Marketing Data Science

Learning Outcomes

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:

• 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;

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;

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

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

Resources & Reading list

General Resources

Access to journal articles to supplement readings. Journal

Business and management journals. Journal

Textbooks

Baesens, B. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications.

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.

Parr-Rus, O.. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner: A Beginner's Guide. SAS Institute.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

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.

Repeat Information

Repeat type: Internal & External