2019/20 Undergraduate Module Catalogue

COMP2121 Data Mining

10 creditsClass Size: 150

Module manager: Dr Eric Atwell
Email: e.s.atwell@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2019/20

Pre-requisites

COMP1121

Databases

This module is not approved as a discovery module

Module summary

This module explores the knowledge discovery process and its application in different domains such as text and web mining. You will learn the principles of data mining; compare a range of different techniques and algorithms and learn how to evaluate their performance.

Objectives

On completion of this module, students should be able to:
-Identify all of the data, information, and knowledge elements, for a computational science application.
-understand the components of the knowledge discovery process
-understand and use algorithms, resources and techniques for implementing data mining systems;
-understand techniques for evaluating different methodologies
-demonstrate familiarity with some of the main application areas;
-demonstrate familiarity with data mining and text analytics tools.

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-demonstrate a broad understanding of the concepts, information, practical competencies and techniques which are standard features in a range of aspects of the discipline;
-apply generic and subject specific intellectual qualities to standard situations outside the context in which they were originally studied;
-appreciate and employ the main methods of enquiry in the subject and critically evaluate the appropriateness of different methods of enquiry;
-use a range of techniques to initiate and undertake the analysis of data and information;
-adjust to professional and disciplinary boundaries;
-effectively communicate information, arguments and analysis in a variety of forms;

Syllabus

Introduction to data mining terminology and components of the data mining process. Data warehouses; Tools and techniques for data cleansing and aggregation. Use of machine learning classifiers for data classification. Meta data. Use of clustering and association tools for data mining. Open-source and commercial text mining and text analytics toolkits. Web-based text analytics. Case studies of current commercial applications.

Teaching methods

Delivery type	Number	Length hours	Student hours
Laboratory	4	1.00	4.00
Class tests, exams and assessment	1	2.00	2.00
Lecture	20	1.00	20.00
Private study hours			74.00
Total Contact hours			26.00
Total hours (100hr per 10 credits)			100.00

Opportunities for Formative Feedback

Coursework and labs.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Design	10.00
Assignment	Lab based	10.00
Total percentage (Assessment Coursework)		20.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr 00 mins	80.00
Total percentage (Assessment Exams)		80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 05/11/2019 08:50:06

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