Module code: MANM528

Module Overview

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more.
Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.

Module provider

Surrey Business School

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

JACs code:

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

Overall student workload

Independent Learning Hours: 90

Lecture Hours: 11

Laboratory Hours: 22

Guided Learning: 12

Captured Content: 15

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

  • An introduction to data mining process model for business and management

  • CRISP-DM using a Data Mining Package

  • Data pre-processing, visualisation and exploratory analysis used in business intelligence

  • Data Modelling models and their applications

  • Accessing and collecting data from the Web

  • Text mining

  • Web optimization from the SEM perspective

  • Web-Analytics and data mining models in real-world applications

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Business Intelligence Report 100

Alternative Assessment

Not applicable

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate: the ability to analyzing a large batch of information to discern trends and patterns Thus, the summative assessment for this module consists of: 

  • Practical based report (addresses all LOs)

Formative assessment

Students are presented with various business problems that they have to collect data for data warehousing; store, manage and organise the data for the desired purpose; use application software to sort the data based the intended purpose for decision making.


Feedback on student's approach of business problems will be given during the computer laboratory sessions. Solutions and recommendations will need to be given with rationale.

Module aims

  • Facilitate a comprehensive understanding of the various data mining and web analytics techniques
  • Familiarize students with data mining and web analytics tools
  • Equip students with the skills to apply data mining and web analytics techniques effectively with real data in business context for intelligence gathering and decision making

Learning outcomes

Attributes Developed
001 Demonstrate an understanding of the data and resources available on thee web of relevance to business intelligence CK
002 Demonstrate capability to access structured and unstructured data KP
003 Apply the practical experience and the theoretical insight needed to reveal patterns and valuable information hidden in large data sets CKP
004 Practice with leading data mining methods and their applications to real- world problems CKPT
005 Apply the fundamentals of business intelligent on business decision making CKPT

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: cultivate an understanding of the main issues and challenges that can be solved using effective data mining and web analytics techniques. The teaching and learning methods include: a lecture, laboratory session, captured contents and guided learning every week as well as take home activities. Web-based learning support and electronic resources will also be provided.

The module will develop analytical skills and the understanding of the subject area through:

  • Lectures

  • In-class discussion

  • Independent learning

  • Guided learning

  • Captured content

The module will develop practical skills through:

  • Lab sessions

  • Coursework

  • Peer discussion

  • Independent learning

  • Guided learning

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: MANM528

Other information

Employability: Students develop highly-sought data mining and web analytics skills which they apply in a range of different business contexts.

Digital Capabilities: Throughout the module, students learn to use a data mining package to explore and analyze large blocks of information to glean meaningful patterns and trends

Resourcefulness & Resilience: Students demonstrate capability to either work on their own or in a team to prepare a substantial piece of coursework which will involve initiative, challenges and opportunities to demonstrate their creativity and an ability to adapt data analysis based on the chosen business context.

Sustainability: The module aims at developing students' understanding, awareness, and capability to develop innovative and sustainable solutions using data mining and web analytics for actionable insights.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Analytics 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 2023/4 academic year.