Data Science, MSc

Faculty: Informatics

Academic discipline: Computer Science and Information Technology

Qualification: Data Scientist

Starting date: September/February

Language requirements: English language proficiency (TOEFL 547 /IELTS 6.0 /oral examination)

Academic requirements: Relevant bachelor’s degree in Information Technology

Duration: 4 semesters

ECTS credits: 120 credits

Tuition Fee: 7,500 USD/year

One-time fees: Application fee 150 USD, entrance procedure fee 350 USD

Short description:

The aim of the program is to train professionals who can understand the properties of different types of data and the structure of complex data sets, uncover the relationships inherent in the data, apply the necessary transformations to raw data to prepare it for analysis, analyze data, draw conclusions from the data, and model real-world processes. They will also be able to develop and manage data-oriented applications, perform and coordinate R&D tasks, and continue their studies in PhD programs.

Lecture, seminar: 40%

Practice: 60%

Internship/Practice: Students should complete a 6-week internship either at the university working on research projects or at a multinational or local company.

Main subjects typically include (this list is indicative and may change):

Year 1: Machine Learning Fundamentals, Statistical Foundations of Data Science, Optimization in Data Science, Data Visualization Methods, Data-oriented Programming, Cloud Computing, Information Security, Data Ethics.
Year 2: Modern Deep Learning Frameworks, Advanced Machine Learning, Advanced Reinforcement Learning, Big Data Technologies, Advanced Robotics, Self-driving Cars, AI Security, Financial Models, Genetics and Big Data.

Career prospects: Data Science, MSc graduates have comprehensive knowledge of the principles and methods of data science, as well as the technical background required to efficiently handle large and complex data sets. With these skills, graduates have a wide range of opportunities in various industries, including positions such as data scientist, data analyst, data engineer, business analyst, or machine learning software developer.

For more information about the program, please check the course Bulletin

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