Unsere Online-Module bieten Studierenden und Lehrkräften die Möglichkeit, digitale und technische Kompetenzen praxisnah zu erweitern. Von Grundlagen der Betriebswirtschaft und Mathematik über Soft Skills und internationales Marketing bis hin zu modernen Technologien wie Machine Learning, Deep Learning, ICT-Security und Android-Entwicklung – die Kurse vermitteln sowohl theoretisches Wissen als auch anwendbare Fähigkeiten. Studierende können ihr Wissen flexibel online aufbauen, während Lehrkräfte ihre professionelle Qualifikation gezielt stärken. So entstehen vielseitige Lernwege, die auf die Anforderungen der digitalen und globalisierten Arbeitswelt vorbereiten.
Die Anmeldung für die Online-Kurse des Wintersemesters 2025-2026 ist geschlossen.
Wir freuen uns auf Ihre Bewerbungen im Frühjahr 2026!
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Duration of the course (lectures): 10 weeks/30 hours
Lecturer: Dr. Olga Matsuga (DNU)
Language: Ukrainian/English
ECTS: -/1
Type of examination: Written assignment
Learning outcomes: By the end of the course, students will be able to:
• Understand the fundamental tasks and basic principles of supervised and unsupervised machine learning.
• Apply Python libraries such as pandas and scikit-learn for data preprocessing and model development.
• Develop, train, and evaluate machine learning models using appropriate methods, metrics, and Python libraries.
Course content: Introduction to machine learning: key concepts and tasks.
• Data preprocessing: theory and hands-on exercises in pandas.
• Classification and regression: models, hyperparameters, model evaluation, overfitting, and practical exercises in scikit-learn.
• Clustering: methods, validation, and hands-on implementation in scikit-learn.
• Dimensionality reduction: overview with practical examples.
Timetable: Wednesdays, Fridays (1/2) (7.10.2026-9.12.2025): 16:00-18:00
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Duration of the course (lectures): 8 weeks/30 hours
Lecturer: Dr. Maryna Ivanchenko (DNU)
Language: Ukrainian/English
ECTS: -/1
Type of examination: Written assignment
Learning outcomes: Learners will understand the core concepts and applications of deep learning, gaining hands-on experience in designing, training, and evaluating neural networks with TensorFlow. They will also acquire practical experience in applying ready-to-use models and in adapting pre-trained models to real-world tasks.
Course content: The course provides the foundations of building and training deep neural networks, along with an introduction to key architectures and techniques. It explores a range of applications such as classification, regression, computer vision, natural language processing and reinforcement learning, covering models from fully connected, convolutional and recurrent networks to GANs, Transformers and Large Language Models.
Timetable: Tuesdays, Thursdays (1.10.2026-19.11.2026): 16:30-18:30
© EDUBA-team
Duration of the course (lectures): 12 weeks/30 hours
Lecturer: Prof. Dr. Oleksandr Lemeshko (NURE)
Language: Ukrainian
ECTS: -/1
Type of examination: Written assignment
Learning outcomes: Understanding the principles of network infrastructure protection; Knowledge of types of network attacks (DoS, MITM, spoofing, sniffing, etc.); Configuring Cisco switches and routers in accordance with security policies; Implementing VLAN, ACL, NAT, VPN, and IPSec to isolate and protect traffic; Detecting and mitigating attacks such as ARP spoofing, DHCP starvation, and MAC flooding.
Course content: Fundamentals of Network Security; LAN Security Techniques; WAN and Routing Security.
Timetable: Thursdays, Saturdays (3.10.2026-19.12.2025): 16:30-18:00
Duration of the course (lectures): October-December
Practical training: 2 hours per week
Lecturer: Prof. Dr. Dmitry Kachan (HSA)
Language: English
ECTS: -/1
Type of examination: Project
Learning outcomes: Learners will understand the core concepts and challenges of distributed software systems, including service boundaries, communication patterns, consistency, reliability, observability, and security. They will gain practical experience in implementing and containerizing distributed services, applying REST and gRPC, using reliability patterns, integrating observability mechanisms, and understanding security mechanisms in distributed applications.
Course content: The course introduces the foundations of distributed software architecture through a coherent combination of lectures and practical labs. It covers microservices, REST-based communication, Docker and Docker Compose, gRPC and Protocol Buffers, data consistency and the CAP theorem, reliability patterns, observability, event-driven architecture, and security in distributed systems. The practical component develops a single evolving project, progressing from a containerized service to a multi-service system with persistence, reliability mechanisms, monitoring, and security. Technologies include Docker, MongoDB, Prometheus, Grafana, and related tools.
Timetable:
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