Advanced Data Analytics Course

Lesson 1 • The Analytics Landscape

Defines descriptive, diagnostic, predictive, and prescriptive analytics. Positions each type within real business decision-making contexts.

Lesson 2 • Data Types and Structures

Covers structured, semi-structured, and unstructured data formats. Connects data structure awareness to storage and processing decisions.

Lesson 3 • The Analytics Project Lifecycle

Maps the end-to-end workflow from problem framing to insight delivery. Anchors all subsequent chapters within a repeatable process.

Lesson 4 • Ethics and Responsible Analytics

Introduces bias, fairness, privacy, and transparency obligations in analytics work. Establishes an ethical lens applied throughout the course.

Lesson 1 • Data Quality Frameworks

Introduces dimensions of data quality and governance-aligned quality scoring. Enables analysts to communicate data health to stakeholders.

Lesson 2 • Building Reproducible Data Pipelines

Teaches pipeline design principles including idempotency, logging, and error handling. Reproducibility ensures consistent analytical outputs.

Lesson 3 • Profiling and Auditing Datasets

Applies statistical profiling to detect anomalies, nulls, and distribution issues. Profiling outputs directly inform cleaning priorities.

Lesson 4 • Data Cleaning Techniques

Covers imputation, deduplication, standardisation, and format normalisation. Each technique is tied to downstream modelling impact.

Lesson 1 • Chart Selection and Design Principles

Maps data types and analytical questions to optimal chart types. Applies visual design principles to maximise clarity and accuracy.

Lesson 2 • Univariate and Bivariate Analysis

Applies summary statistics and distribution analysis to single and paired variables. Builds intuition for data shape before modelling.

Lesson 3 • Multivariate Exploration Techniques

Uses dimensionality reduction and pair plots to reveal complex variable relationships. Prepares analysts for feature selection in modelling.

Lesson 4 • Interactive and Dashboard Visualisation

Builds interactive dashboards with filters, drill-downs, and dynamic views. Connects dashboard design to specific audience decision needs.

Lesson 1 • Confidence Intervals and Effect Sizes

Teaches interval estimation and standardised effect size measures. Shifts focus from binary significance to magnitude of practical impact.

Lesson 2 • Probability Foundations for Analysts

Reviews probability rules, distributions, and the central limit theorem. Provides the statistical backbone for all inferential techniques.

Lesson 3 • Parametric and Non-Parametric Tests

Covers t-tests, ANOVA, chi-square, and rank-based alternatives. Guides test selection based on data type and distributional assumptions.

Lesson 4 • Multiple Testing and Power Analysis

Addresses false discovery rate control and sample size determination. Ensures experiments are adequately powered and statistically sound.

Lesson 1 • Model Evaluation and Validation

Applies cross-validation, confusion matrices, and calibration curves to assess models. Prevents overfitting and ensures reliable out-of-sample performance.

Lesson 2 • Regression Modelling Techniques

Covers linear, polynomial, and regularised regression for continuous targets. Connects coefficient interpretation to business-relevant predictions.

Lesson 3 • Hyperparameter Tuning and Optimisation

Uses grid search, random search, and Bayesian optimisation to maximise model performance. Balances computational cost against accuracy gains.

Lesson 4 • Feature Engineering and Selection

Transforms raw variables into informative features and removes redundant ones. Directly improves model accuracy and generalizability.

Lesson 1 • Association Rule Mining

Extracts frequent itemsets and association rules using support, confidence, and lift. Applied to market basket and recommendation contexts.

Lesson 2 • Anomaly and Outlier Detection

Detects rare events using statistical, distance-based, and isolation methods. Connects anomaly detection to fraud, quality control, and monitoring.

Lesson 3 • Clustering Algorithms and Applications

Covers k-means, hierarchical, and density-based clustering methods. Applies each to customer segmentation and behavioral grouping tasks.

Lesson 4 • Cluster Validation and Interpretation

Uses silhouette scores, Davies-Bouldin index, and business validation to assess clusters. Ensures segments are statistically sound and actionable.

Lesson 1 • Causal Graphs and Structural Models

Uses directed acyclic graphs to encode causal assumptions and identify confounders. Prevents spurious conclusions from observational analyses.

Lesson 2 • Experimental Design and A/B Testing

Designs controlled experiments with proper randomization, controls, and stopping rules. Produces reliable causal estimates from business experiments.

Lesson 3 • Uplift Modeling and Treatment Effects

Estimates heterogeneous treatment effects to target interventions at responsive individuals. Maximizes ROI of marketing and operational campaigns.

Lesson 4 • Quasi-Experimental Methods

Applies difference-in-differences, regression discontinuity, and synthetic controls when randomization is infeasible. Extracts causal signals from observational data.

Lesson 1 • Model Deployment and MLOps Basics

Covers model packaging, API serving, and CI/CD pipelines for analytics products. Bridges the gap between model development and production use.

Lesson 2 • Model Monitoring and Drift Detection

Tracks data drift, concept drift, and performance degradation in deployed models. Ensures models remain accurate and reliable over time.

Lesson 3 • Building an Analytics Roadmap

Prioritizes analytics initiatives by business impact, feasibility, and data readiness. Produces a sequenced roadmap aligned to organizational strategy.

Lesson 4 • Measuring Analytics Business Impact

Quantifies ROI of analytics initiatives using attribution, counterfactuals, and value tracking. Demonstrates analytics value to executive stakeholders.