Undergraduate Programme and Module Handbook 2025-2026
Module BUSI3541: Business Analytics for Artificial Intelligence (M&M)
Department: Management and Marketing
BUSI3541: Business Analytics for Artificial Intelligence (M&M)
| Type | Tied | Level | 3 | Credits | 20 | Availability | Available in 2025/2026 | Module Cap | Location | Durham |
|---|
| Tied to | N509 |
|---|---|
| Tied to | N510 |
| Tied to | N511 |
Prerequisites
- Marketing Research Methods (BUSI2351)
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- The module aims to:
- To understand the foundations of analytics and Artificial Intelligence methods.
- To provide knowledge of, and ability to apply, analytics and Artificial Intelligence techniques in marketing.
- To implement analytics and Artificial Intelligence models using a programming language (e.g., Python).
- To effectively communicate data insights and develop skills in using analytics and Artificial Intelligence to inform marketing decisions and strategies.
- To explore the ethical considerations and challenges associated with data and AI in marketing.
Content
- Fundamentals of programming for analytics and Artificial Intelligence
- Introduction to analytics and Artificial Intelligence
- Descriptive techniques e.g., data visualisation, data analysis, descriptive statistics, with applications to marketing problems.
- Predictive techniques, e.g., regressions, classification, clustering and forecasting, with applications to marketing problems.
- Prescriptive techniques, e.g., linear optimisation, with applications to marketing problems.
- Artificial Intelligence advanced application in marketing problems (e.g., marketing segmentation, topic modelling).
- Ethical considerations of data and Artificial Intelligence.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students should be able to have:
- An understanding of the key concepts and techniques in analytics and Artificial Intelligence relevant to marketing.
- Knowledge of a range of analytics and Artificial Intelligence techniques and ability to apply them critically to marketing problems.
- An understanding of the applicability and limitations of analytics and Artificial Intelligence techniques.
Subject-specific Skills:
- By the end of the module students should be able to:
- Choose appropriate analytics and Artificial Intelligence techniques for some key marketing problems.
- Implement business analytics models using a programming language, e.g., Python.
- Interpret the results of analytics and Artificial Intelligence models and their relevance for companies.
Key Skills:
- Digital literacy skills
- Communication, both written and verbal
- Problem-solving
- Critical thinking
- Data analysis
- Presentation skills
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Teaching is via lectures and workshop. Learning takes place through attendance at lectures, preparation for and participation in workshops, and private study.
- Formative assessment is by means of a series of in-class activities.
- In readiness for this module, teaching and learning activities will be scheduled on Marketing Research Methods for delivery in the Easter term on Year 2.
- Summative assessments include four homework sets, 10 questions per set, a 500-word executive summary and one video recording of the final project, which includes the implementation of analytics and Artificial Intelligence tools to solve a marketing problem.
- Workshops will be scheduled in the Easter term of Year 2 of study on BUSI2351 Marketing Research Methods in readiness for starting Business Analytics for Artificial Intelligence in Year 3.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 10 | Weekly | 2 hours | 20 | |
| Workshops | 4 | Fortnightly | 2 hours | 8 | ■ |
| Preparation and Reading | 172 | ||||
| Total | 200 |
Summative Assessment
| Component: Project | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Exercise | 10 Questions | 10% | |
| Exercise | 10 Questions | 10% | |
| Exercise | 10 Questions | 10% | |
| Exercise | 10 Questions | 10% | |
| Review | 500 word executive summary of the final project | 10% | |
| Presentation | 10 minute video recording of the final project | 50% | |
Formative Assessment:
Students will be set various formative tasks throughout the module. In particular students will work on teaching cases. To solve and analyse these cases they will apply business analytics techniques and interpret and discuss their output. The purpose of these tasks is to provide students with feedback on developing their technical skills and their understanding of the application of business analytics techniques to prepare them for the summative assessments.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University