Module Code: |
H9DAPA |
Long Title
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Domain Applications of Predictive Analytics
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Title
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Domain Applications of Predictive Analytics
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
Vikas Sahni |
Module Author: |
Jenette Carson |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
Master’s degree or higher in a computing or cognate discipline.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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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 |
Dependencies |
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).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Entry requirements |
A level 8 degree or its equivalent in any discipline
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Module Content & Assessment
Indicative Content |
General Strategies Revisited
Analytics and Predictive analytics, Big data and predictions, Applying PA, Credit Scores
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Deployment
Business case for PA, domains where it is working, the DARPA challenge, Advertisement options
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Ethics
Ethical issues of marketing analytics, HR analytics, Data aggregation and selling, Civil Rights and Big data, Predictive Policing
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Data
Using Social media data, Insights from Consumer Behaviour, Financial Data, Healthcare etc., p-value, Importance of business meaning
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Modelling 1
Predictive modelling methods, Decision Trees, Overlearning
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Modelling 2
Classification and Regression trees
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Ensembles 1
Meta-learning, Recommender systems, Kaggle and Crowdsourcing
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Ensembles 2
Bagging, Improvement gains, Generalisations
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QA 1
QA systems, Natural Language Processing, Structured Data, Unstructured Collections
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QA 2
IBM Watson – history, now it works, applications in different domains
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Uplift 1
Persuasion modelling, Incremental modelling, Uplift decision trees
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Uplift 2
Applications – Upsell, Cross-sell, Customer Retention
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Assessment Breakdown | % |
Coursework | 100.00% |
AssessmentsFull Time
Coursework |
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. |
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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 |
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Assessment Type: |
Continuous Assessment |
% of total: |
40 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
Non-Marked: |
No |
Assessment Description: Project Design |
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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 |
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No End of Module Assessment |
Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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Reassessment Description The repeat strategy for this module is by repeat assessment/project that covers all learning outcomes.
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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 |
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Siegel, E.. (2016), Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Wiley Press.
| Supplementary Book Resources |
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Dean Abbott, Applied Predictive Analytics: Principle and Techniques for the Professional Data Analyst (Wiley, 2014)..
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John W. Foreman, Data Smart: Using Data Science to Transform Information into Insight (Wiley, 2013)..
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Gordon S. Linoff and Michael J. A. Berry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (Wiley, 2011).
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Anasse Bari, Mohamed Chaouchi, and Tommy Jung, Predictive Analytics For Dummies (For Dummies, a Wiley Brand, 2014).
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Jeffrey Strickland, Predictive Modeling and Analytics (lulu.com, 2014)..
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Vijay Kotu and Bala Deshpande, Predictive Analytics and Data Mining:Concepts and Practice with RapidMiner (Morgan Kaufmann, 2014).
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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 |
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Other Resources |
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[Website], The Predictive Analytics Guide—articles,
industry portals, and other resources:,
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[Website], The Predictive Analytics Times—industry
news, technical articles, videos,
events, and community:,
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[Website], The Prediction Book,
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