Module overview
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- derive actionable insights through the results of analyses and communicate them to a non-technical audience.
 - evaluate and apply data analytics techniques to solve Marketing Analytics related problems, and then reflect upon the selected approach;
 - select and apply suitable methods to collect data, and then integrate, prepare and manage these data;
 - critically analyse, interpret, organise and visualize quantitative and qualitative data
 
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 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.
 - the different types of marketing analytics activities involved advanced analytical techniques in contemporary organisations;
 
Syllabus
Learning and Teaching
Teaching and learning methods
| Type | Hours | 
|---|---|
| Independent Study | 126 | 
| Teaching | 24 | 
| Total study time | 150 | 
Resources & Reading list
                                      General Resources
                                
  
Business and management journals. Journal
Access to journal articles to supplement readings. Journal
Textbooks
Baesens, B. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications.
Parr-Rus, O.. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner: A Beginner's Guide. SAS Institute.
Mayer-Schonberger. Big data : a revolution that will transform how we live, work, and think. USA: Goldstone Books Limited.
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