About this course
Networks are everywhere! We find them in transportation systems that connect cities throughout the world, in the web of interactions within our cells, in the vast expanse of the World Wide Web that powers our daily searches, and even in the social relationships that define our communities. They are crucial for understanding why certain webpages appear at the top of your Google searches or why a specific song is suggested to you on Spotify.
In this course, you will dive into the world of complex networks. You will explore computational and mathematical methods used to characterize, represent, and analyze these intricate systems. Using a hands-on approach, the Python programming language, and the NetworkX library, you will model, visualize, analyze, and extract insights from real-world networks.
Syllabus
Link to Draft Syllabus
Go to syllabusThis is a draft syllabus. The final syllabus will be available here a few days prior to the new course’s first start date.
Pre-requisites
One year of computer science at university level. One of the computer science courses should be in algorithms and data structures. Knowledge of at least one programming language (e.g. Python/Javascript/Java/C++/Matlab). A course in probability is recommended.
Faculty
Panagiota Katsikouli
FacultyPh.D. in Informatics, University of Edinburgh (2018). Currently lecturing as Faculty Member at the Open Institute of Technology (2023-present) and involved in Teaching and Research at the University of Copenhagen (2020–present). Previous experience as post-doctoral Researcher at the Technical University of Denmark (2019-2020), University College of Dublin (2019), and INRIA Lyon (2018-2019). Teaching and Research. With DIS since 2023.