Dual B.Sc. Program in Data and Information Engineering and Mathematics

This four-year integrated undergraduate program is jointly offered by the Faculty of Data and Decision Sciences and the Faculty of Mathematics, leading to a Bachelor of Science (B.Sc.) in Data and Information Engineering and Mathematics.

The program is intended for highly qualified students with strong mathematical ability and an interest in the interplay between advanced theory and computational practice. It is a selective and academically strong track.

The program combines strong training in mathematics with a comprehensive education in data and information engineering. Its structure emphasizes a solid theoretical foundation, enabling students to develop a principled understanding of models and algorithms and to apply and extend them in complex settings.

The curriculum includes core courses in mathematics, such as calculus, linear algebra, probability, and combinatorics, as well as courses in data science and engineering, including machine learning, statistics, data structures, databases, and artificial intelligence. Advanced topics studied in later stages include stochastic processes, functional analysis, and distributed information systems. The program also includes laboratory courses focused on data collection, analysis, and presentation.

The program requires the completion of 169 academic credits, including required courses, electives in both participating faculties, enrichment courses, and free electives. The elective component enables students to pursue in-depth study in both theoretical mathematics and data and information engineering.

Graduates are well prepared for roles that require a strong analytical foundation and the ability to work with complex models and data. The combination of mathematical depth and computational training supports work in areas such as data science, machine learning, and algorithm development, particularly in settings that require a deeper understanding of underlying methods.

The program also provides a strong foundation for graduate studies in mathematics, data science, and related fields, meeting the academic requirements of both theoretical and applied programs.