Master-level studies involve specialized study in a field of research or an area of professional practice. Earning a master’s degree demonstrates a higher level of mastery of the subject. Earning a master’s degree can take anywhere from a year to three or four years. Before you can graduate, you usually must write and defend a thesis, a long paper that is the culmination of your specialized research.
Big data deals with the quantity, nature, context, consistency and quality of large data sets. A firm grasp of mathematics and computer science is required for this field of study. Results in big data have been used in the military, public finance sector, education and more.
Romanian university qualifications highly appreciated and recognized in Europe and beyond, lowest tuition fees and living cost in Europe. The Romanians have an old and rich history, especially in the capital Bucharest with its 2 million people. International students willing to study in Romania can apply either to the Ministry of Education and Research or to the chosen Romanian university, in order to receive the Letter of Acceptance.
Cluj-Napoca city is the capital of Cluj County in Romania and the 2nd most populated after Bucharest. It is a metropolitan area with a population of over 300,000 residents. It has around 150 pre-university educational institutions.
Top Masters in Big Data in Cluj-Napoca in Romania
The master’s program aims at providing students with the appropriate tools for further doctoral studies and professional activity. [+]
The master’s program aims at providing students with the appropriate tools for further doctoral studies and professional activity.Program objectives
Acquisition of theoretical, applicative and practical knowledge in:complex systems modeling based on mathematical concepts and methods, and on programming concepts and techniques. programming and usage on/of computation systems, especially those of high performance, which are necessary for solving real-life problems and for simulating complex problem solutions. exploitation (data-analysis, knowledge-discovering) and visualization of „big data” for computation problems, statistical interpretations, decision processes, or for scientific instruments. applicative scientific domains where high-performance systems are used. analysis and improvement of software processes. professional modeling for teamwork as well as interdisciplinary approaches to research and development. Core courses Programming Paradigms Parallel and Distributed Operating Systems Formal Modelling of Concurrency Advanced Methods in Data Analysis Functional parallel programming for big data analytics Models in parallel programming General Purpose GPU Programming Workflow Systems Resource-aware computing Data Mining Grid, Cluster and Cloud Computing Knowledge Discovery in Wide Area Networks Admission requirements ... [-]