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Practice of Simulation & Data Sciences


Computer simulation and data science are the professions of the future. During this course we focus on the advanced applications used to understand complex systems in broad areas including natural and physical sciences, social sciences, life sciences and management of (big) data. The students will have the opportunity to understand how to develop and apply high performance methods for numerical simulations used to solve complex problems related to time series analysis, modeling of real-life phenomena and computational medicine. A large part of the course is dedicated to data science and how to use data storage and data analysis in a smart way. Participants will learn various process discovery algorithms and the key analysis techniques in traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. A basic understanding of programming and statistics (undergraduate level) is assumed.



No required texts. Notes of the course. The lectures are designed to be self-contained.



Angelopoulos S.

Course director

Limongelli V.

Course director

Raniolo S.

Course director

Additional information

Academic year
Master of Science in Computational Science, Core course, Lecture, 1st year
PhD programme of the Faculty of Informatics, Elective course, Lecture (2 ECTS)