Data Analytics and Business intelligence Course

Lesson 1 • Data Types and Structures

Covers structured, semi-structured, and unstructured data with real-world examples. Establishes vocabulary used throughout the course.

Lesson 2 • The Business Intelligence Ecosystem

Maps the BI stack from data sources to dashboards. Students understand how each layer contributes to insight delivery.

Lesson 3 • Analytics Categories and Use Cases

Defines descriptive, diagnostic, predictive, and prescriptive analytics. Connects each category to concrete business outcomes.

Lesson 1 • Data Quality and Governance

Defines quality dimensions—accuracy, completeness, consistency, timeliness. Introduces governance policies that ensure trustworthy analytics.

Lesson 2 • Data Sources and Ingestion Methods

Surveys primary, secondary, internal, and external data sources. Introduces batch and streaming ingestion patterns.

Lesson 3 • Data Privacy and Compliance Principles

Covers privacy-by-design, consent, anonymisation, and regulatory compliance concepts. Prepares students to handle sensitive data responsibly.

Lesson 4 • Relational Database Fundamentals

Teaches table design, keys, and relationships in relational databases. Provides the structural knowledge required for SQL querying in the next chapter.

Lesson 1 • Core SQL Query Syntax

Introduces SELECT, FROM, WHERE, and ORDER BY clauses. Builds the query-writing foundation for all subsequent SQL topics.

Lesson 2 • Joins and Relationships

Covers INNER, LEFT, RIGHT, and FULL joins to combine tables. Students retrieve integrated datasets from normalised schemas.

Lesson 3 • Window Functions and Advanced SQL

Applies RANK, ROW_NUMBER, LAG, and LEAD for time-series and ranking analysis. Unlocks analytical patterns unavailable with basic aggregation.

Lesson 4 • Aggregation and Grouping

Teaches GROUP BY, HAVING, and aggregate functions for summarising data. Enables metric calculation essential to BI reporting.

Lesson 1 • Merging and Reshaping Datasets

Combines datasets through joins, unions, and pivots to create analytical tables. Mirrors SQL joins in a programmatic data-preparation context.

Lesson 2 • Identifying and Handling Missing Data

Classifies missing data mechanisms and evaluates imputation vs. removal strategies. Prevents bias introduced by improper handling of nulls.

Lesson 3 • Data Transformation Techniques

Applies normalisation, standardisation, encoding, and binning to reshape variables. Prepares features for visualisation and modelling tasks.

Lesson 4 • Detecting and Treating Outliers

Uses statistical and visual methods to identify outliers and decides on treatment. Protects downstream analysis from distortion by extreme values.

Lesson 1 • EDA Reporting and Storytelling

Structures EDA findings into a coherent narrative for stakeholders. Bridges technical exploration and business communication.

Lesson 2 • Multivariate Exploration Techniques

Extends analysis to three or more variables using heatmaps, pair plots, and grouping. Surfaces complex interactions hidden in bivariate views.

Lesson 3 • Hypothesis Generation and Testing Basics

Introduces null and alternative hypotheses, p-values, and significance levels. Connects EDA findings to formal statistical validation.

Lesson 4 • Descriptive Statistics Essentials

Covers measures of central tendency, spread, and shape for numeric variables. Provides the statistical language used in all subsequent analytical work.

Lesson 1 • Principles of Effective Visualisation

Applies Gestalt principles, pre-attentive attributes, and data-ink ratio to chart design. Establishes standards that prevent misleading visuals.

Lesson 2 • Colour, Typography, and Layout

Applies colour theory, accessible palettes, and typographic hierarchy to dashboards. Ensures readability across audiences and devices.

Lesson 3 • Dashboard Performance and Governance

Optimises query performance, establishes refresh schedules, and enforces access controls. Ensures dashboards remain accurate and secure in production.

Lesson 4 • Interactive Dashboard Construction

Builds filters, drill-downs, and dynamic parameters in BI tools. Enables end users to self-serve analytical questions.

Lesson 1 • Communicating Predictive Insights

Translates model outputs into business recommendations using plain language and visuals. Addresses stakeholder scepticism about algorithmic predictions.

Lesson 2 • Machine Learning Concepts for Analysts

Defines supervised, unsupervised, and reinforcement learning without deep maths. Positions ML as an extension of the analytics toolkit.

Lesson 3 • Regression Models for Forecasting

Builds linear and multiple regression models to predict continuous outcomes. Interprets coefficients in business terms.

Lesson 4 • Classification Models

Applies logistic regression, decision trees, and random forests to binary and multiclass problems. Selects models based on interpretability and accuracy needs.

Lesson 1 • Data Culture and Change Management

Identifies barriers to data adoption and applies change management frameworks. Builds organisational readiness for analytics-driven decisions.

Lesson 2 • Evaluating and Scaling Analytics Programmes

Applies maturity models to assess current capabilities and plan scaling investments. Guides organisations from ad hoc reporting to predictive intelligence.

Lesson 3 • Analytics Team Structures and Roles

Compares centralised, decentralised, and federated analytics operating models. Helps students design teams that balance agility and governance.

Lesson 4 • Building a Data Strategy

Defines vision, priorities, and success metrics for an enterprise analytics programme. Connects data investments to measurable business value.