Topics covered by the course include secure system modelling, security management standards, domain model for security, risk management, risk assessment, management of protected assets, security risks, and security countermeasures (e.g., access control, penetration testing, identity management, etc.), security level assessment, management of security at different types of systems (e.g., business process-aware systems, intelligent infrastructure, blockchain-based systems, identity management systems, risk-aware forensic systems, etc.).

This course provides an introduction to computational chemistry methods and their application. In practical works DFT methods will be used to study actual electrochemical problems.

This course gives an introduction to modern data acquisition (DAQ) concepts and devices. From the fundamental principles of Analog-Digital conversion to high-level design considerations, you will learn how to differentiate available DAQ approaches and pick the right hardware for your measurements. In addition to hardware-based signal conditioning, the course will teach you post-processing techniques regarding digital signal filtering, conversions between time- and frequency-dependent representations, and introduces global optimization methods for parameter estimation. A significant part of the course is dedicated to labwork that will guide you through various measurement techniques such as on-demand, continuous, and finite sampling. You will learn to perform synchronized measurements between multiple devices and implement customized DAQ solutions for both low- and high-frequency applications.

The course is based largely on practical works. Data on ecological communities: matrix of sites x taxa. Statistical software package R, its main principles. Reading and exploring data in R. Transformation of data. Finding similarities. Multivariate methods: ordination and clustering. Including environmental data. Environmental and taxonomic similarity. Including data on taxa. Measuring biodiversity: taxonomic, functional, phylogenetic and genetic diversity. Predicting distribution of taxa and biodiversity. Analysing time series. Simulating ecological communities. Presenting results in R.

This course teaches fundamental data engineering skills, i.e., data modeling, data storage, transformation, and orchestration. The course discusses the role of data engineers in the data science life cycle. We look at how to build modern and robust data-processing pipelines in order to enable data analytics and machine learning capabilities of organizations.

The course starts with an introduction to R - one of the most powerful data analysis and visualisation language, that has been taking over both business world and academia. R is open source software, but is frequently mentioned to have a rather steep learning curve. We will overcome this together. We will go through the modern R workflow starting from data aquisition and munging and ending with publication ready visualisation of our data.