• MKAY9006
  • Predictive Analytics

  • Credits (ECTS): 10
  • Marketing

Modules are delivered
as part of a programme.
To apply for the
programme,
see the DIT website

Module Description

The module is designed to introduce business graduates without specific backgounds in Information Systems or analytical methods, to a variety of predictive techniques using industry-standard (but highly accessible) purpose-designed software, geared to the special nature of ?Big Data?, particularly that provided by internet sales and marketing campaigns. Participants will also receive a thorough grounding in relevant underlying statistical/ probability theory but in a practical and user-friendly lab environment.

Indicative Syllabus

Understanding data.
Introduction to the PASW interface.
The tools of predictive analytics.
Computer techniques to automate data processing.
Customer classification techniques including logistic regression.
Using time series to forecast from historical data.
Using Bayesia Tree Networks predictively
Using neural and C&R Tree Networks to design retail sales promotions.
Customer classification using discriminant analysis.
Using rule induction models to analyse retail customer behaviour.
Identifying customer clusters using k-nearest neighbour models.

ISCED:342: DO NOT USE - ARCHIVE HEA 2014
Total Contact Teaching Hours:20

Please note that the catalogue is provided as a guide to modules in DIT. Not all modules listed will necessarily be offered every year and new modules may also be added. Information subject to change. For detail on specific programmes/modules please contact the relevant School directly.