The development of new, sophisticated experimental setups requires the integration of many independent components into one, complex device. This task could be easily done with the use of so-called virtual instruments, that control all hardware devices from one software platform. Virtual instruments could be additionally assisted with electronic appliances, composed of components that nowadays are often available in a form of evaluation boards and modules. Unlike commercial laboratory equipment, custom-built devices provide unique features, that allow for exploration of unknown areas of natural sciences. In this course, we present the concept of virtual instruments and provide basic knowledge on electronics and electrical measurements. We focus on the practical application, demonstrating building blocks that could be easily implemented in laboratory practice. The course will be divided into three parts: lectures, hands-on laboratories, and group projects.

You will be introduced to the best practices of designing reliable and redundant computer network topologies. The aim will be the creation of networks resistant to various types of failures. Redundancy issues in the second and third layer will be raised. Then, various methods of computer networks management will be presented. First, the standard methods of configuration and IOS systems management. Secondly, modern methods of programming SDN networks will be practiced, during which students will train the centralized configuration of devices using Python scripts and the API of IOS systems. Second part of the course is devoted to securing networks and network devices against attacks and overload. The final part will be devoted to monitoring and analyzing network traffic and detecting threats or attacks. Students, individually or in groups, will have the task of creating a redundant network, protected against various types of attacks for a chosen case study. Classes are conducted in a physical laboratory of computer networks based on Cisco devices. A virtual environment in the form of Packet Tracer simulator, GNS3 emulator and virtual machines of the real networking operating systems will also be used for support.

According to CareerCast, Data Scientist is one of the best job of recent years. It requires a unique blend of skills from three disciplines: mathematics (especially statistics), computer science (especially data analysis) and domain knowledge (in the field it will be applied), which is very attractive to many employers. Strong computer science skills and different approach to data analysis, based on scientific method, is what makes Data Scientists different from statisticians. At the same time, Python is becoming a language of choice for many data scientists, next to languages like Scala and statistical packages like R. It is also the first programming language many people learn, no matter their age. This course gives you a chance to quickly build up your Python skills, learn basics of how data scientist works and apply all this to a project on a real, large data sets. This course is highly practical.

The main goal of the course is to teach students the holistic approach to the design of the low energy demand buildings with special emphasis on the utilisation of renewable energy in building installations, application of heat recovery systems and maintaining thermal comfort of users. The course focuses on thermal comfort and heat production (to supply heating and domestic hot water systems) for low energy buildings. The subject covers solutions based on sustainable design including for example solar thermal collectors, air-to-water heat pumps, energy recovery heat exchangers in air handling units, etc. Students will participate mostly in active forms like laboratory, calculus and simple project. All practical exercises will be preceded by short lectures.