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Computer Science: Computer Science and Science Communication and Society

The Computer Science and Science Communication and Society (SCS) specialisation offers students the possibility to combine computer science and science communication.


The two-year, full-time programme (120 EC) consists of:

  • the SCS component with a minimum of 40 EC and a maximum of 60 EC;

  • a maximum of 20 EC of electives chosen from the Computer Science or the SCS programme.

  • at least 30 EC of level-500 Specialisation courses and seminars in Computer Science to be selected in correspondence with the topic of the Master’s Thesis Research Project;

  • a Master's Thesis Research Project (30 EC) in one of the LIACS research groups.

See also

More information

  • If you are interested in the SCS specialisation or any of the courses, please request an application form at

  • For specific questions about programme content, curriculum choices and/or study planning, please contact the Computer Science study advisor/education coordinator or the SCS programme coordinator dr. Anne Land.

Specialisation courses and seminars Computer Science

Important notes for students who started before 1 September 2020:

  • You can only take the new course Introduction to Deep Learning if you have not previously passed Neural Networks or Deep Learning and Neural Networks.

  • You can only take the new course Software Verification if you have not previously passed Advances in Model Checking.

Other announcements:

  • Due to Covid-19 the following courses are cancelled:

    • Better Science for Computer Scientists (3 EC)
    • Competitive Programming (6 EC)
    • Information Retrieval and Text Analytics (6 EC)
    • Information Theoretic Data Mining (6 EC)
  • Students can choose an alternative course from the Computer Science and SCS list of specialisation courses and seminars (see below).

Vak EC Semester 1 Semester 2

Fall semester

Advanced Data Management for Data Analysis 6
Advances in Data Mining 6
Automated Machine Learning 6
Better Science for Computer Scientists - CANCELLED 3
Computational Creativity 6
Complex Networks (BM) 6
Computational Models and Semantics 6
Computational Molecular Biology 6
Distributed Data Processing Systems 6
Evolutionary Algorithms 6
Foundations of Software Testing 6
High Performance Computing I 6
Information Theoretic Data Mining - CANCELLED 6
Introduction to Deep Learning 6
Introduction to Machine Learning 6
Multimedia Systems 6
Quantum Algorithms 6
Seminar Advanced Deep Reinforcement Learning 6
Seminar Swarm-based Computation with Applications in Bioinformatics 6
Social Network Analysis for Computer Scientists 6
Software Development and Product Management 6
Text Mining 6
Urban Computing 6

Spring semester

Advances in Deep Learning 6
Applied Quantum Algorithms 6
Audio Processing and Indexing 6
Bio-Modeling 6
Competitive Programming - CANCELLED 6
Concurrency and Causality 6
Cloud Computing 6
Embedded Systems and Software 6
High Performance Computing II 6
Image Analysis with Applications in Microscopy 6
Information Retrieval and Text Analytics - CANCELLED 6
Modern Game AI Algorithms 6
Multicriteria Optimization and Decision Analysis 6
Multimedia Information Retrieval 6
Psychology of Programming 6
Quantum Computing 3
Reinforcement Learning 6
Robotics 6
Seminar Combinatorial Algorithms 6
Software Verification 6
Sports Data Science 6

Research components Computer Science

Vak EC Semester 1 Semester 2
Master Class 0
Master's Thesis Research Project (CS & SCS/EDU) 30

Science Communication & Society component

For detailed information, please visit:

Course levels

  • Level 100
    Introductory course, builds upon the level of the final pre-university education examination.
    Characteristics: teaching based on material in textbook or syllabus, pedagogically structured, with
    practice material and mock examinations; supervised workgroups; emphasis on study material and
    examples in lectures.

  • Level 200
    Course of an introductory nature, no specific prior knowledge but experience of independent
    study expected.
    Characteristics: textbooks or other study material of a more or less introductory nature; lectures, e.g. in
    the form of capita selecta; independent study of the material is expected.

  • Level 300
    Advanced course (entry requirement level 100 or 200).
    Characteristics: textbooks that have not necessarily been written for educational purposes; independent
    study of the examination material; in examinations independent application of the study material to
    new problems.

  • Level 400
    Specialised course (entry requirement level 200 or 300).
    Characteristics: alongside a textbook, use of specialist literature (scientific articles); assessment in the
    form of limited research, a lecture or a written paper. Courses at this level can, to a certain extent, also
    be on the master’s curriculum.

  • Level 500 Course with an academic focus (entry requirement: the student has been admitted to a
    master’s programme; preparatory course at level 300 or 400 has been followed).
    Characteristics: study of advanced specialised scientific literature intended for researchers; focus of the
    examination is solving a problem in a lecture and/or paper or own research, following independent
    critical assessment of the material.

  • Level 600
    Very specialised course (entry requirement level 400 or 500)
    Characteristics: current scientific articles; latest scientific developments; independent contribution (dissertation research) dealing with an as yet unsolved problem, with verbal presentation.

The classification is based on the Framework Document Leiden Register of Study Programmes.

Career Perspective