Data Science and Analytics for Business Course

Lesson 1 • Ethics, Privacy, and Data Governance Basics

Addresses responsible data use, privacy principles, and governance roles within organisations. Anchors ethical thinking as a non-negotiable foundation for all subsequent work.

Lesson 2 • The Data-Driven Business Landscape

Examines how organisations use data to gain competitive advantage and improve decisions. Establishes the business case for analytics as the chapter's motivating context.

Lesson 3 • Data Types, Structures, and Sources

Covers structured, semi-structured, and unstructured data along with internal and external data sources. Prepares students to identify appropriate data for any business question.

Lesson 4 • Types of Analytics and Their Applications

Distinguishes descriptive, diagnostic, predictive, and prescriptive analytics with real business examples. Provides a taxonomy students apply throughout the course.

Lesson 1 • Exploratory Data Analysis Techniques

Uses summary statistics and initial visualisations to understand distributions and relationships. Guides hypothesis formation before formal modelling begins.

Lesson 2 • Data Transformation and Feature Engineering

Applies normalisation, encoding, aggregation, and derived variable creation to raw data. Transforms cleaned data into features suitable for statistical and machine learning models.

Lesson 3 • Data Pipeline Design and Documentation

Introduces reproducible pipeline architecture, version control for data, and documentation standards. Ensures analytical work is auditable, repeatable, and team-ready.

Lesson 4 • Data Collection and Ingestion Methods

Covers manual entry, file imports, APIs, and database queries as data ingestion techniques. Connects data sourcing to the quality of all subsequent analysis.

Lesson 1 • Sampling, Distributions, and the Central Limit Theorem

Explains sampling methods, key probability distributions, and why the central limit theorem enables inference. Provides the theoretical basis for hypothesis testing covered next.

Lesson 2 • Probability and Uncertainty in Business

Covers probability rules, conditional probability, and Bayes' theorem in business decision contexts. Establishes the probabilistic thinking required for all inferential and predictive work.

Lesson 3 • Hypothesis Testing and Statistical Significance

Teaches null and alternative hypotheses, p-values, and Type I/II errors using business examples. Enables students to validate claims and evaluate experiments rigorously.

Lesson 4 • Correlation, Regression, and Causation

Distinguishes correlation from causation and introduces simple and multiple linear regression. Bridges descriptive statistics to the predictive modelling covered in later chapters.

Lesson 1 • Relational Database Concepts

Introduces tables, keys, relationships, and schema design as the structural foundation of SQL work. Ensures students understand the data model before writing queries.

Lesson 2 • Advanced SQL for Analytics

Introduces window functions, ranking, and date/time operations for sophisticated analytical queries. Prepares students for the complex reporting and cohort analysis used in business intelligence.

Lesson 3 • Core SQL Querying

Covers SELECT, WHERE, ORDER BY, and LIMIT clauses to retrieve and filter business data precisely. Forms the baseline query skill set expanded in every subsequent SQL section.

Lesson 4 • Aggregation, Grouping, and Subqueries

Applies GROUP BY, HAVING, and aggregate functions alongside subqueries to summarise business metrics. Enables students to compute KPIs directly from raw transactional data.

Lesson 1 • Data Storytelling and Narrative Structure

Structures analytical findings into a compelling narrative arc with context, conflict, and resolution. Transforms raw insights into persuasive business recommendations.

Lesson 2 • Dashboard Design for Business Audiences

Applies layout hierarchy, KPI selection, and interactivity principles to build executive dashboards. Connects visualisation skills to real stakeholder communication needs.

Lesson 3 • Presenting Insights to Non-Technical Stakeholders

Covers simplification strategies, handling questions, and adapting technical content for executive audiences. Ensures analytical value is not lost in translation to decision-makers.

Lesson 4 • Core Chart Types and When to Use Them

Teaches bar, line, scatter, pie, and heat map charts with guidance on appropriate business contexts. Builds a practical visual vocabulary students use throughout the course.

Lesson 1 • Model Evaluation, Selection, and Deployment

Teaches cross-validation, hyperparameter tuning, and model comparison to select production-ready models. Covers deployment considerations including monitoring and model drift detection.

Lesson 2 • Unsupervised Learning and Customer Segmentation

Applies k-means clustering, hierarchical clustering, and dimensionality reduction to discover hidden patterns. Enables market segmentation, anomaly detection, and product recommendation strategies.

Lesson 3 • Machine Learning Concepts and Business Fit

Defines supervised, unsupervised, and reinforcement learning and maps each to common business problems. Establishes when ML adds value versus when simpler analytics suffice.

Lesson 4 • Supervised Learning: Classification Models

Covers logistic regression, decision trees, and random forests for predicting categorical business outcomes. Teaches model training, validation, and interpretation in business terms.

Lesson 1 • Self-Service Analytics and Governed Access

Balances user autonomy with data governance through role-based access, certified datasets, and usage monitoring. Enables business users to explore data safely without IT bottlenecks.

Lesson 2 • Semantic Layer and Data Modelling

Covers calculated measures, hierarchies, and relationships within a BI semantic layer for consistent metrics. Ensures all reports draw from a single, governed definition of business KPIs.

Lesson 3 • BI Architecture and Data Warehousing

Explains data warehouse design, star and snowflake schemas, and ETL versus ELT pipelines. Provides the architectural context for all BI tool configuration that follows.

Lesson 4 • BI Performance Optimisation and Scalability

Addresses query performance, aggregation tables, incremental refresh, and capacity planning for large datasets. Prepares students to maintain BI systems as data volumes and user counts grow.

Lesson 1 • Decision Intelligence and Augmented Decision-Making

Integrates ML outputs, optimisation models, and human judgment into structured decision workflows. Advances students from reporting insights to actively improving organisational decisions.

Lesson 2 • Analytics Roadmap and Prioritisation

Teaches use-case scoring, resource allocation, and phased roadmap construction aligned to business strategy. Enables students to prioritise analytics investments for maximum organisational return.

Lesson 3 • Measuring Analytics ROI and Business Impact

Applies value measurement frameworks to quantify cost savings, revenue lift, and risk reduction from analytics. Equips students to justify analytics investments to executive and financial stakeholders.

Lesson 4 • Responsible AI and Ethical Governance at Scale

Extends foundational ethics to enterprise AI governance, model explainability, and regulatory compliance. Ensures advanced analytics deployments remain trustworthy, fair, and auditable.