Web Science (M) COMPSCI5078
- Academic Session: 2018-19
- School: School of Computing Science
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Available to Erasmus Students: No
Web Science is the study of the World Wide Web (WWW), its components, facets and characteristics and the impact it has on both society and technology. The World Wide Web changed the way in which we create information, communicate and interact. New models of social networks (LinkedIn, Facebook, etc.) create opportunities, which were not available before. Exploiting such data and networks for the benefit of individuals and organizations has become a key in our knowledge society.
3 hours per week
Requirements of Entry
Web Science (H)
Coursework 20%, Examination 80%
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? No
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
The nature of the coursework is such that it takes a significant number of days to produce it and this effort is infeasible for supporting the re-doing of such coursework over the summer.
The objective of this course is to introduce students to the field of web science and critically examine methodologies and techniques used in the field.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Skills to analyse and implement technical solutions on social web applications
2. Describe the techniques employed in developing advertising models on the web
3. Describe the techniques needed to analyse social networks
4. Ability to understand and rationalise privacy threats and mitigation strategies in online communities
5. Describe deep learning models and their usage on social systems
6. Describe methodologies to conduct large scale data analysis to analyse user behaviour on the web, to predict user demographics and purchase behaviours
7. Describe sentiment and emotion extraction techniques and employ them
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.