Module overview
Aims and Objectives
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- demonstrate an ability to interpret the data presented in different formats
- communicate ideas and arguments fluently and effectively in a variety of written formats;
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- the different types of marketing analytics activities involved advanced analytical techniques in contemporary organisations;
- the complexities of collecting, integrating, processing and managing data from a wide range of internal and external sources and issues involved for appropriate application;
- how various data science techniques can be used to uncover the potential of various types of data to gain actionable insights and support marketing decisions.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- select and apply suitable methods to collect data, and then integrate, prepare and manage these data;
- evaluate and apply data analytics techniques to solve Marketing Analytics related problems, and then reflect upon the selected approach;
- critically analyse, interpret, organise and visualize quantitative and qualitative data
- derive actionable insights through the results of analyses and communicate them to a non-technical audience.
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Teaching | 24 |
Independent Study | 126 |
Total study time | 150 |
Resources & Reading list
General Resources
Access to journal articles to supplement readings. Journal
Business and management journals. Journal
Textbooks
Mayer-Schonberger. Big data : a revolution that will transform how we live, work, and think. USA: Goldstone Books Limited.
Parr-Rus, O.. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner: A Beginner's Guide. SAS Institute.
Baesens, B. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications.
Kabacoff, R.I. (2011). R in Action: Data Analysis and Graphics with R. Shelter Island: Manning Publications.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Class practicals
- Assessment Type: Formative
- Feedback: Feedback will be provided to students during class/computer practical session, via the Blackboard and through individual meetings.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Group presentation | 30% |
Report | 70% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Individual Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Individual report | 100% |
Repeat Information
Repeat type: Internal & External