Predictive and Prescriptive Analytics - CB794

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury Autumn
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6 15 (7.5)

Pre-requisites

BUSN3130 Introduction to Statistics for Business (or equivalent)

Restrictions

None

2019-20

Overview

The aim of this module is to equip students with basic knowledge of analytics tools to analyse and interpret data, forecast future trends and optimise decisions in many areas of business, including operations, marketing and finance.
The module covers two indicative themes as follows:

1. Predictive analytics. In this part of the module, students will learn approaches to extract information from existing data sets in order to determine patterns and predict future outcomes and trends. Approaches include regression analysis, forecasting techniques, simulation and data mining.

2. Prescriptive analytics. In this part of the module, students will learn how to develop optimisation models to support business decision making. Students will be guided through demonstrations involving a variety of business problems, including transportation, assignment, product mix and scheduling problems.

Details

This module appears in:


Contact hours

Total contact hours: 21
Private study hours: 129
Total study hours: 150

Method of assessment

Main assessment methods
In-Course Test 1, 45 minutes (20%)
In-Course Test 2, 45 minutes (20%)
Examination, 2 hours (60%).

Reassessment methods
100% exam

Indicative reading

Albright S. and Winston W.L. (2016). Business Analytics: Data Analysis & Decision Making (6th Ed). Boston, MA: Cengage.

Evans, J. R. (2013). Business Analytics. Methods, Models and Decisions. Harlow: Pearson Education.

Winston, W.L. (2004). Operations Research: Applications and Algorithms (4th Ed.), Belmont, MA: Duxbury Press.

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Use predictive and prescriptive analytic techniques to handle a variety of business problems.
- Apply regression analysis and forecasting techniques to characterise relationships among business variables, identify patterns in data and predict future trends.
- Build and solve linear optimisation models and interpret their results for effective decision making
- Develop a systematic understanding of different types of optimisation models and how they can be applied in practice to solve problems in different business contexts

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Use a variety of scientific approaches to build and solve models for a range of practical management problems.
- Analyse the models and be able to make recommendations based on that analysis.
- Demonstrate an ability to select the most appropriate solution technique for particular problems.
- Plan work and study independently using relevant resources.

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