Course Catalogue

Web Science (H) COMPSCI4077

  • Academic Session: 2026-27
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

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.

Timetable

3 hours per week.

Requirements of Entry

None

Excluded Courses

Web Science (M)

Co-requisites

None

Assessment

Coursework 20%, Examination 80%

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? No

It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.

[No exceptions]

Course Aims

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. Able to apply generative AI solutions to web applications.

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 methodologies to conduct large-scale data analysis to analyse user behaviour on the web.

6. Develop prediction models for structured and unstructured textual data.

Minimum Requirement for Award of Credits

No exceptions