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MAN-20207 - Module Specification
Keele Business School
Faculty of Business, Law, Humanities & Social Sciences

For academic year: 2026/27 Last Updated: 17 March 2026

MAN-20207 - Advanced Analytics and Business Intelligence
Coordinator: Rotimi Ogunsakin
Lecture Time: See Timetable...
Level: Level 5
Credits: 30
Study Hours: 300
School Office: 01782 733094

Programme/Approved Electives for 2026/27

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2026/27

In the Advanced Analytics and Business Intelligence module, you will gain a deep understanding of advanced data analytics techniques and their real-world applications. The module covers statistical and machine learning methods, such as classification, clustering, predictive modelling, text mining, and visual analytics, all within the context of business and organisational decision-making. You will also explore the principles of Business Intelligence (BI) and its role in transforming data into actionable insights.
You will acquire practical skills in building machine learning models, automating data workflows, developing dynamic data pipelines, and integrating real-time data for predictive and prescriptive analytics. Additionally, you will learn to create interactive visualisations that support evidence-based decision-making. These skills will be developed through hands-on exercises, case studies, and projects focused on real-world business challenges.
This module is key to Business Management with Analytics programme, providing the tools needed to analyse complex data and support strategic decision-making in various industries. It will enhance your technical expertise, preparing you for careers in data analytics in the business domain, such as in finance, marketing, operations, management, manufacturing, consulting, and IT.

Aims
The Advanced Analytics and Business Intelligence module aims to equip students with a deep understanding of advanced data analytics techniques, their theoretical foundations, and their practical applications in industrial and organisational contexts. The module introduces students to advanced data analytics techniques underpinned by statistical and machine learning models, including classification, clustering, predictive modelling, text mining, and visual analytics. It further examines the core principles of Business Intelligence, emphasising its critical role in modern decision-making processes, and equips students with technical expertise in automating data engineering workflows, developing dynamic data pipelines, and integrating real-time data into statistical and machine learning models for descriptive, diagnostic, predictive, and prescriptive analytics. The module also emphasises the conceptualisation and implementation of interactive and real-time visualisations to support evidence-based decision-making to generate strategic insights.

Intended Learning Outcomes

demonstrate a comprehensive understanding of advanced data analytics techniques, including their theoretical underpinnings and practical applications in industries.: 1
apply advanced analytics techniques undepinned by statistical and machine learning models, such as classification, clustering, predictive modelling, text mining, and visual analytics to solve complex business problems.: 1
critically evaluate the principles of Business Intelligence and its role in supporting modern business decision-making processes.: 2
develop and automate data engineering workflows and dynamic data pipelines to integrate real-time data into already developed statistical and machine learning models: 2
design and implement interactive, real-time visualisations that enable evidence-based decision-making and strategic insights: 2

Study hours

12 x 2-hour Lecture
12 x 2-hour Tutorial/laboratory
24 x 6-hour independent study and practices (Students read recommended articles; watch recommended videos; read recommended chapters of the textbook; and complete recommended practical exercises)
108 guided learning, practical tasks, assessment preparation and individual support

School Rules

None

Description of Module Assessment

1: Assignment weighted 60%
Individual Report
In this individual report, you will be provided with a specific dataset and a corresponding business case scenario. You will be required to apply relevant statistical and machine learning techniques—such as classification, clustering, and predictive modelling—to analyse the data and generate insights. The report will demonstrate: The correct application and interpretation of the chosen statistical and machine learning models. A critical analysis of the results, highlighting key findings and their implications. An evaluation of the business impact of the techniques applied, explaining how the insights can inform decision-making. Justifications for selecting specific techniques, with a clear rationale for their suitability in addressing the business case scenario. The report will be 1,500 words in length, excluding references, tables, analytics artefacts (models, graphs, charts, etc) and appendices. The focus should be on the clarity of the analysis, the use of appropriate statistical and machine-learning techniques, and the quality of the justification for the techniques chosen

2: Group Assessment weighted 40%
Group Presentation
In this assignment, you will work in groups of 4-5 students to tackle a real-world business problem using the provided industry datasets. Each group (max five students) is expected to apply the statistical and machine learning techniques covered in the module, as well as business intelligence and data pipeline automation methods, to derive actionable insights. You will need to demonstrate: -Proficiency in selecting and applying appropriate statistical and machine learning models to analyse the data. -The ability to automate data engineering workflows that support descriptive, diagnostic, predictive, and prescriptive analytics. -Expertise in designing and implementing interactive, real-time visualisations that facilitate evidence-based decision-making. You will develop presentation slides and deliver a 15-minute presentation of your analysis and solutions, followed by a 5-minute Q&A session. The presentation will take place face-to-face on campus, with all group members required to participate actively. The tutors and an independent evaluator/second marker (a member of the academic team) shall be present to ensure fairness. Each group member will be required to complete a peer assessment form. Therefore, depending on the peer assessment returned, members of the same group may receive different grades, as the grades may be adjusted based on peer feedback to ensure fairness. Reasonable adjustments shall be put in place for students who are unable to present in person. They will be able to record their section of the group presentation and submit. This will in no way impact the overall group grade or the student's individual grade.

The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 17 March 2026