Prospectus

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Computer Science: Computer Science and Science Communication and Society

Master

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

Curriculum

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 infoscs@biology.leidenuniv.nl.

  • 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).

Course 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

Course 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