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Computer Science: Computer Science and Advanced Data Analytics

Master

The Computer Science and Advanced Data Analytics specialisation consists of a full-time, two-year master’s programme (120 EC).

Curriculum

Year 1&2

  1. Choice of one of the following required Core Components (36 EC) in year 1:

Core Components:

  • Advanced Computing and Systems

  • Advanced Data Analytics

  • Artificial Intelligence

  • Foundations of Computing

  1. Choice of:

  • a selection of 24 EC of Specialisation courses and seminars in year 1 and 18 EC of Specialisation courses and seminars in year 2

  • a selection of 24 EC of Specialisation courses and seminars in year 1 and the Introductory Research Project (18 EC) in year 2

Year 2

  1. The Master’s Thesis Research Project (42 EC )

    (including Master Class, Written Master's Thesis and Master's Thesis Presentation)

See also

For more information on the specific requirements of this specialisation, see the appendix of the Course and Examination Regulations.

More information

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

Core components (36 EC)

Important notes:

  • Students who started before 1 September 2019, are not required to complete one of the Core components and can freely compose their own 78 EC study plan using the Specialisation courses and seminars listed in this Prospectus (see tab Specialisation courses and seminars (42)).
Vak EC Semester 1 Semester 2

Core component: Advanced Computing and Systems (36 EC)
Cloud Computing 6
Embedded Systems and Software 6
High Performance Computing I 6
High Performance Computing II 6
Multimedia Systems 6
Robotics 6
Secure Systems - CANCELLED 6

Core component: Advanced Data Analytics (36 EC)
Advances in Data Mining 6
Deep Learning and Neural Networks 6
Information Retrieval and Text Analytics 6
Reinforcement Learning 6
Social Network Analysis for Computer Scientists 6

Choice of one of the following two courses:
Seminar Distributed Data Mining 6
Complex Networks (BM) 6

Core component: Artificial Intelligence (36 EC)
Automated Machine Learning 6
Deep Learning and Neural Networks 6
Evolutionary Algorithms 6
Modern Game AI Algorithms 6
Multicriteria Optimization and Decision Analysis 6
Reinforcement Learning 6

Core component: Foundations of Computing (36 EC)
Advances in Model Checking 6
Coordination and Component Composition 6
Foundations of Software Testing 6
Quantum Algorithms 6
Seminar Combinatorial Algorithms 6

Choice of one of the following two courses:
Modern Game AI Algorithms 6
Reinforcement Learning 6

Specialisation courses and seminars (42 EC)

Important notes:

  • Students who started from 1 September 2019, may substitute 18 EC of Specialisation courses and seminars with an Introductory Research Project of 18 EC supervised by at least one LIACS scientific staff member. See tab Research Components.
    They are also advised to use the Programme Overview 2019-2020 (see Files) and add the recommended courses listed at the Core component of their choice in their Specialisation courses and seminars (42 EC) study plan.

  • Students who started before 1 September 2019), are not required to complete one of the Core components, but can freely compose their own 78 EC study plan using the Specialisation courses and seminars listed below. They also have the option of substituting the Introductory Research Project (18 EC) with 18 EC of Specialisation courses and seminars in year 2.

Vak EC Semester 1 Semester 2

Fall semester
Advanced Data Management for Data Analysis 6
Advances in Data Mining 6
Audio Processing and Indexing 6
Automated Machine Learning 6
Better Science for Computer Scientists 3
Cloud Computing 6
Complex Networks (BM) 6
Computational Molecular Biology 6
Coordination and Component Composition 6
Evolutionary Algorithms 6
Foundations of Software Testing 6
High Performance Computing I 6
Information Theoretic Data Mining 6
Multicriteria Optimization and Decision Analysis 6
Multimedia Systems 6
Quantum Algorithms 6
Seminar Swarm-based Computation with Applications in Bioinformatics 6
Social Network Analysis for Computer Scientists 6
Social Signal Processing 6
Software Development and Product Management 6
Text Mining 6

Spring semester
Advances in Model Checking 6
Applied Quantum Algorithms 6
Bio-Modeling 6
Competitive Programming 6
Deep Learning and Neural Networks 6
Embedded Systems and Software 6
High Performance Computing II 6
Image Analysis with Applications in Microscopy 6
Information Retrieval and Text Analytics 6
Modern Game AI Algorithms 6
Multimedia Information Retrieval 6
Psychology of Programming 6
Quantum Computing 3
Reinforcement Learning 6
Robotics 6
Secure Systems - CANCELLED 6
Seminar Combinatorial Algorithms 6
Seminar Distributed Data Mining 6
Urban Computing 6

Research components

Vak EC Semester 1 Semester 2
Introductory Research Project 18
Master Class 0
Master's Thesis Research Project (CS) 42

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.