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 an Elective
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