Module Details
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title PREDICATIVE ANALYTICS FOR DECISION MAKING
Code CKIT526
Coordinator Prof FP Coenen
Computer Science
Coenen@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2018-19 Level 7 FHEQ Whole Session 15

Aims
    1·   To provide students with a systematic and critical understanding of key predictive analytics techniques.
    2·   To provide students with a comprehensive knowledge of the types of business problems predictive analytics can solve.
    3·   To provide students with a practical understanding of the process of applying the tools and techniques of predictive analytics in commercial environments.
    4·   To provide students with an opportunity to apply the tools and techniques learnt on the module to typical business problems.

Learning Outcomes

A thorough and critical knowledge of key predictive analytics techniques.

A deep and critical understanding of how to match appropriate predictive analytics technique to a given business related problem.

A significant ability to translate results from a predictive analytics process into business actions.

An ability to deploy the end-to-end process of predictive analytics in commercial settings.

Syllabus
1 
Week 1
Common predictive analytics techniques (e.g. logistic regression, classification trees, collaborative filtering) and model development best practices. 
 
Week 2
The End-to-end process of predictive analytics in the context of commercial environments: (i) problem identification, (ii) data assessment, (iii) data preparation, (iv) model development, (v) model deployment and (vi) making insights available to decision makers.
 
Week 3
Application of predictive analytics scoring models I: customer acquisition. Use case on optimising direct marketing campaigns utilising lists of scored prospects. 
 
Week 4
Application of predictive analytics scoring models II: customer retention. Use case on decreasing customer churn.
 
Week 5
Application of predictive analytics scoring models III: operational efficiency. Use case of optimising collection agent’s time by prioritising outreach to "high risk customers".
 
Week 6
Application of predictive analytics scoring models IV: recommender systems. Use case on product recommendations in an online store. 
 
Week 7
End-to-end application of predictive analytics techniques in business environments I.
 
Week 8
End-to-end application of predictive analytics techniques in business environments II (continued from week 7). 

Teaching and Learning Strategies

Online Learning - Weekly seminar supported by asynchronous discussion in a virtual classroom environment facilitated by an online instructor.

Number of hours per week that students are expected to attend the virtual classroom so as to participate in discussion, dedicated to group work and individual assessment is 7.5.

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           60
Weekly seminar supported by asynchronous discussion in a virtual classroom environment facilitated by an online instructor.
60
Timetable (if known)           Number of hours per week that students are expected to attend the virtual classroom so as to participate in discussion, dedicated to group work and individual assessment is 7.5.
  		 
Private Study 90
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Coursework  Weekly Discussion Qu  Whole session  40  No reassessment opportunity  Standard UoL penalty applies  eight discussion questions There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. 
Coursework  750-1000 word report  Week 1  8  No reassessment opportunity  Standard UoL penalty applies  Import and prepare for modelling a data set using RapidMiner There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. 
Coursework  750-1000 word report  Week 2  8  No reassessment opportunity  Standard UoL penalty applies  Explore a data set using RapidMiner’s statistical and visualisation features There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. 
Coursework  1250-1800 word repor  Week 4  14  No reassessment opportunity  Standard UoL penalty applies  Design, build and validate an ensemble classification model There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. 
Coursework  750-1000 word report  Week 5  8  No reassessment opportunity  Standard UoL penalty applies  Improve the performance of a logistic regression model There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. 
Coursework  750-1000 words repor  Week 6  8  No reassessment opportunity  Standard UoL penalty applies  Design, build and validate a product recommender model There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. 
Coursework  1500 word report des  Week 8  14  No reassessment opportunity  Standard UoL penalty applies  Design, build, and validate a customer segmentation (clustering) model There is no reassessment opportunity, The nature of the adopted online learning paradigm is such that no reassessment opportunity is available; instead students failing the module will be offered the opportunity to retake the entire module. Notes (applying to all assessments) 1) Due to nature of the on­line mode of instruction work is not marked anonymously. (2) Students who fail the module have the opportunity to repeat the entire module. (3) The "Standard UoL Penalty" for late submission that applies is the "Standard UoL Penalty" agreed with respect to online programmes offered in collaboration with Laureate Online Education. (4) For group work assessments groups typically comprise 3 to 4 students. Both group and individual contributions are assessed and integrated to produce a final mark for each student. 

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
Explanation of Reading List:

The online programmes offered by the department of Computer Science in Collaboration with Laureate Online Education use online materials wherever possible including the online resources available within the University of Liverpool’s libraries. This module does not require a specific text book.