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Curricular information is subject to change
At the end of the course students should have a thorough knowledge of the main techniques used in text analytics, some familiarity with the software used to implement these techniques and a knowledge of some of the main application areas. Students should have developed a knowledge of the main application areas in which these techniques prove useful and know how to evaluate new text-analytics systems.
| Student Effort Type | Hours |
|---|---|
| Lectures | 24 |
| Practical | 12 |
| Specified Learning Activities | 140 |
| Total | 176 |
This course is designed to be taken by students with no prior programming experience.
Learning Exclusions:None
Learning Recommendations:This course is designed to be taken by students with no prior programming experience (though it will involve coursework using Python and R). Having said this, prior experience of, at least, one programming language will clearly be a boon. Neither does the course assume a previous qualification in Computer Science.
It is very much a from-scratch introduction to text analytics.
| Description | % of Final Grade | Timing |
|---|---|---|
| Continuous Assessment: Practical work and projects | 60 |
Throughout the Trimester |
| Examination: Written Exam | 40 |
2 hour End of Trimester Exam |
Compensation
This module is not passable by compensation
Resit Opportunities
In-semester assessment
Remediation
If you fail this module you may repeat or substitute where permissible.
| Name | Role |
|---|---|
| Dr Claudia Orellana Rodriguez | Tutor |