NDAK14004U Web Science

Volume 2014/2015

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 properties (HTTP, cookies, caching, content delivery networks)
  • Recommender systems
  • Online advertising and auctions
  • Collective intelligence and crowdsourcing
  • Opinion and data mining
  • Data analytics and search engine optimisation 
Learning Outcome


  • Identify and explain the basic architecture of the Web
  • Identify and explain the basic models and techniques of mining and analysing information on the Web
  • Identify and explain different criteria for eCommerce 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 e-commerce applications, and 
  • Designing and calibrating solutions appropriate for expected usage loads



  • Explain the basic Web principles and properties to both laymen and specialists
  • Use standard procedures and practices when designing or implementing Web mining and e-commerce 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.

The literature will be listed in Absalon.

Sample Literature

  • E. Siegel. 2013. “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”. Wiley.
  • D. Jannach, M. Zanker, A. Felferning, G. Friedrich. 2011. “Recommender Systems: an Introduction”. Cambridge University Press.

B. Croft, D. Metzler, T. Strohman. 2010. “Search Engines: Information Retrieval in Practice”. Pearson.

The course will use a combination of lectures, lab sessions, class discussions and student presentations. Where possible, relevant guest lecturers will be involved. Students are expected to come to lectures and lab sessions prepared and to be active in class, as well as show initiative in their assignments.
  • Category
  • Hours
  • Lectures
  • 28
  • Practical exercises
  • 57
  • Preparation
  • 14
  • Project work
  • 50
  • Theory exercises
  • 57
  • Total
  • 206
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.
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal assessors.
Participation in the re-exam requires that students hand in two new assignments. Each student will anonymously evaluate fellow students’ work.
Criteria for exam assesment

Related to the learning outcomes