Module manager: Prof Eric Atwell
Email: e.s.atwell@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
COMP1012 Introduction to Programming or COMP1121 Databases and Programming Experience
This module is not approved as a discovery module
This module explores the data mining 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, algorithms and tools and learn how to evaluate their performance.
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.
On completion of this module, students should be able to
- understand the data mining process and its application in different domains such as text and web mining;
- understand the principles of data mining;
- compare a range of different techniques, algorithms and tools and evaluate their performance.
-demonstrate familiarity with some of the main application areas;
-demonstrate familiarity with data mining and text analytics tools.
Introduction to data mining terminology and components of the data mining process, text analytics, and SketchEngine; tools and techniques for data collection and data cleansing, use of machine learning classifiers for data classification, open-source and commercial data mining and text analytics resources and toolkits, CRISP-DM and WEKA; word meanings, text tagging, and scaling to big data; use of clustering and association tools for data mining, chatbots for university education; Machine Translation, Information Extraction, and Python tools for text analytics; web-based text analytics; case studies of current research and commercial applications in data mining and text analytics, BERT.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Class tests, exams and assessment | 2 | 2 | 4 |
| Lecture | 8 | 1 | 8 |
| Private study hours | 76 | ||
| Total Contact hours | 12 | ||
| Total hours (100hr per 10 credits) | 88 | ||
Coursework and labs.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Report | 60 |
| In-course Assessment | Test 1 | 20 |
| In-course Assessment | Test 2 | 20 |
| Total percentage (Assessment Coursework) | 100 | |
Resits will be assessed by coursework.
The reading list is available from the Library website
Last updated: 9/25/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team