NDAK14004U Web Science (WS)

Volume 2019/2020

MSc Programme in Computer Science


The course objective is to offer an advanced introduction into Web Science. The goal is to understand and model the Web as a structure and to design and evaluate some of the major technologies operating on the Web (see below). Through applied projects, the course aims to stimulate and prepare students for their MSc thesis work.


Content in detail:

  • The World Wide Web as a network and its challenges
  • Recommender systems
  • Collective intelligence and crowdsourcing
  • Opinion and data mining
  • Data analytics
Learning Outcome


  • The basic models and techniques of mining information on the Web
  • Different criteria for analytics applications



Students should be able to transfer the above knowledge to real-world tasks by:

  • Designing appropriate strategies for crawling, mining and analysing Web information
  • Planning and carrying out appropriate evaluations
  • Diagnosing problems in standard Web mining and analytics applications
  • Designing and calibrating solutions appropriate for expected usage loads



  • Explain basic Web principles and properties to both laymen and specialists
  • Use standard procedures and practices when designing or implementing Web mining and analytics solutions
  • Present evaluation analyses and results so that a technically qualified person can follow and obtain similar findings


The literature consists of seminal research and review articles from central journals and selected papers from peer-reviewed conferences, textbooks and research reports. This is supplemented with practical experience gained through lab sessions.

See Absalon for a list of literature.

It is expected that students know how to program and have a working knowledge of Machine Learning that can be obtained by any undergraduate course in Machine Learning.

Academic qualifications equivalent to a BSc degree is recommended.
The course will use a combination of lectures, lab sessions, class discussions and student presentations. Where possible, relevant guest lecturers will be involved.
  • Category
  • Hours
  • Lectures
  • 28
  • Practical exercises
  • 57
  • Preparation
  • 14
  • Project work
  • 50
  • Theory exercises
  • 57
  • Total
  • 206
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written assignment
Oral defence
The exam will consist of two parts; a written report and an oral presentation. The oral presentation will be a 20-minute oral examination without preparation. The grade will be based on an overall assessment.
Exam registration requirements

80% atttendance in the lectures and labs is required in order to qualify for the exam.

All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.

Students will need to submit new projects/assignments no later than three weeks before the re-exam; and a 20-minute oral examination without preparation will be administered covering the full course syllabus.

The part-examinations/assignments must be individually approved. The final grade is based on an overall assessment of the new projects/assignments and the oral examination.

Criteria for exam assesment

See Learning Outcome.