Master's

For a quick summary see our brochure - available in light and dark versions.

Our Master of Computer and Data Science is a highly-selective program track for students with a strong background in mathematics, computer science, and/or applied statistics. The track features a demanding curriculum that focuses on equipping the students with the theoretical foundations and practical skills that they need to become leading data science professionals, researchers, or teachers of data science.

The program is part of the Faculty of Computer and Information Science (University of Ljubljana) second-cycle master’s study program Computer and Information Science. Upon completion, graduates earn the title Master of Computer and Data Science.

It is designed for 2-year full-time study, divided into four semesters of 30 ECTS each, for a total of 120 ECTS. Enrollment is open to anyone that has completed a first-cycle program or equivalent study program. However, admissions are very competitive.

CURRICULUM

Da t a S c i e n c e E l e c t ive Cor e ( L i s t A ) Da t a S c i e n c e E l e c t ive ( L i s t B ) Da t a S c i e n c e E l e c t ive Cor e ( L i s t A ) Mathematics I Principles of Uncertainty I n t rod u c t ion t o Da t a S c i e n c e Da t a S c i e n c e E l e c t ive ( L i s t B ) Da t a S c i e n c e E l e c t ive ( L i s t B ) M a s t e r‘ s Thesis G e n e r a l U n iversi t y E l e c t ive Mathematics II Machine Learning for Data Science I Pro j e c t Co u r s e Computer Science Module (4 courses) 1 s t y e a r 2nd year

The curriculum is divided up into 4 semesters, 2 semesters each year.

The first year features 6 compulsory Core courses. Additionally, the student elects one 4-course Module from the Computer science Master’s program, for a total of 10 courses in the first year.

In the second year each students selects 5 Data Science elective courses. At least 2 of them must be Elective Core courses while the rest can be selected from the broader list of Data Science elective courses. Each student also has one General elective course, which can be selected from the courses offered by the Faculty of computer and information science or even other Faculties of the University of Ljubljana.

A substantial part of the second year - the ECTS equivalent of 4 courses - is dedicated to the Master’s thesis. Working on the Thesis is expected to be the culmination of the student’s effort and knowledge. With successfully defending the thesis, the student completes the Master’s in Computer and Data Science.

For more information and some background read our Blog post on the overall structure of the Master’s program.

Core courses

about-image
Introduction to Data Science

Learn the techniques and tools for working with data, hands-on, with guest lecturers from industry and academia: getting, storing, transforming, exploring, and analyzing data, including reproducibiliy, ethics, and privacy.

about-image
Mathematics I

Mathematics are the basic building blocks of modern quantiative science. The first mathematical course covers mathematical analysis/calculus, linear algebra, and optimization at an engineering graduate level.

about-image
Mathematics II

The second mathematical course covers advanced topics that are important for modern statistics and machine learning, such as non-linear and stochastic optimization, functional analysis, and multi-linear algebra.

about-image
Principles of Uncertainty

The course introduces the student to probability, the language of uncertainty, and statistic, the methods and techniques for reasoning with uncertainty.

about-image
Machine Learning for Data Science I

This course will equip the student with the methodological foundations and a broad set of tools for tackling prediction, forecasting, pattern recognition, and other typical analytical tasks.

about-image
Project Course

Students apply what they have learned so far to solving a problem from research or industry. Working in teams is encouraged and each team receives expert guidance.

about-image
Master's Thesis

The Master's thesis is a comprehensive individual project under the supervision of a faculty member. By successfully submiting and defending the thesis, the student completes the Master's program.

List A: Data Science elective core

about-image
Big Data

Data management, cloud storage, non-relational databases, distributed analytics, and other things you should know when the data are just too big.

about-image
Deep Learning

The family of machine learning methods behind the breakthroughs in areas such as autonomous driving, computer vision, and NLP.

about-image
Machine Learning for Data Science II

Selected advanced topics, recent developments, and preparation for further study and research in Machine learning.

about-image
Bayesian Statistics

Learn probabilistic programming, how to build and apply statistical models, and provide statistical support to researchers and professionals.

List B: Data Science elective courses

More elective courses coming soon!

about-image
Big Data

Data management, cloud storage, non-relational databases, distributed analytics, and other things you should know when the data are just too big.

about-image
Deep Learning

The family of machine learning methods behind the breakthroughs in areas such as autonomous driving, computer vision, and NLP.

about-image
Machine Learning for Data Science II

Selected advanced topics, recent developments, and preparation for further study and research in Machine learning.

about-image
Bayesian Statistics

Learn probabilistic programming, how to build and apply statistical models, and provide statistical support to researchers and professionals.

about-image
Advanced Topics in Computer Vision

about-image
Artificial Intelligence

about-image
Biomedical signal and image processing [lectures in Slovenian]

about-image
High Performance Computing

about-image
Introduction to Bioinformatics

about-image
Image-Based Biometry

about-image
Machine Learning [lectures in Slovenian]

about-image
Natural Language Processing

about-image
Network Analysis

about-image
Web Information Extraction and Retrieval [lectures in Slovenian]

ADMISSIONS

Prerequisites

The Data Science Master’s assumes that the enrolling student is knowledgeable in calculus, linear algebra, probability, algorithms and data structures, and programming skills in a general-purpose programming language. We anticipate that most students will have gaps in at least one of these areas, so this will not preclude them from being accepted. However, we expect students to put in the required extra work and catch up during the 1st year of the Master’s or prior to enrolling by consulting the following references:

  • Magnus L. Hetland: Beginning Python, 2nd Edition, Apress, 2008.
  • Cormen et al.: Introduction to Algorithms, 3rd Edition, MIT Press, 2009 [Chapters 2, 3.1, 4.1, 7.1, 7.2, 10.1, 10.2, 11.2, 12.1-12.3, 15.1, 16.1, 22.1-22.4., 23.1, 23.2]
  • James Stewart: Calculus: Early Transcendentals, 8th Edition, Cengage Learning, 2017.
  • Gilbert Strang: Introduction to Linear Algebra, 5th Edition, 2016. [Chapters 1-6]
  • Sheldon Ross: A First Course in Probability, 9th Edition, Pearson Education India, 2013.
Pre-enrollment

With pre-enrollment we will identify outstanding applicants and will guide them through the enrollment process. We especially recommend pre-enrollment to non-EU students and students interested in participating in our Data Science Scholarly Projects.

To pre-enrol, submit the required documentation (see Submission checklist below) to datascience@fri.uni-lj.si before the pre-enrollment deadline. Submissions will be reviewed and selected students will be invited to a follow-up interview. The interview will include subject-matter questions from areas that are formal prerequisites for enrollment (see Prerequisites).

Successful pre-enrollment does not guarantee that the student will be accepted to the program, as this is subject to meeting the formal enrollment criteria (e.g., graduation from the undergraduate studies). Pre-enrollment is not mandatory and unsuccessful pre-enrollment does not preclude the student from applying to our Master’s program. However, if the number of applicants exceeds the number of available places, selection of students that will not be selected in the pre-enrollment process will be based on a written entrance examination.

Pre-enrollment submission checklist:

  • Completed and signed pre-enrollment form (link).
  • Structured CV (education, work experience, technical skills, language proficiency, bibliography, other relevant achievements).
  • Contact information of up to three persons (name, institution, email) that we can contact and ask for applicant’s professional or academic reference. Preferably these are applicant’s past advisors, mentors, or employers.
  • Academic transcript that includes all university-level courses completed so far and grades received. Non-Univesity of Ljubljana students should also provide a description of the grading system.
  • Motivational letter (1 page, 500 words maximum).

All submitted documents must be in English and in pdf format; incomplete submissions may be rejected without review.

Pre-enrollment deadline: 30. 3. 2019 (23:59 CET)

Pre-enrollment notification: 15. 4. 2019

Follow-up interviews and Scholarly Project selection: May 2019

** 201920 pre-enrollment process has been completed. **

Enrollment

The enrollment process has started. Students must enroll through the eVŠ portal. Please refer to the FRI website pages for the Data science track (link) and the parent Computer and information science Master’s (link).

Make sure you pay attention to enrollment deadlines and entry exam dates.

SCHOLARLY PROJECTS

We want to give our students the opportunity to focus entirely on their studies and at the same time gain invaluable hands-on experience.

Each year, we will offer several Scholarly Projects. A DataScience@UL-FRI Scholarly Project is a 2-year project where the student gains experience by working on a real-world industry or research problem as part of their coursework (homework, Introduction to Data Science and Project courses, Master’s thesis). Each student is assigned a mentor from the UL-FRI faculty and, if the problem originates from industry, also an external advisor.

Students that participate in Scholarly Projects are provided with financial support, including tuition. This makes Scholarly Projects an attractive option for students from abroad.

If you are interested in a Scholarly Project we recommended that you participate in the pre-enrolment process and express your interest by ticking the corresponding field on the pre-enrolment form. Scholarly Project offers will be made as part of the pre-enrolment (and later enrolment) process.

FREQUENTLY ASKED QUESTIONS (FAQ)

How much is tuition?
Tuition is 8.000,00 EUR per year. EU students do not have to pay tuition. Students from certain countries outside of the EU also do not have to pay tuition. Contact the Student affairs office for more information about your specific case.

When will the Master’s in Data Science start?
The Master’s program starts in school year 2019-20. The pre-enrollment process starts early 2019.

Can I enroll part-time?
No. The program is designed for full-time study only.

Don't miss out on all the latest news and events!