2021/22 Undergraduate Module Catalogue
COMP2121 Data Mining
10 creditsClass Size: 200
Module manager: Prof Eric Atwell
Email: e.s.atwell@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2021/22
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 this module, students should be able to:
understand 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.
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 | 12 | 1.00 | 12.00 |
Class tests, exams and assessment | 2 | 2.00 | 4.00 |
Lecture | 8 | 1.00 | 8.00 |
Private study hours | 76.00 | ||
Total Contact hours | 24.00 | ||
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 |
In-course Assessment | Report | 60.00 |
In-course Assessment | Test 1 | 20.00 |
In-course Assessment | Test 2 | 20.00 |
Total percentage (Assessment Coursework) | 100.00 |
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 websiteLast updated: 15/03/2022 16:12:19
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