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Adriana Iamnitchi (A.I.)

I have been the course coordinator of the following courses. 

  • KEN4275 Network Science (MSc programs)

Many aspects of everyday life and science can be represented as networks: social networks represent relationship (links) between people (nodes); brain activity can be represented via synapses (links) between neurons (nodes); the street map is formed of roads (links) that connect at intersections (nodes); authors of scientific papers connect to each other in a citation network, with directed links from the paper cited to the paper citing it; communication networks connect routers via physical or logical links; etc. Network analysis plays a significant role in the “big data” analytics because of size, data velocity, or computational complexity. This course focuses on the study of network structures and dynamic processes on networks using real data from various disciplines, including socio-technological platforms, biology, social science, and economics. Topics cover the analysis and modeling of complex networks, network dynamics, community detection, network resilience and contagion, as well as processing of network structures for machine learning tasks.

  • BCS2110 Computer Networks (Computer Science BSc program)

This course introduces the fundamental concepts in computer networking. It covers the principles and structures of network architectures, protocols, and interfaces. Topics include the OSI and TCP/IP models, network devices, routing algorithms, wireless communication, network security, and emerging technologies.  

  • BENC 2011 Data Science (Business Engineering BSc program, 2022-2024)

Data science is an interdisciplinary field concerning scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. This course presents the key four aspects of data science: data acquisition and preparation for investigation (scrapping, wrangling, cleaning, sampling, management) to guarantee high quality and quick and reliable access, exploratory data analysis to generate hypotheses and intuition, modelling based on statistical/machine learning and correct communication of the analysis outcomes through visualisation, storytelling and reporting. Lectures and tutorials emphasise the practical use of these aspects and prepare students for developing real-world data-driven applications.