Programme Curriculum

Analytics Foundations and Descriptive Analytics

• Building the Analytics Mindset

• • From Data to Decisions: Business Framing and Analytical Thinking
  • The Analytics Spectrum: Descriptive, Diagnostic, Predictive, Prescriptive, AI Augmented
  • KPIs that Matter: Defining and Aligning with Business Goals
• Data Handling and Preparation for Analysts

• • Cleaning Like a Pro: Missing Data, Duplicates, Integrity Checks
  • Structuring Data for Analysis: Wide vs. Long Formats, Joins, Normalisation
  • Trustworthy Analytics: Documentation and Reproducibility Basics
• Descriptive Analytics in Action

• • Describing Data that Matters: Distributions, Central Tendencies, Variability
  • Segmentation and Profiling: Slicing and Grouping for Insights
  • Visual Storytelling: Dashboards and Visuals that Executives Understand
• SQL and Excel for Descriptive Power

• • SQL Essentials for Analysts: Queries, Joins, Aggregations, Window Functions
  • Excel and Pivot Analytics: Fast Summarisation and Scenario Analysis
  • Connecting SQL/Excel to BI Dashboards

Diagnostic Analytics and Exploratory Data Analysis

• Diagnostic Analytics

• • Trend and Pattern Detection: Correlation, Outliers, Variance Analysis
  • Cohort Analysis: Behaviour Over Time (Retention, Churn)
  • Root-Cause Analytics: Structured Frameworks for Diagnosis
• Essential Statistics for Analysts

• • Sampling, Confidence Intervals, Effect Sizes
  • Hypothesis Testing in Context: T-Tests, Chi-Square, ANOVA Explained Simply
• Experimentation and A/B Testing

• • Designing Test-and-Learn Strategies: Control vs. Treatment
  • Reading A/B Test Results: Uplift, Significance, Pitfalls
  • Observational Data and Quasi-Experiments
• Time Series Analysis and Forecasting

• • Temporal Thinking: Seasonality, Trend, Cyclicality
  • Practical Forecasting Models: Moving Averages, ARIMA, Prophet Basics
  • Applications: Sales Forecasting, Demand Planning, Operational Efficiency
  • AI in Forecasting: Machine Learning Approaches for Time Series

Predictive Analytics

• Introduction to Predictive Analytics

• • Regression vs. Classification Problems
  • When to Use Supervised vs. Unsupervised Learning
  • Data Splitting and Validation Basics
• Regression and Predictive Modeling

• • Linear and Logistic Regression: Interpreting Coefficients and Impact
  • Feature Engineering: Turning Raw Data into Predictive Signals
  • Model Evaluation: Accuracy, Precision, Recall, RMSE
• Advanced Predictive Techniques

• • Decision Trees and Random Forests: Intuitive, Powerful, Interpretable
  • Clustering and Segmentation: K-means, Hierarchical Clustering for Business Groups
  • Predictive Analytics in Action: Churn Prediction, Fraud Detection, Demand Modelling
  • AI for Prediction: Neural Networks and Gradient Boosting in Business Applications
• Communicating Predictive Insights

• • Visualising Predictions: Probability Distributions, ROC Curves, Lift Charts
  • Scenario Planning: “What If” Analysis with Predictive Models
  • Telling The Predictive Story: Translating Technical Output for Stakeholders

Prescriptive Analytics, AI and Enterprise Impact

• Prescriptive Analytics Foundations

• • From Prediction to Prescription: Optimising Decisions with Analytics
  • Optimisation and Simulation Basics: Linear Programming, Scenario Simulations
  • AI-Driven Prescriptions: Recommender Systems and Intelligent Optimisation
• Decision Science in Practice

• • Trade-Off Analysis: Balancing Cost, Risk, and Return
  • Prescriptive Dashboards: Integrating Optimisation with BI
  • Human and AI Collaboration: Designing Decision Support Systems
• Ethics, Governance and Responsible Analytics

• • Bias and Fairness in Analytics: Identifying Risks in Models and Decisions
  • Privacy and Compliance: Data Use in Line with Global Standards (GDPR, CCPA basics)
  • Governance for Analysts: Documentation, Versioning, Explainability
  • Responsible AI: Transparency, Accountability, and Human Oversight