Master's

For a quick summary see our brochure. It’s available in light and dark versions.

Our Master’s of Computer and Data Science is a state-of-the-art, intensive, and rewarding program that focuses on equipping students with the theoretical foundations, practical knowledge, and hands-on experience. If you are a student with a technical or mathematical background and you aspire to be a leading data science professional, researcher, or teacher of data science, then this program is for you!

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 with sufficient technical and mathematical content.

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

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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.

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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.

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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.

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Principles of Uncertainty

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

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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.

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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.

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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

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Big Data

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

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Deep Learning

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

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Machine Learning for Data Science II

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

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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

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Big Data

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

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Deep Learning

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

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Machine Learning for Data Science II

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

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Bayesian Statistics

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

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Advanced Topics in Computer Vision

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Artificial Intelligence

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Biomedical signal and image processing [lectures in Slovenian]

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High Performance Computing

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Introduction to Bioinformatics

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Image-Based Biometry

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Machine Learning [lectures in Slovenian]

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Natural Language Processing

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Network Analysis

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Web Information Extraction and Retrieval [lectures in Slovenian]

ADMISSIONS

Important dates

Info-lecture: Feb 13 2020 at 13:15 (FRI, lecture room P03)

Pre-enrollment deadline: Mar 30 2020 extended to Apr 12 2020 (23:59 CET)

Pre-enrollment feedback: Apr 15 2020

Pre-enrollment substantive test: May 2020

Enrollment: August 2020

Entrance exam: September 2020

Program start: October 2020

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 202021

The purpose of pre-enrollment is to identify prospective applicants and advise them on the program and the admissions process. Those who pre-enrol will also have the opportunity of taking a substantive test can be, if successful, used as a substitute to the Data Science Master’s entrance exam. 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 requested documentation (see Submission checklist below) to datascience@fri.uni-lj.si before the pre-enrollment deadline. We will review your submission and provide feedback.

Pre-enrollment does not guarantee that you will be accepted to the program - you still have to apply (see Enrollment below) and meet the formal enrollment criteria (e.g., graduation from the undergraduate studies). Pre-enrollment is also not mandatory - not pre-enrolling does not preclude you from applying to our Master’s program.

Pre-enrollment submission checklist:

  • Completed and signed pre-enrollment form (link).
  • Structured CV (education, work experience, technical skills, language proficiency, bibliography, other relevant achievements).
  • Academic transcript that includes all university-level courses completed so far and grades received (can be in English or Slovenian). Non-Univesity of Ljubljana students should also provide a description of the grading system. UL-FRI undergraduate students may instead of the transcript include a short statement that they allow us to inspect their grades in Studis system for the purposes of the Data Science pre-enrollment process.
  • Motivational letter (1 page, 500 words maximum).

All submitted documents must be in English and in pdf format.

Enrollment 202021

To be announced…

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.

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

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