DATA MINING AND TEXT ANALYTICS - 2026/7

Module Overview

Data mining is the process of identifying anomalies, patterns, and correlations within large datasets to predict outcomes. It involves exploring databases to extract potentially useful information such as rules, structures, regularities, and hidden trends. A wide range of analytical techniques is used to transform this information into actionable insights that support decision-making, improve customer relationships, reduce risks, increase revenues, and optimise operational efficiency. In today's data-driven business environment, data mining and business analytics play a critical role in maintaining a competitive advantage. This module introduces key analytical approaches and case studies of well-known applications, including shopping basket analysis (such as loyalty card systems), fraud detection in financial transactions, stock market prediction, risk analysis in banking, web analytics, and social network analysis. In addition, the module introduces AI-driven analytics methods to enhance traditional data mining approaches. These include automated pattern discovery, predictive modelling, and the analysis of unstructured data such as text. Emphasis is placed on how these methods can support more accurate forecasting, deeper insight generation, and improved decision-making in real-world business contexts.

Module provider

Surrey Business School

Module Leader

EMROUZNEJAD Ali (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

• An introduction to the data mining process model for business and management
• CRISP-DM using analytical software environments
• Data pre-processing, visualisation and exploratory analysis used in business intelligence
• Data Modelling techniques and their applications
• Accessing and collecting data from the Web
• Text mining and unstructured data analysis
• AI-driven analytics for pattern recognition and predictive modelling
• Web optimisation from the SEM perspective
• Web Analytics and data mining models in real-world applications

Assessment pattern

Alternative Assessment

N/A

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

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 data mining, text analytics, and AI-driven analytical techniques
• Familiarize students with data mining and web analytics tools
• Equip students with the ability to apply data mining, text analytics, and AI techniques in real-world business contexts for decision-making and intelligence gathering

Learning outcomes

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

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM528

Other information

Employability: Students develop highly sought-after 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 analytical software and AI-enhanced tools to explore large datasets and extract meaningful patterns and trends.

Resourcefulness & Resilience: Students demonstrate the 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.

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 2026/7 academic year.