NDAK14004U Web Science (WS)

Volume 2018/2019

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 realistic and sound 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 and search engine optimisation
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 in a proper format of a written report such 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.

Sample Literature

  • E. Siegel. “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”. Wiley. Latest edition.
  • D. Jannach, M. Zanker, A. Felferning, G. Friedrich. “Recommender Systems: an Introduction”. Cambridge University Press. Latest edition.
  • B. Croft, D. Metzler, T. Strohman. “Search Engines: Information Retrieval in Practice”. Pearson. Latest edition.
It is expected that students know how to program and have a working knowledge of Machine Learning.
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
The exam will consist of a portfolio of two assignments and each student will be required to anonymously evaluate fellow students’ work.
The part-examinations/​assignments must be individually approved. The final grade is based on an overall assessment.
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 two weeks before the re-exam; additionally 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 portfolio and the oral examination.

Criteria for exam assesment

See Learning Outcome.