Module code: COMM053

Module Overview

In today’s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.

Module provider

Computer Science and Electronic Eng

Module Leader

THOMAS Spencer (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 8

Independent Learning Hours: 98

Lecture Hours: 20

Laboratory Hours: 18

Captured Content: 6

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative content includes:

  • Business Data Science, Big Data and Business Analytics

  • Business Intelligence (BI), Decision Support Systems (DSS) and Data Warehouses (DW)

  • Data Mining Life Cycle & Cross Industry Standard Process for Data Mining (CRISP-DM)

  • Data Preparation

  • Introduction to Machine Learning Tasks

  • Association Rule Mining

  • Clustering Algorithms

  • Decision Tree Learning

  • Regression

  • Model Evaluation

  • Visualisation

  • Project Management

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK (GROUP) 100

Alternative Assessment

Coursework I (group) - Implement a Business Analytics Solution based on a case study

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module’s intended learning outcomes described above.

Thus, the summative assessment for this module consists of:

  • A group project based on a case study. The students will need to analyse the business problem, determine the business objectives, relevant performance metrics to measure them and provide the business requirements for a business analytics solution and then implement and evaluate it. This will address LO1 to LO5 .


Formative assessment and feedback

Self-assessment lab exercises will be provided in SurreyLearn. Feedback is also given during the class discussions and lab sessions and as part of the feedback provided for the group project.

Module aims

  • The aim of this module is to introduce students to Business Analytics from a practical point of view.
  • Students will also learn about related concepts such as Data Mining Life Cycle, Machine Learning Algorithms, Model Evaluation and Data Visualisation.
  • Students will learn about applications of Business Analytics through case studies and practical examples in lab sessions and coursework.

Learning outcomes

Attributes Developed
001 Analyse business objectives and the choice of performance metrics to measure them and translate these into Key Performance Indicators KCP
002 Understand and describe different data mining techniques (e.g. classification, clustering, regression, etc) and how these can be applied to different real-world problems KT
003 Analyse a given business problem and provide a well-reasoned rationale for the choice of tools and techniques KCPT
004 Implement and evaluate a business analytics solution for a given scenario and justify the approach KCPT
005 Appreciate the importance of team-work when carrying out the above activities PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

help students achieve the intended learning outcomes of the module through in-class discussions and hands-on exercises in the lab sessions and via the coursework.


The learning and teaching methods include:

  • 20 hours of lectures with class discussion

  • 18 hours of lab classes

  • 8 hours of workshop and presentations

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: COMM053

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Data Science MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.