Module overview
This module focuses on in-depth and advanced statistical tools for analysing big data. The module uses real raw data (as well as big data) that require knowledge of data pre-processing prior to systematic data analysis. This includes using suitable analytical statistical tools (e.g. R, SAS, etc.). The module will focus on how different advanced statistical techniques could be used in the marketing analytics area.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- critically analyse, interpret, organise and visualize quantitative and qualitative data
- select and apply suitable methods to collect data, and then integrate, prepare and manage these data;
- 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;
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- 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;
- the complexities of collecting, integrating, processing and managing data from a wide range of internal and external sources and issues involved for appropriate application;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- communicate ideas and arguments fluently and effectively in a variety of written formats;
- demonstrate an ability to interpret the data presented in different formats
Syllabus
The topics covered include:
- How this module integrates with previous analytics modules; Revision of necessary fundamental content from previous semester; Overview of expected goals;
- The management problems associated with, data necessary for, data processing needed for, concepts underlying, practical implementation of, and interpretation and communication of results from relevant statistical techniques
Learning and Teaching
Teaching and learning methods
Teaching methods include:
- Lectures explaining the problems and concepts
- Laboratory sessions where the tools can be practiced and applied
- Guided independent study
Through all delivery methods the content and presentation of the module will be accessible and inclusive.
Learning activities include:
- An assignment
- Laboratory work
- Case study / problem solving activities
- Private study
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
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.
Kabacoff, R.I. (2011). R in Action: Data Analysis and Graphics with R. Shelter Island: Manning Publications.
Baesens, B. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications.
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