2024/25 Taught Postgraduate Module Catalogue
GEOG5927M Predictive Analytics
15 creditsClass Size: 220
Module manager: Will James
Email: W.H.M.James@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
This module is not approved as an Elective
Module summary
This module explores and evaluates techniques used to analyse consumers and their behaviours. The module combines theory and practical examples, with case studies embedded throughout. Through linked and supported practical activities, students develop an understanding of statistical modelling techniques, which inform their individual assessed work. Assessment tests both skills and understanding, with an emphasis in groupwork assessment on how models and accompanying findings can be communicated.Objectives
The module aims to:1. Introduce students to computational modelling techniques;
2. Demonstrate how those techniques can be used for consumer analysis;
3. Evaluate the usefulness of these techniques, and their analysis, for supporting consumer analysis.
Conceptual learning -- the what and why of computational modelling for consumer analysis -- is covered directly via lectures. Skills-based learning – the how of computational modelling for consumer analytics – is covered via hands-on practicals.
Learning outcomes
On completion of this module students will have demonstrated the following subject learning outcomes:
1. Explain and critically evaluate the role of computational analytics and modelling in analysing consumer behaviours, drawing on applied examples.
2. Understand how to execute those modelling techniques using appropriate data sources and software packages.
3. Identify and recommend computational analytics techniques to address particular needs, justifying this recommendation using acquired conceptual and practical knowledge.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Academic. Assessment will involve academic writing and presenting, requiring students to report on work in a concise and evidence-based manner, with integrity.
2. Digital. Students will use digital technology to collaborate with each other and to communicate their work in an engaging way.
3. Work-ready. Using industry standard tools for computational analysis, students will develop IT skills valued in the workplace. Assessments will develop collaboration and teamwork skills.
4. Technical. Technical skills will be developed using modern open software and programming environments.
5. Sustainability. Through behavioural modelling and systems-level thinking, students will consider how sustainable choices can be encouraged.
6. Enterprise. Through the assessed activity, students will work and communicate with others; reasoning over quantitative evidence, they will make decisions under uncertainty.
Syllabus
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 4 | 2.00 | 8.00 |
| Practical | 4 | 2.00 | 8.00 |
| Seminar | 1 | 4.00 | 4.00 |
| Private study hours | 130.00 | ||
| Total Contact hours | 20.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Opportunities for Formative Feedback
Lecture and practical activities have short question and answer sessions integrated, providing regular opportunities for formative assessment of student progress. Additionally, in-depth support and clarification is provided to students via the weekly 2hr computer practicals.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Assignment | Coursework 1: Applied and skills-focussed data analysis activity. | 75.00 |
| Assignment | Coursework 2: Data analysis activity focussed on evaluation and decision-making. | 25.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Resit for Coursework 1 follows same format as the first attempt. Resit for Coursework 2 will take the form of an individual report.
Reading list
There is no reading list for this moduleLast updated: 29/04/2024 16:14:37
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