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Prospectus

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Computer Science: Data Science

The Data Science: Computer Science specialisation is a two-year, full-time master’s programme offered in cooperation with the Mathematical Institute.

Curriculum

Year 1&2

  1. The required Core component Data Science (36 EC)

  2. A selection of Specialisation courses and seminars (42 EC)

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 component Data Science (36 EC)

Important notes for students who started before 1 September 2020:

For former students Computer Science and Advanced Data Analytics with core component Advanced Data Analytics:
  • If you have previously completed Deep Learning and Neural Networks as core course, you may use that instead of Statistical Learning or Text Mining.

  • Similarly, if you have previously completed Seminar Distributed Data Mining, and/or have completed or will complete Complex Networks as core course, you may use one of those two courses instead of Statistical Learning or Text Mining.

For Data Science: Computer Science students who started before 1 September 2020:
  • You are free to replace any of the 2020-2021 core courses by previously mandatory core courses: Advanced Statistical Computing, Information Theoretic Data Mining, Deep Learning and Neural Networks, Introduction to Data Science for Computer Scientists, and Linear & Generalized Linear Models and Linear Algebra.

  • Note that Introduction to Data Science for Computer Scientists can only be included in your programme if you also include at least one of Advanced Statistical Computing, Linear & Generalized Linear Models and Linear Algebra, and Statistical Learning.

  • You can only take the new course Data Science in Practice if you have not previously passed Introduction to Data Science for Computer Scientists.

Course EC Semester 1 Semester 2

Core component Data Science (36 EC)

Year 1/2

Advances in Data Mining 6
Social Network Analysis for Computer Scientists 6
Text Mining 6
Information Retrieval and Text Analytics 6
Reinforcement Learning 6

Year 2

Statistical learning 6

Specialisation courses and seminars (42 EC)

Important notes for all 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.

  • Due to Corona restrictions the course Competitive Programming will not be given this semester.
    Students can choose an alternative course from their specialisation.

Course EC Semester 1 Semester 2

Fall semester

Advanced Data Management for Data Analysis 6
Advanced Statistical Computing 3
Automated Machine Learning 6
Computational Creativity 6
Complex Networks (BM) 6
Computational Models and Semantics 6
Computational Molecular Biology 6
Data Science in Practice 6
Distributed Data Processing Systems 6
Evolutionary Algorithms 6
Foundations of Software Testing 6
High Performance Computing I 6
Information Theoretic Data Mining 6
Introduction to Deep Learning 6
Linear & generalized linear models and linear algebra 9
Multimedia Systems 6
Quantum Algorithms 6
Seminar Advanced Deep Reinforcement Learning 6
Seminar Swarm-based Computation with Applications in Bioinformatics 6
Software Development and Product Management 6
Urban Computing 6

Spring semester

Advances in Deep Learning 6
Applied Quantum Algorithms 6
Audio Processing and Indexing 6
Better Science for Computer Scientists 3
Bio-Modeling 6
Cloud Computing 6
Competitive Programming 6
Concurrency and Causality 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
Multicriteria Optimization and Decision Analysis 6
Multimedia Information Retrieval 6
Multivariate analysis and multidimensional data analysis 6
Psychology of Programming 6
Quantum Computing 3
Robotics 6
Seminar Combinatorial Algorithms 6
Software Verification 6
Sports Data Science 6

Research components

Course EC Semester 1 Semester 2
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.

Career Perspective