Module code: MAN2189

Module Overview

The aim of this module is to introduce many of the important new ideas in data mining and business analytics, explain them as statistical framework, and describe some of their applications in Business, Finance, Marketing, and Management.

Data mining is the process of mining large quantity of data to extract useful information. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. 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. An understanding of business analytics and data mining concepts and techniques can offer a valuable advantage in the competition for jobs and placements. This sector remains one of just a few areas showing consistent growth in terms of job opportunities and salaries even during recession.

Module provider

Surrey Business School

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

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

Overall student workload

Independent Learning Hours: 117

Lecture Hours: 11

Laboratory Hours: 11

Captured Content: 11

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

The module content will focus on a selected set of critical areas in data analytics and the software tools used. As an indication of the kind of concepts that will be covered, below is an indicative set of topics:

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

  • Introduction to IBM SPSS Modeler software

  • Data pre-processing, visualisation and exploratory analysis used in large datasets

  • Use of AI including neural network in data mining and its application in risk analysis

  • Classification, decision trees and their applications.

  • Association and rule mining and their applications to business and management

  • Data mining predictive models and their applications

  • Accessing and collecting data from the Web and introduction to text mining

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Analytics Tools Group coursework 60
Examination Online Analytics Tools examination 40

Alternative Assessment

An alternative to the group project is a defined individual project using a given dataset and problem description (1500 words for the individual).

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

· Knowledge of the entire data analytics workflow process

· Appreciation of the objectives for robust data analytics workflow processes

·Ability to apply the theories, conceptual frameworks and methodologies that underpin data analytics and Machine learning algorithms in real applications

Thus, the summative assessment for this module consists of:

  • Analytics Tools Group coursework 3000 words (60%)

For the project coursework, the students will work either individually or in small groups, to apply and deepen their knowledge with the criteria related to learning outcomes (1) to (5). The project will include the analysis of a set of real-world data using data analytics techniques within the taught software tools and visualising the results appropriately to produce a written report. Evidence of background reading must be provided. The project provides an opportunity for the student to experience the whole process of data analytics using machine learning by using the taught software tools using the approaches introduced in the workshops/labs, to undertake all necessary steps using the standard data mining process. The student will present the project findings and conclusions in a written report, give an oral presentation and provide code / software used, all of which will be marked.  Students will be informed about the coursework topic and individual/group at the beginning of the semester.

· Analytics Tools Computer lab examination (40%)

The 2-hour online examination (4-hour window) will be a standard open book computer based - examination, with material coming directly from the module content. The broader topics for the questions will be covered during the taught and laboratory sessions. The problems will be similar to those discussed in the taught component, computer laboratory or indicated in the essential reading material. The answers will mainly be discursive and will be in alignment with the criteria related to learning outcomes.


Formative assessment

· Online assessments tied to lab sessions



· Group feedback on lab session results – common errors, examples of good practice.

· Individual feedback after online assessment covering key concepts.

Module aims

  • Introduce the fundamentals of data mining and its application to business analytics.
  • Identify the various algorithms of data mining and how to effectively apply them to real-world problems.
  • Explore leading data mining software, as well as Python and R, using examples in many areas such as Business, Finance, Marketing, and Management.

Learning outcomes

Attributes Developed
001 Identify the key steps of the data mining process. KP
002 Apply key methods using analytics software for turning diverse data into useful insights. KPT
003 Demonstrate the ability to use data mining packages such as IBM-Modeler and coding in Python and R for analytics. KPT
004 Construct text mining models for analysing complex data structures. CPT
005 Recognise and tools needed to reveal patterns and valuable information hidden in large data sets. KCPT

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:

· encourage a critical understanding of the importance of a robust data workflow that translate diverse, often messy data sources into useful cleaned/insights for decision-making.

· cultivate an understanding of the main issues and challenges; provide a coherent conceptual framework; develop a critical awareness of the various approaches of machine learning algorithm.

· introduce students the concept of a robust data workflow and to teach the basics of the various tools that enable operational delivery of data insights in business. The intention is to teach the objectives of the data workflow process (e.g. clean data, simple to understand insights) and how that relates to the decisions to be made off the data, rather than an exhaustive list of prospective software.


The course will be practical, encouraging students to get hands-on with example data analytics software tools, such as IBM SPSS Modeler and programming in Phyton and R that allow them to progress through all stages of the data workflow process, giving them an appreciation of the operational processes and software required to deliver effective operational data processes in business. Methods of teaching will be delivered and reinforced through a combination of lectures (1 hour x 11 weeks), computer laboratory sessions (1 x 11 weeks), captured contents (1 hour x 11 weeks).

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: MAN2189

Other information


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Management (Business Analytics) BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Business Management BSc (Hons) 2 Optional A weighted aggregate of 40% overall and a pass on the pass/fail unit of assessment is required to pass the module
Business Economics BSc (Hons) 2 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 2023/4 academic year.