Advanced Data Analysis Techniques

Program Overview

Once descriptive reporting is in place, organisations need analysts who can explain why results change and what is likely to happen next. This programme develops advanced techniques: multiple and logistic regression, hypothesis testing and ANOVA, clustering and segmentation, dimensionality reduction, time series forecasting and an introduction to predictive modelling with model validation. Exercises use Python or R on business datasets from government services, banking and energy, with emphasis on choosing the right method and communicating uncertainty honestly.

Learning Outcomes

• ✓Build and interpret a multiple regression model, checking assumptions, multicollinearity and residuals.
• ✓Apply logistic regression to a binary outcome and evaluate it with a confusion matrix and ROC curve.
• ✓Test differences between groups using t-tests, ANOVA and appropriate non-parametric alternatives.
• ✓Segment customers or assets with k-means or hierarchical clustering and profile the resulting groups.
• ✓Produce a time series forecast with seasonality and report prediction intervals.
• ✓Validate models with train-test splits and cross-validation to avoid overfitting.

Target Audience

• Data analysts and BI analysts moving into advanced methods
• Statisticians and research officers
• Financial and risk analysts
• Performance measurement specialists in government entities

Detailed Program Modules

Training Methodology

Applied Project & Assessment