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 2019-20 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 the implementation of Data Mining algorithms and how to evaluate their performance. To be able to match Data Mining concepts to organisational requirements.


Learning Outcomes

(LO1) A deep understanding of the end-to-end data mining process, and how it can be used to meet organisational needs.

(LO2) 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.

(LO3) A wide-ranging and systematic knowledge of a range of Data Mining tools and techniques.

(LO4) An ability to critically evaluate Data Mining techniques in the context of a variety of end goals.

(LO5) A practical ability to implement Data Mining algorithms in software and consequently a deep understanding of the operation of such algorithms.

(S1) Organisational skills

(S2) Communication skills

(S3) IT skills

(S4) Communication and collaboration online participating in digital networks for learning and research

(S5) Learning skills online studying and learning effectively in technology-rich environments, formal and informal

Syllabus
 

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, d ealing 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.   We ek 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

Teaching Method 1 - Online Learning
Description: Weekly seminar supported by asynchronous discussion in a virtual classroom environment facilitated by an online instructor.
Attendance Recorded: Yes
Notes: 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

 60
Timetable (if known)              
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
Group project phase 1 - proposal Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 6  One week / 1,500-1,7    10       
Group project phase 2 - data mining Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Weeks 7  One week / 1,500-1,7    14       
Group project phase 3 - analysis of results Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 8  One week / Video rec    14       
eight discussion questions Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :whole session  Weekly Discussion Qu    32       
Report on the R programming language and RStudio Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 1  One week/ Two page r    6       
Report on data cleaning Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 2  One week/ Two page r    6       
Decision tree programming exercise Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 3  One week/ Two page r    6       
Association rule mining exercise Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 4  One week/ Two page r    6       
Data clustering exercise Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 5  One week/ Two page r    6       

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