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 DATA MINING
Code CKIT527
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
  • To facilitate a comprehensive understanding of how knowledge can be extracted from data using the concept of Data Mining.
  • To provide a systematic understanding of the end-to-end data mining process including the data preprocessing requirements.
  • To develop a critical understanding of the tools and techniques of Data Mining and the ability deploy these tools and techniques.
  • To allow students to gain experience of th e implementation of Data Mining algorithms and how to evaluate their performance.
  • To be able to match Data Mining concepts to organisational requirements.

Learning Outcomes

A deep understanding of the end-to-end data mining process, and how it can be used to meet organisational needs.

A comprehensive understanding of the pre-processing and transformation tasks that need to be applied to data so that Data Mining tools and techniques can be deployed.

A wide-ranging and systematic knowledge of a range of Data Mining tools and techniques.

An ability to critically evaluate Data Mining techniques in the context of a variety of end goals.

A practical ability to implement Data Mining algorithms in software and consequently a deep understanding of the operation of such algorithms.


Syllabus
1 

Week 1
Data Mining concepts, the end-to-end Data Mining process, different types of data that can be used in this process, problems that may be encountered and methods for dealing with these problems.
 
Week 2
Data pre-processing for data mining, dealing with noisy, incomplete, and inconsistent data.
 
Week 3
Decision Tree extraction algorithms and their implementations coupled with a critical analysis of the advantages and disadvantages of a range of Decision Tree induction techniques.
 
Week 4
Association pattern mining algorithms and their implementations using the Apriori algorithm.

 

Week 5
Cluster analysis algorithms, including the k-means algorithm, and a critical analysis of similarities and differences between classification and clustering techniques.

 

Week 6
Deep learning, covering neural networks, their structure, types, and applications.
 
Week 7
Mining Data Streams and time series.
 
Week 8
Mining text, web, and social network data

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  32  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  One week/ Two page r  Week 1  6  No reassessment opportunity  Standard UoL penalty applies  Report on the R programming language and RStudio 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  One week/ Two page r  Week 2  6  No reassessment opportunity  Standard UoL penalty applies  Report on data cleaning 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  One week/ Two page r  Week 3  6  No reassessment opportunity  Standard UoL penalty applies  Decision tree programming exercise 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  One week/ Two page r  Week 4  6  No reassessment opportunity  Standard UoL penalty applies  Association rule mining exercise 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  One week/ Two page r  Week 5  6  No reassessment opportunity  Standard UoL penalty applies  Data clustering exercise 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  One week / 1,500-1,7  Week 6  10  No reassessment opportunity  Standard UoL penalty applies  Group project phase 1 - proposal 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  One week / 1,500-1,7  Weeks 7  14  No reassessment opportunity  Standard UoL penalty applies  Group project phase 2 - data mining 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  One week / Video rec  Week 8   14  No reassessment opportunity  Standard UoL penalty applies  Group project phase 3 - analysis of results 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 in the University of Liverpool’s libraries. This module does not require a specific text book.