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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.
Indicative Module Content:The course aims to provide students with a firm understanding of the key areas of Text Analytics research, and give a flavour of the application domains in which it has been applied.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Practical | 12 |
Specified Learning Activities | 144 |
Total | 180 |
This course is designed to be taken by students with no prior programming experience.
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 | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Practicals | Throughout the Trimester | n/a | Graded | No | 50 |
Examination: Written essay-question(s) type of exam. | 2 hour End of Trimester Exam | No | Graded | Yes | 50 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
Students are given feedback on practicals.