Module Code: H9DAPA
Long Title Domain Applications of Predictive Analytics
Title Domain Applications of Predictive Analytics
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Vikas Sahni
Module Author: Jenette Carson
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Master’s degree or higher in a computing or cognate discipline.


Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically analyse advanced predictive analytics methodologies in order to assess best practice guidance when applied to complex data mining problems
LO2 Investigate and evaluate key concepts and advanced predictive analytics techniques and assess when to apply such techniques on complex datasets and practical problem domains.
LO3 Contextualise, research and utilise current data approaches, applications and technologies in order to develop predictive analytics strategies to address a variety of real world situations
LO4 Critically review and apply appropriate data mining research and assess research methods
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

A level 8 degree or its equivalent in any discipline


Module Content & Assessment

Indicative Content
General Strategies Revisited
Analytics and Predictive analytics, Big data and predictions, Applying PA, Credit Scores
Business case for PA, domains where it is working, the DARPA challenge, Advertisement options
Ethical issues of marketing analytics, HR analytics, Data aggregation and selling, Civil Rights and Big data, Predictive Policing
Using Social media data, Insights from Consumer Behaviour, Financial Data, Healthcare etc., p-value, Importance of business meaning
Modelling 1
Predictive modelling methods, Decision Trees, Overlearning
Modelling 2
Classification and Regression trees
Ensembles 1
Meta-learning, Recommender systems, Kaggle and Crowdsourcing
Ensembles 2
Bagging, Improvement gains, Generalisations
QA 1
QA systems, Natural Language Processing, Structured Data, Unstructured Collections
QA 2
IBM Watson – history, now it works, applications in different domains
Uplift 1
Persuasion modelling, Incremental modelling, Uplift decision trees
Uplift 2
Applications – Upsell, Cross-sell, Customer Retention
Assessment Breakdown%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Project proposal
Assessment Type: Continuous Assessment % of total: 40
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Project Design
Assessment Type: Continuous Assessment % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Project Report and Presentation
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The repeat strategy for this module is by repeat assessment/project that covers all learning outcomes.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 12 Every Week 12.00
Independent Learning Independent learning (hours) 89 Every Week 89.00
Total Weekly Contact Hours 36.00

Module Resources

Recommended Book Resources
  • Siegel, E.. (2016), Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Wiley Press.
Supplementary Book Resources
  • Dean Abbott, Applied Predictive Analytics: Principle and Techniques for the Professional Data Analyst (Wiley, 2014)..
  • John W. Foreman, Data Smart: Using Data Science to Transform Information into Insight (Wiley, 2013)..
  • Gordon S. Linoff and Michael J. A. Berry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (Wiley, 2011).
  • Anasse Bari, Mohamed Chaouchi, and Tommy Jung, Predictive Analytics For Dummies (For Dummies, a Wiley Brand, 2014).
  • Jeffrey Strickland, Predictive Modeling and Analytics (, 2014)..
  • Vijay Kotu and Bala Deshpande, Predictive Analytics and Data Mining:Concepts and Practice with RapidMiner (Morgan Kaufmann, 2014).
  • John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press, 2015)..
This module does not have any article/paper resources
Other Resources
Discussion Note: