LIACS > For students > Master Computer Science > Curriculum Data Science

Curriculum CS with specialization Data Science

Curriculum Data Science
The  Data Science specialization of the master computer Science provides thorough knowledge and understanding of statistical and computational aspects of data analysis, including their application in Databases, Advances in Data Mining, Networks, Pattern Recognition, and Deep Learning.

Programme Overview
The programme of Data Science is a full-time 120 EC 2-year Master programme. It consists of a selection of  courses from a core programme in Computer Science (42 EC in the first year) and three mandatory specialisation courses (21 EC in the first year) from Statistical Science. In the second year it consists of two mandatory specialisation courses (9 EC) from Statistical Science, one specialization course (6 EC) and the Master Thesis (42 EC).

 1st year:
Core programme (42 EC)
Specialization courses (21 EC)

2nd year:
Core programme (6 EC)
Specialization courses (9 EC)
Master Thesis (42 EC)

First Year (in total 63 EC):
The first year of the specialization Data Science of the master Computer Science at Leiden University is structured as follows:

Core programme Group A (selection of three courses)
Advances in Data Mining (Fall)
Databases and Data Mining (Fall)
Evolutionary Algorithms (Fall)l
Complex Networks (Fall)
Muticriteria Optimization and Decision Analysis (Fall)
Social Network Analysis (Fall)

Core programme Group B (selection of four courses)
Bayesian Networks (Spring)
Multimedia Information Retrival (Spring)
Seminar Distributed Data Mining (Spring)
Neural Networks (Spring)
Information Retrieval and Text Analytics (Spring)

Specialization courses (Statistical Science, mandatory):
Introduction to Data Science (fall 2016, 6 EC)
Linear & Generalized Linear Models and Linear Algebra (fall 2016, 9 EC)
Multivariate and Multidimensional Data Analysis (spring 2017, 6 EC))

Second Year (in total 57 EC):
Specialization courses (Statistical Science, mandatory):
Advanced Statistical Computing (fall, 6 EC)
Statistical Learning Theory (fall 2017, 3 EC)

Core programme (selection of one course of Group A, see above)

Master Thesis (42 EC)
In the second year, you will work on innovative research and applications within the context of one project, namely the master thesis.

The master thesis project is research or application in a bigger context. Both projects are executed in collaboration with LIACS staff members, so you are strongly embedded into a group. Even if you are combining this with a company internship, close supervision by LIACS will be provided. A master thesis report and an oral presentation are required for the master thesis. Look here for recent master thesis projects.

Master thesis projects are diverse in their topics, and can focus on applications, algorithms and software as well as on foundations and theory. You can conduct them completely at LIACS, or through collaborations and internships with companies and organizations (supervision in any case is done by LIACS staff members).

The master class is a biweekly meeting of all master students working on their research project or master thesis. It is stimulating discussions, presentations, exchanging useful information, and supporting you during your second year.


Last edited on 06 Mar 2017 at 10:39