.

Lesson 4 • Dashboard Performance and Maintenance

Addresses query optimisation, refresh schedules, and version control. Ensures dashboards remain accurate and performant over time.

Lesson 1 • Hypothesis Testing Framework

Establishes null and alternative hypotheses, p-values, and decision rules. Gives learners a rigorous process for testing business claims.

Lesson 2 • Confidence Intervals and Estimation

Teaches point and interval estimation for population parameters. Enables learners to quantify uncertainty in analytical conclusions.

Lesson 3 • Probability and Distributions

Introduces probability concepts and key statistical distributions. Provides the theoretical base for all inferential methods that follow.

Lesson 4 • A/B Testing in Business Contexts

Applies hypothesis testing to controlled business experiments. Prepares learners to design, run, and interpret A/B tests correctly.

Lesson 1 • Classification and Tree-Based Models

Introduces decision trees, random forests, and gradient boosting. Expands the modelling toolkit for complex classification problems.

Lesson 2 • Clustering and Segmentation

Applies k-means and hierarchical clustering for customer and market segmentation. Connects unsupervised learning to strategic business use cases.

Lesson 3 • Regression Models for Forecasting

Covers linear and logistic regression for continuous and binary outcomes. Enables learners to build interpretable predictive models.

Lesson 4 • Machine Learning Fundamentals

Defines supervised, unsupervised, and reinforcement learning paradigms. Frames ML within the analytics workflow established in earlier chapters.

Lesson 1 • Measuring Analytics ROI

Defines frameworks for quantifying the financial and operational value of analytics. Enables learners to justify investments and demonstrate impact.

Lesson 2 • Building a Data-Driven Culture

Examines organisational behaviours and leadership practices that enable analytics adoption. Prepares learners to champion data-driven change.

Lesson 3 • Ethics and Responsible Analytics

Addresses algorithmic bias, fairness, transparency, and accountability in analytics. Ensures learners apply analytics responsibly across all contexts.

Lesson 4 • Analytics Strategy and Roadmap

Guides the creation of a multi-year analytics strategy aligned to business priorities. Translates vision into executable initiatives with clear milestones.