Module manager: Prof Eric Atwell
Email: e.s.atwell@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
| COMP5712M | Programming for Data Science |
| COMP2121 | Data Mining |
This module is not approved as a discovery module
Introduction to linguistic theory and terminology. Understand and use algorithms and resources for implementing .and evaluating text mining and analytics systems. Develop solutions using open-source and commercial toolkits. Consider the applications of data mining and text analytics through case studies in information retrieval and extraction.
On completion of this module, students should be able to ...
- understand theory and terminology of empirical modelling of natural language;
- understand and use algorithms, resources and techniques for implementing and evaluating text mining and analytics systems;
- demonstrate familiarity with some of the main text mining and analytics application areas;
- appreciate why unrestricted natural language processing is still a major research task.
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.
Introduction to linguistic theory and terminology.
Algorithms and techniques for computer-assisted text processing, focusing on applied and corpus-based problems such as spell checking, collocation and co-occurrence discovery and text analytics.
Open-source and commercial text mining and text analytics toolkits. Web-based natural language processing.
Case studies of current commercial applications in text mining, beyond English, Arabic data, machine translation, information retrieval, information extraction, chatbots and text classification.
Current research in text analytics.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Laboratory | 12 | 1 | 12 |
| Lecture | 8 | 1 | 8 |
| Private study hours | 130 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Attendance monitoring and in-lecture interaction.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Test 1 | 20 |
| In-course Assessment | Test 2 | 20 |
| In-course Assessment | Report | 60 |
| Total percentage (Assessment Coursework) | 100 | |
This module is reassessed by coursework only.
There is no reading list for this module
Last updated: 9/25/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team