Module code: COM3018

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


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

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

Overall student workload

Workshop Hours: 8

Independent Learning Hours: 88

Lecture Hours: 20

Laboratory Hours: 18

Guided Learning: 10

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
School-timetabled exam/test In-class Test (1 hr) 20
Coursework COURSEWORK (GROUP) 80

Alternative Assessment

The alternative assessment will be an individual coursework with a smaller scope than the group coursework.

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 mid-term class test, which evaluates students' comprehension of essential data science concepts and their relevance to various business contexts. This addresses LO1 and LO2.

  • 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

Formative assessment and feedback during the lab sessions

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:

  • lectures with class discussion

  • lab classes

  • 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: COM3018

Other information

Digital Capabilities

The advanced AI skills taught in this module provide students digital skills that are fundamental to solving many computer science problems today. Building on COM2028 (Artificial Intelligence) teaches students techniques to use computers to identify patterns in large datasets. These skills are highly valued in industry.


This module provides advanced AI, and software skills that are important in solving a many real-life problems today. As the title suggests, students are equipped with practical experience through lab sessions of employing a range of AI techniques to analyse large datasets. The problem-solving skills, theoretical skills, and mathematical and statistical skills are all highly valuable to employers.

Global and Cultural Skills

Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world.

Resourcefulness and Resilience

This module involves practical problem-solving skills that teach a student how to reason about and solve new unseen problems through combining the theory taught with practical technologies for systems that are in everyday use. Students who complete this module will be able to take different data sets and apply a range of techniques to analyse them for patterns. This is a common problem in computer science with widespread applications in industry such as customer data analytics.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computing and Information Technology BSc (Hons) 1 Optional A weighted aggregate mark of 40% is required to pass the module
Computer Science BSc (Hons) 1 Optional A weighted aggregate mark of 40% is required to pass the module
Computer Science MEng 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 2025/6 academic year.