Prospectus

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Computer Science: Data Science

The Data Science: Computer Science specialisation is a two-year, full-time master’s programme.

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.

Core component Data Science (36 EC)

Important notes:

  • Students Data Science: Computer Science who started before 1 September 2021 are allowed to replace the Core courses Data Science with any six courses worth 36 EC from (1) the current core courses (as listed in Article 7.3 of the OER), and (2) Computational Statistics, Information Theoretic Data Mining, Information Retrieval, Introduction to Deep Learning, Linear & Generalized Linear Models and Linear Algebra, and Statistical Learning.

Below is a list of course list name changes, per academic year. In all cases, the course with the old name is considered equivalent to the new one.

As of 1 September 2021, the following course name changes came into effect:

  • Information Retrieval and Text Analytics was renamed to Information Retrieval.

As of 1 September 2020, the following course name changes came into effect:

  • Deep Learning and Neural Networks was renamed to Introduction to Deep Learning.
Course 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:

Below is a list of course list name changes, per academic year. In all cases, the course with the old name is considered equivalent to the new one.

As of 1 September 2023, the following course name changes came into effect:

  • Advanced Statistical Computing was renamed to Computational Statistics;

  • Missing Data and Causal Inference was renamed to Causal Inference; and

  • Multivariate and Multidimensional Data Analysis was renamed to Nonlinear (Mixed) Data Analysis.

As of 1 September 2022, the following course name changes came into effect:

  • Advances in Deep Learning was renamed to Seminar Advances in Deep Learning; and

  • Computational Molecular Biology was renamed to Biological and Biomedical Informatics.

As of 1 September 2020, the following course name changes came into effect:

  • Advances in Model Checking was renamed to Software Verification;

  • Introduction to Data Science (for Computer Scientists) was renamed to Data Science in Practice.

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

    • Computational Imaging and Tomography
    • Bio-Modeling
    • Cloud Computing
    • Information Theoretic Data Mining
    • Seminar Advanced Deep Reinforcement Learning
    • Seminar Advances in Deep Learning
    • Seminar Trustworthy Artificial Intelligence
    • Sports Data Science
  • Students who started before 1 September 2020 may choose to replace 18 EC of elective courses and seminars with an Introductory Research Project (18 EC).

  • 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.

Course EC Semester 1 Semester 2

Fall semester

Advanced Data Management for Data Analysis 6
Audio Processing and Indexing 6
Automated Machine Learning 6
Bayesian Optimization 6
Biological and Biomedical Informatics 6
Complex Networks (BM) 6
Computational Creativity 6
Computational Models and Semantics 6
Evolutionary Algorithms 6
Multimedia Systems 6
Quantum Algorithms 6
Seminar Advanced Deep Reinforcement Learning 6
Seminar Trustworthy Artificial Intelligence 6
Software Development and Product Management 6
System and Software Security 6
Urban Computing 6
Video Games for Research 6

Spring semester

Applied Quantum Algorithms 6
Bio-Modeling 6
Causal Inference for Computer Scientists 6
Cloud Computing 6
Computational Imaging and Tomography 6
Cryptographic Engineering 6
Distributed Systems 6
Educational Technologies 6
Embedded Systems and Software 6
Foundations of Software Testing 6
High Performance Computing 6
Image Analysis with Applications in Microscopy 6
Information Theoretic Data Mining 6
Modern Game AI Algorithms 6
Multicriteria Optimization and Decision Analysis 6
Proof Formalisation 6
Recommender Systems 6
Robotics 6
Seminar Advances in Deep Learning 6
Software Verification 6
Sports Data Science 6

Elective courses and seminars Data Science

Causal Inference I 3
Computational Statistics 3
Data Science in Practice 6
Data Visualization 6
Exploratory Data Analysis 6
Linear and Generalized Linear Models 6
Statistical Learning 6

Research project

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 Preparation

Career preparation at Leiden University

In addition to offering you a solid university education, Leiden University aims to prepare you as well as possible for the labour market, and in doing so contribute to the development of your employability. In this way, it will become easier for you to make the transition to the labour market, to remain employable in a dynamic labour market, in a (career) job that suits your own personal values, preferences and development.

'Employability' consists of the following aspects that you will develop within your study programme, among others:
1. Discipline-specific knowledge and skills
Knowledge and skills specific to your study programme.

2. Transferable skills
These are skills that are relevant to every student and that you can use in all kinds of jobs irrespective of your study programme, for example: researching, analysing, project-based working, generating solutions, digital skills, collaborating, oral communication, written communication, presenting, societal awareness, independent learning, resilience.

3. Self-reflection
This concerns self-reflection in the context of your (study) career, including reflecting on the choices you make as a student during your studies, what can you do with your knowledge and skills on the labour market?

In addition, reflecting on your own profile and your personal and professional development. Who are you, what can you do well, what do you find interesting, what suits you, what do you find important, what do you want to do?

4. Practical experience
Gaining practical experience through internships, work placements, projects, practical (social) assignments, which are integrated into an elective, minor or graduation assignment.

5. Labour market orientation
Gaining insight into the labour market, fields of work, jobs and career paths through, for example, guest speakers and alumni experiences from the work field, career events within the study programme, the use of the alumni mentor network, interviewing people from the work field, and shadowing/visiting companies in the context of a particular subject.

Employability in the curriculum of Computer Science

General
During the master Computer Science (CS), we want to provide you with the best possible preparation to enter the job market after graduation.

The master CS programme is strongly research-driven, which allows training of discipline-specific knowledge and skills, but also teaches students to work in a professional environment and fosters the development of an extensive set of transferable skills. This makes its graduates also well-prepared for a career outside academia.

In addition, in the Master Class, which runs over the entire second year of the programme, students are explicitly trained in several aspects of academic skills and are stimulated to make self-directed, conscious choices for their own professional development and preparation for a successful start of their career on the job market.

Activities contributing to employability

First and/or second year

  • All courses (discipline-specific knowledge and skills)

  • Science Skills Platform with a Personal and Professional development domain (transferable skills, self-reflection)

  • Mentorship and tutoring (transferable skills, self-reflection)

  • Visits to companies and organisations as part of courses (practical experience, labour market orientation)

  • ‘Inhousedays’ at companies via study association De Leidsche Flesch (labour market orientation)

  • Symposia and seminars by study association De Leidsche Flesch (labour market orientation)

  • Workshops and Career Colleges Science Career Service (transferable skills, self-reflection, labour market orientation)

Second year

  • Master’s Thesis Research Project (discipline-specific knowledge and skills, transferable skills, self-reflection, practical experience)
    o Optional: internship (practical experience, labour market orientation)

  • Master Class (transferable skills, self-reflection)
    o Including: career orientation (e.g., guest lectures) (labour market orientation)

Activities to prepare for the labour market co-curricular or outside the curriculum

Every year, various activities take place, within, alongside and outside of the CS study programme, which contribute to your preparation for the labour market, especially where it concerns orientation towards the work field/the labour market, (career) skills and self-reflection. These may be information meetings on decision moments within your programme, but also career workshops and events organised by the CS programme, the faculty Career Service, study association De Leidsche Flesch, or others, including:

  • Alice & Eve

  • Leiden University Study abroad festival

  • Annual Leiden University Diversity Symposium

Career Service, LU Career Zone and career workshops calendar

Faculty Career Service
The Career Service of your faculty offers information and advice on study (re)orientation and master's choice, (study) career planning, orientation on the labour market and job applications.

Leiden University Career Zone Leiden University Career Zone is the website for students and alumni of Leiden University to support their (study) career. You can find advice, information, (career) tests and tools in the area of (study) career planning, career possibilities with your study, job market orientation, job applications, the Alumni Mentor network, job portal, workshops and events and career services.

Workshops and events
On the course calendar you will find an overview of career and application workshops, organised by the Career services.