Studiegids

nl en

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)

Vak EC Semester 1 Semester 2

Core component Data Science (36 EC)

Year 1/2

Advances in Data Mining 6
Deep Learning and Neural Networks 6
Information Theoretic Data Mining 6
Introduction to Data Science for Computer Scientists 6
Linear & generalized linear models and linear algebra 9

Year 2

Advanced Statistical Computing 3

Important note:

Students who started before 1 September 2019 are allowed to choose three of the following five courses to fullfil the requirements of the Core component Data Science:

  • Advances in Data Mining

  • Deep Learning and Neural Networks

  • Information Theoretic Data Mining

  • Multivariate analysis and multidimensional data analysis

  • Statistical Learning

Specialisation courses and seminars (42 EC)

Vak EC Semester 1 Semester 2
Advanced Data Management for Data Analysis 6
Automated Machine Learning 6
Better Science for Computer Scientists 3
Competitive Programming 6
Complex networks (BM) 6
Evolutionary Algorithms 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 Algorithms 6
Quantum Computing 3
Reinforcement Learning 6
Seminar Distributed Data Mining 6
Social Network Analysis for Computer Scientists 6
Social Signal Processing 6
Statistical learning 6
Text Mining 6
Urban Computing 6

Research components

Vak 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.