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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.


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.

Core component Data Science (36 EC)

Important notes:

  • Students who started Data Science: Computer Science before 1 September 2021, are allowed to substitute one or more of their old mandatory courses, Advanced Statistical Computing, Deep Learning and Neural Networks, Information Theoretic Data Mining, Information Retrieval and Text Analysis, Linear & Generalized Linear Models and Linear Algebra, and Statistical Learning with current core courses.

  • Information Retrieval is not available to students who completed Information Retrieval and Text Analytics (Level 500, 6 EC) before 1 September 2021.

  • Students who completed either Neural Networks (Level 500, 6 EC) or Deep Learning and Neural Networks (Level 500, 6 EC) before 1 September 2020, substitute Introduction to Deep Learning by the one already taken.

Vak EC Semester 1 Semester 2

Core component Data Science (36 EC)

Advances in Data Mining 6
Introduction to Deep Learning 6
Social Network Analysis for Computer Scientists 6
Text Mining 6
Information Retrieval 6
Reinforcement Learning 6

Specialisation courses and seminars (42 EC)

A choice can be made from the Specialisation courses and seminars below during the first and second year of the programme for at least 42 EC.

Important notes:

  • Software Verification is not available to students who completed Advances in Model Checking (Level 500, 6 EC) before 1 September 2020.

  • Seminar Swarm-based Computation with Applications in Bioinformatics is available in the Spring of odd years only (2023, 2025, …)

  • The following courses have limited availability. Details on the admission procedure can be found in the course descriptions:

    • Advances in Deep Learning
    • Competitive Programming
    • Multimedia Information Retrieval
    • Seminar Advanced Deep Reinforcement Learning
    • Sports Data Science
  • Students who started before 1 September 2020 are allowed to do an 18 EC Introductory Research Project and at least 24 EC of Specialisation courses and seminars instead of at least 42 EC of Specialisation courses and seminars.

  • Data Science in Practice is not available to students who completed either Introduction to Data Science (Level 400, 6 EC) or Introduction to Data Science for Computer Scientists (Level 400, 6 EC) before 1 September 2020.

Vak EC Semester 1 Semester 2

Fall semester

Advanced Data Management for Data Analysis 6
Advanced Statistical Computing 3
Automated Machine Learning 6
Better Science for Computer Scientists 3
Complex Networks (BM) 6
Computational Creativity 6
Computational Models and Semantics 6
Computational Molecular Biology 6
Data Science in Practice 6
Evolutionary Algorithms 6
Distributed Data Processing Systems 6
Foundations of Software Testing 6
High Performance Computing I 6
Introduction to Machine Learning 6
Information Theoretic Data Mining 6
Linear & generalized linear models and linear algebra 9
Multimedia Systems 6
Quantum Algorithms 6
Seminar Advanced Deep Reinforcement Learning 6
Software Development and Product Management 6
Statistical learning 6
System and Software Security 6
Urban Computing 6
Video Games for Research 6

Spring semester

Advances in Deep Learning 6
Applied Quantum Algorithms 6
Audio Processing and Indexing 6
Bio-Modeling 6
Cloud Computing 6
Competitive Programming 6
Computational Imaging and Tomography 6
Cryptographic Engineering 6
Embedded Systems and Software 6
High Performance Computing II 6
Image Analysis with Applications in Microscopy 6
Modern Game AI Algorithms 6
Multivariate analysis and multidimensional data analysis 6
Multicriteria Optimization and Decision Analysis 6
Multimedia Information Retrieval 6
Psychology of Programming 6
Quantum Computing 3
Robotics 6
Seminar Combinatorial Algorithms 6
Software Verification 6
Sports Data Science 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.

Career Orientation

During the Master Computer Science we want to provide you with the best possible preparation for the job market. In addition to knowledge, it is important that you develop skills, gain practical experience, orientate on positions & careers, and reflect on your own profile and development. In addition to substantive knowledge, it is also important to be aware of the so-called transferable skills that you develop outside and during your education. These are, for example, your cognitive skills such as critical thinking and communication. Altogether, this contributes to your development as a professional and offers good preparation for the labour market.

The MSc Computer Science in Leiden is a two-year program that cover Computer Science from its foundations to its most recent developments. Taught by leading researchers in their fields, the program is strongly research-driven. By learning the scientific state of the art, however, you are also prepared for a career outside science. You can choose from a wide range of course topics and electives to fit your own specific interests. Also, our close international collaborations with industry and other scientific disciplines enable you to conduct research inspired by applications of Computer Science. You gain academic skills by conducting small projects for individual courses, and ultimately combine everything you’ve learned in a large (42 EC) Master’s Thesis Research Project.

Our program offers seven challenging specialisations, so that you can experience the scientific and societal impact of Computer Science in areas such as Artificial Intelligence and Machine Learning, Bioinformatics, Computing and Systems, Data Science, and Foundations of Computing.

At various times during your studies, questions about this subject may arise, such as: How can you use the knowledge and skills you gain within and outside your study program in the labor market? Which direction do you choose within your study and why? What are you already able to do, and what skills do you still want to learn? How do you translate the courses you choose into something you would like to do later?

You may have already discussed this with the study advisor, mentor, tutor, the Science Career Service, fellow students or made use of the Leiden University Career Zone. All kinds of activities are organized where you get the chance to orientate yourself on the job market and gives opportunities to reflect on your own development, possibilities and (study) career profile as well. Central to this are the questions: "What are my capabilities?", "What do I want?" and "How do I achieve my goals?".
In the prospectus, learning objectives have been formulated for each subject, the purpose of which is to inform you which components are covered in the development of your (study) career profile and preparation for the labour market. Various activities are also organized that help you in making all kinds of career choices and to develop skills. An overview of activities is shown below.


First and/or second year

Second year

  • Research skills as part of the Master’s Thesis Research Project

  • Career Orientation as part of the Master Class

  • Alumni lectures as part of the Master Class

  • The possibility to submit a scientific paper to a subject-specific conference, attend the conference, and present there

Science Career Service

Science Career Service, one of the utilities of the Science faculty, offers information and advice on study (re)orientation, career planning and personal professional profile as well as preparation for the job market, such as job applications. Facilities provided to students include online information, walk-in consultations, workshops and individual counselling sessions. In addition, Science Career Service offers expertise and support to programmes that want to strengthen the connection between their curriculum and the job market. This can vary from providing specific guest lectures/workshops to advising on integrating career orientation programmes into the curriculum.

LU Career Zone

The Leiden University Career Zone is the website for students and alumni of Leiden University to support their (study) career planning. You will find advice, information, video recordings of webinars and tools such as professional tests to get an idea of your personal profile. You can also explore positions and sectors, you will find tips about CV, job application, LinkedIn and there is a vacancy platform that you can make use of.


Leiden University likes to prepare students and young alumni well for the job market. For this we use the knowledge and experience of Leiden alumni. To bring students and young alumni with questions about their careers into contact with experienced alumni, Leiden University has established the Mentor Network. Students and young alumni can register for free.


Do you have questions about your (study) career choices and has the above information not been able to help you further? Your study adviser is always available to discuss your plans and concerns.