Prescriptive and Predictive Analytics module (BU51039)

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This module will provide the required knowledge of analytics tools to analyse and interpret data for decision making in businesses, within a wider ethical context. The module will introduce the key concepts, methods and tools for business analytics, which guide data-driven decision making in organisations. There are three elements for analysing data in businesses: descriptive statistics, prescriptive statistics and predictive statistics.

  1. Prescriptive Analytics
    In the first part of the module, students will learn how to frame business questions mathematically, and develop optimisation models to support and recommend business solutions. This module uses a suite of models in Management Science (network models, Data Envelopment Analysis, linear and nonlinear programming) to address real life business questions with examples being scheduling and distribution problems, marketing product mix and production and revenue maximisation.
  2. Predictive Analytics
    In the predictive analytics component of this module, students will learn about the applications of machine learning in order to develop and compare predictive models. Predictive analytics are very useful for depicting and forecasting trends which are more informative than traditional descriptive statistics. This module will start off by explaining the predictive analytics tools and analytics environment. Approaches used in this module will include classification systems, nearest neighbour, and various forecasting techniques.