Big Data & Business Intelligence Course

Lesson 1 • Data Ecosystem Architecture

Surveys the components of a modern data ecosystem including sources, pipelines, storage, and consumption layers. Provides a mental model for the full-stack data environment.

Lesson 2 • Evolution of Analytics Maturity

Traces the progression from descriptive to prescriptive analytics and introduces maturity models. Helps learners benchmark their organisation's current capabilities.

Lesson 3 • Defining Big Data Concepts

Introduces the 5 Vs framework and distinguishes structured, semi-structured, and unstructured data. Anchors all subsequent technical topics in shared terminology.

Lesson 1 • Data Governance and Quality

Covers data quality dimensions, governance frameworks, and stewardship roles essential for trustworthy analytics. Ensures storage decisions align with compliance and accuracy requirements.

Lesson 2 • NoSQL and Distributed Databases

Compares document, key-value, columnar, and graph databases and their appropriate use cases. Prepares learners to handle unstructured and high-velocity data scenarios.

Lesson 3 • Relational Databases for Analytics

Reviews relational database principles, schema design, and SQL fundamentals relevant to BI workloads. Establishes the baseline before introducing distributed alternatives.

Lesson 4 • Data Warehousing Principles

Explains dimensional modelling, star and snowflake schemas, and ETL design for enterprise warehouses. Directly enables the reporting and dashboard work covered later.

Lesson 1 • Distributed Computing Fundamentals

Explains MapReduce, distributed file systems, and cluster resource management as the basis for big data processing. Provides the conceptual foundation for framework-specific tools.

Lesson 2 • Pipeline Orchestration and Monitoring

Covers workflow scheduling, dependency management, and observability practices for production pipelines. Ensures learners can maintain reliable data flows in enterprise environments.

Lesson 3 • Stream Processing Concepts

Introduces event-driven architectures, message brokers, and windowing strategies for real-time data. Extends batch skills to continuous data scenarios.

Lesson 4 • Batch Processing with Spark

Covers Spark's RDD, DataFrame, and Dataset APIs for large-scale batch transformations and aggregations. Builds core processing skills used throughout the course.

Lesson 1 • Data Transformation Techniques

Covers cleansing, standardisation, enrichment, and aggregation transformations that convert raw data into analytics-ready form. Directly supports warehouse and lake loading.

Lesson 2 • Integration Architecture Patterns

Surveys hub-and-spoke, data mesh, and event-driven integration architectures for enterprise environments. Equips learners to choose the right pattern for organisational scale.

Lesson 3 • Loading Patterns and Strategies

Explains full load, incremental load, upsert, and partition-based loading strategies for warehouses and lakes. Balances performance with data freshness requirements.

Lesson 4 • Data Quality in Pipelines

Integrates validation rules, profiling, and anomaly detection directly into ETL workflows. Prevents bad data from propagating to downstream BI systems.

Lesson 1 • Choosing the Right Chart Type

Maps analytical questions to appropriate chart families including comparison, distribution, relationship, and composition charts. Prevents common chart-type mismatches.

Lesson 2 • Designing Effective Visualisations

Applies data-ink ratio, decluttering, and annotation techniques to produce clear, honest charts. Directly improves the quality of dashboards built in the previous chapter.

Lesson 3 • Data Storytelling Frameworks

Introduces narrative structures, insight sequencing, and audience-tailored messaging for data presentations. Transforms static reports into persuasive analytical stories.

Lesson 4 • Principles of Visual Perception

Explains pre-attentive attributes, Gestalt principles, and cognitive load theory as applied to data displays. Grounds all visualisation decisions in human perception science.

Lesson 1 • Report Design and Layout

Applies design principles to structure reports for clarity, scannability, and executive consumption. Bridges data availability with effective communication.

Lesson 2 • Connecting BI Tools to Data Sources

Covers live connections, import mode, and semantic layer configuration for BI tool data access. Ensures reports reflect accurate and timely data.

Lesson 3 • Dashboard Development Best Practices

Guides learners through KPI selection, visual hierarchy, and interactivity design for executive dashboards. Produces decision-support tools aligned with business goals.

Lesson 4 • Publishing and Sharing BI Content

Covers deployment, access control, scheduling, and embedding strategies for distributing BI content. Completes the BI delivery lifecycle from development to consumption.

Lesson 1 • Unsupervised Learning and Segmentation

Applies clustering and dimensionality reduction to discover hidden patterns and customer segments in large datasets. Extends analytics beyond labelled data scenarios.

Lesson 2 • Supervised Learning for Business

Covers regression and classification algorithms applied to business prediction problems such as churn and demand forecasting. Connects ML techniques to tangible BI outcomes.

Lesson 3 • Time Series and Forecasting

Introduces decomposition, smoothing, and autoregressive models for business forecasting use cases. Directly supports demand planning and financial projection scenarios.

Lesson 4 • Integrating Models into BI Workflows

Covers model deployment patterns, scoring pipelines, and embedding predictions into dashboards and reports. Closes the loop between advanced analytics and BI consumption.

Lesson 1 • Privacy, Security, and Compliance

Addresses data privacy principles, access control frameworks, and regulatory compliance requirements for analytics platforms. Protects organisational and customer data assets.

Lesson 2 • Enterprise Data Strategy Design

Guides learners through defining data vision, roadmap, and capability priorities aligned with business objectives. Produces a strategic blueprint for data investment.

Lesson 3 • BI Governance Frameworks

Covers data ownership, stewardship councils, policy enforcement, and audit mechanisms for governed BI environments. Ensures analytics outputs are trustworthy and compliant.

Lesson 4 • Measuring BI Programme Value

Introduces ROI frameworks, adoption metrics, and business impact measurement for BI initiatives. Enables leaders to justify and optimise data investments.