The modern world is characterized by exponential growth in the amount of data captured from various data sources. Collection, storage, and processing of data are central elements of present-day commerce and science, with rapid adoption in areas ranging from business analytics and marketing to computer vision and natural language processing. They all rely on advanced algorithms, models, computing methods, and efficient and scalable computer systems - all encompassed under the Computing master study programme.
Data science combines computer science and statistics in order to uncover meaningful information from data, ultimately producing new knowledge and value in a digitized and globalized economy. With the rising amounts of data being generated and collected each day, data scientists are becoming increasingly important in both industry and academia. The Data science profile provides students with strong foundations in mathematics, statistical modeling, machine learning, and other knowledge enabling them to develop data science solutions for a large range of applications, solve advanced engineering problems, conduct research and development, and act as leaders in the current digital revolution.
High emphasis is placed on practical skills and early career development during the study. The study programme also facilitates the development of soft engineering skills through the set of transversal courses, which complement engineering education with an additional set of skills that are necessary to form an engineer as a complete person.
A visual guide to the study programme is presented below. The numbers in rows represent academic semesters (two semesters per year, a total of two years) and the numbers in columns represent ECTS points (30 ECTS points per semester).
The visual guide shows the structure of mandatory courses. For a full list of elective and transversal courses please visit the study programme page.
How will your study look like in practice?
In our study programme MSc in Data Science at FER, you will gain strong foundations in mathematics and statistics courses that are prerequisites for a deep understanding of the nature of data and strategies for data analysis. Our lecturers and researchers work in a wide range of exciting areas, so you will have the opportunity to learn more about domain-specific applications through numerous elective courses, seminars, and projects.
Are you curious about how machines can understand human language? The researchers at TakeLab are committed to state-of-the-art natural language processing and text analysis research. Want to know is it really "six degrees of separation" between you and anyone in the world? The SocialLab research group seeks answers to that and other questions regarding social networks and online human social behavior. If learning how computers can learn to detect objects in images is more your cup of tea - the Image Processing Group develops computer vision methods for biomedical images in order to improve diagnostic processes. Intrigued by challenges brought on by Big Data? Our StreamsLab will introduce you to leading Big Data platforms and technologies. And if you are interested in bioinformatics, the LBCB will gladly show you their state-of-the-art genome assembly algorithms. For those more attracted to finance, the researchers at LAFRA teach machines to understand financial markets and risk. These are just some of the research laboratories and groups you can get involved in, with an opportunity to participate in a number of data science-related EU-funded research projects as well as industrial projects.
FER alumni are highly appreciated professionals not only in Croatia but also in high-tech companies in different sectors worldwide. FER actively cooperates with more than 450 companies, including some of the most significant high-tech companies from Croatia but also all over the world. Both students and companies benefit from this network that provides an opportunity to connect the best matching talents with industry needs even before students finish their studies.
As a data science professional, you will become an engineer with high-level technical skills, who is capable of building complex quantitative algorithms in order to organize and analyze large amounts of information in numerous application areas. Graduated engineers are in high demand for industry positions such as data scientist, machine-learning scientist, data analyst, data engineer, business analyst, and computer system analyst.
Students are offered early career guidance through the services of Career Centre, which helps students to connect with employers through the organization of various networking events, such as Job Fair and Career Speed Dating.
Students are highly encouraged to participate in an internship programme to gain a competitive edge on the labor market while still studying.
As a Faculty with traditionally highly appreciated research components on an international level, students can also take a part in more than 250 international and national R&D projects, both scientific research and industry collaboration projects, that run at FER every year.
For students with startup aspirations, a student-friendly startup incubator is a right place to visit and explore new horizons.
To meet the entry requirements for master's level studies, all students must fulfill the following requirements:
General requirement: A successfully completed an appropriate bachelor or integrated study with a workload equivalent to that of 180 ECTS or higher
English language requirement: Documented proficiency in English.
More detailed information about meeting admission requirements and application procedure are available here.
Formal application procedures and online enrolment application are available here. Please note that we have three submission deadlines:
Early bird – due 21 May 2021
Regular – due 23 July 2021
Last call – due 17 September 2021
We strongly recommend completing and submitting your application by Early bird or Regular deadlines.
The Last call deadline is intended for students who are unable to apply by the Regular deadline due to administrative reasons (such as differences in school system calendars or delays in obtaining academic records) or due to extraordinary circumstances (such as natural disaster or health-related issues).
Prospective students can explore master study website to acquire more information.