Summary of content

This module introduces students to Predictive analytics taking the analysis a step further by forecasting future outcomes or trends based on historical data and statistical modelling techniques. It involves developing predictive models that can anticipate future events, behaviours, or trends, enabling businesses to proactively identify opportunities and risks. Predictive analytics helps businesses make data-driven forecasts, optimise resource allocation, and develop strategies to capitalise on emerging opportunities or mitigate potential threats.

The module provides a comprehensive overview of predictive analytics, focusing on the development, interpretation, and application of predictive models in business and management contexts. Students will begin by exploring the fundamental concepts and strategic uses of predictive analytics before progressing through the essential stages of the predictive modelling pipeline. This includes data preparation, feature engineering, and the application of statistical techniques such as regression analysis, classification methods and time series forecasting. Each unit is designed to build both technical competence and analytical judgement, enabling students to select suitable modelling techniques based on dataset characteristics and business objectives.

The latter part of the module emphasises the practical application of predictive analytics in business functions such as industry analysis, lifetime value prediction, and recommendation systems. Students will examine real-world case studies and datasets to extract actionable insights and communicate findings effectively to both technical and non-technical stakeholders. A strong focus is placed on model evaluation, interpretability, and ethical considerations, ensuring students can deploy predictive models responsibly and transparently. By the end of the module, students will be able to critically assess model performance, deliver data-driven recommendations, and navigate the ethical and operational complexi

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