Description

This program focuses on techniques to analyze large volumes of data and on information systems that use these techniques to offer smart services to their users. Graduates will be able to design and implement software solutions for supporting real-time data-driven decision making. Students will be familiarized with large scale and in-memory databases; the ecosystem of distributed processing and its components with particular focus on open-source development of these components; business analytics and reporting tools over aggregated data from multiple sources; visual analytics tools and their components; the utilization and adaptation of machine learning and data mining methods in realtime scenarios.

In 2016, the Data Science and Technology Department was established, with the support of T-Labs (Deutsche Telekom), as the first industrial department at ELTE. The department conducts a joint research program with Deutsche Telekom, involving students, company professionals, PhD students and lecturers of the University in its research activities. This program is recommended to applicants who:

• want to understand the properties of various data types and the structure of complex data sets, recognize the relationships among data, apply the necessary raw data transformations, draw conclusions, and model real-world processes;
• want to conduct analyses to detect, discover and better understand the abounding data around us generated by social media, manufacturing systems, medical devices, logistic services, and countless others, on a daily basis.

This program enables students to:

• understand the concepts of data analysis, ethics, data security, mathematics, statistics, the programming principles and contexts – in particular, data types, representations, transformation and optimization procedures, as well as the principles of multivariate statistics and machine learning – which are required to innovate and conduct research in data science;
• gain in-depth technical skills in scalable data collection techniques and data analysis methods, and learn how to use and develop a suite of tools and technologies that address data capture, processing, storage, transfer, analysis, visualization, and related concepts (e.g., data access, data pricing, and data privacy);
• be aware of the operation of current technologies used for analysis and modelling, and be able to apply them in real-life situations, including the ones with large amounts of data;
• be familiar with techniques used for storing, processing and visualizing large amounts of data, and with the properties of the different ecosystems of tools.

Strength of program

The high standard of training is guaranteed by the highly qualified academic staff. Teaching is supported by modern infrastructure and well-equipped computer labs (artificial intelligence, databases, and robotics). The library of the Faculty contains several thousand volumes. Upper-year students and PhD students help the first-year students in a mentoring system to overcome their first challenges at the university. In addition to the high level of theoretical training, the Faculty's relationship with the business community, the joint research and development projects offer up-to-date practical knowledge and experience to the students. The Faculty has concluded bilateral agreements with numerous universities in the world, which allows students to study one or two semesters, or participate in research projects, at a partner institution.

Extracurricular undergraduate research activities of the students are supported and supervised by leading scientists of the Faculty. These students present their findings at a conference organized by the Faculty every year. Workshops are also organized with international partners.