Module code: COM3018

Module provider

Computer Science

Module Leader


Number of Credits


ECTS Credits



FHEQ Level 6

JACs code


Module cap (Maximum number of students)


Module Availability

Semester 1

Overall student workload

Independent Study Hours: 110

Lecture Hours: 20

Laboratory Hours: 20

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK (GROUP) I 50
Examination 2 HOUR EXAM 50

Alternative Assessment

Implement a Business Analytics Solution based on a case study.

Prerequisites / Co-requisites


Module overview

Motivations   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 drive the business forward.

Examples   Business Analytics is a set of tools that enable data collected within and outside a company to run its business. These tools are used by online companies to provide recommendations or supermarkets to provide you with vouchers for products similar to the ones you have previously bought. They enable insurance companies to evaluate whether or not claims are likely to be fraudulent. In healthcare, the tools can be used to optimize patient treatments and resource allocation.

Analytic capabilities   Business Analytics operates at the interface between the technology and the business information system so that a company can turn all available data, both within and outside the organisation, into useful information that can be used to make both operational and strategic decisions. This is achieved by visualising data in different ways, forecasting the future trends, categorising customers into different market segments for targeted marketing, and computing Key Performance Indicators (KPIs), among others.


Module aims

The aim of this module is to introduce students to business analytics from a practical point of view. Students will learn about applications of business analytics through case studies and practical examples in laboratory sessions. They will also work in team since data analysts work with domain experts, data managers, as well as decision- and policy makers.

Technical details like data warehousing, Business Intelligence and visualisation will be covered. In addition, the students will learn how to translate business objectives into Key Performance Indicators; and the project management challenges of integrating any Business Analytics Solution into the existing business processes.

While the emphasis is on the application, students will be using an industry standard tool set provided such as Matlab, Tableau, Cognoz Insight, SPSS Modeller, and Watson Analytics.

Learning outcomes

Attributes Developed
1 Discuss the importance of business objectives and analyse the appropriateness of the choice of performance metrics to measure them KCP
2 Understandand and describe the different Business Analytics techniques(eg business intelligence, data warehousing, data mining, reporting, visualisation, etc) how they are applied to different real world examples K
3 Analyse a given business problem and provide a well-reasoned rationale for the choice of Business Analytics solution KCPT
4 Discuss the benefits/drawbacks of different approaches to the roll-out of a solution(top-down or bottom-up, etc) and its implication on project management. KCT
5 Implement a business analytics solution for a given scenario and justify their approach KCPT
6 Appreciate the importance of carrying out the above activities by working closely in a team PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes:

Why analytics – business game (alignalytics)
Business Intelligence

Anatomy of a BI system
Running a BI Project
Databases and Data Warehouse
Performance and Optimisation
Data Query and Manipulation
Business Impact and techniques

Performance Management and Finance

Performance Reporting & Scorecarding
Close, Consolidate & Report
Financial Statement Reporting
Profitability Modelling and Optimisation
Planning, Analysis and Forecasting

Analytical Applications 1

Data Preperation
Risk analytics

Analytical Applications 2

Predictive Modelling
Text/Social Media Analytics
Crime/Fraud Analytics

Big Data       

Big Data (Volume, Velocity, Variety)
Unstructured Data,
Big Sheets

Cognitive Computing

Learning Systems
Inside a real-time analytics solution

Analytics Projects Issues and Challenges

Project Management – discuss a real project and the issues that arise
Data Privacy and Security
Data accuracy and validity
Deployment and Maintenance
Legal Issues and Responsibilities

Methods of Teaching / Learning

The learning and teaching strategy is designed to help students achieve the learning outcomes of the module through

in-class discussions of case studies

hands-on exercises in the laboratories


The learning and teaching methods include:

10 weeks of 2 hours of lectures and 18 hours of laboratory sessions and tutorials


Assessment Strategy

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

Thus, the summative assessment for this module consists of:

A group project looking at 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 before implementing it. This will address LO 1—6 .

An unseen 2 hours exam addressing LO1, 2, 3 and 4.

The group project will be due around week 9. The exam takes place at the end of the semester during the exam period.


Formative assessment and feedback

No formative assessment is provided but feedback is given during the class discussions and laboratory sessions and as part of the feedback provided for the summartives assessments.


Reading list


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computer Science BSc (Hons) 1 Optional A weighted aggregate mark of 40% is required to pass the module
Computing and Information Technology BSc (Hons) 1 Optional A weighted aggregate mark of 40% 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 2017/8 academic year.