Our online modules provide students and teaching staff with the opportunity to enhance their digital and technical skills in a practical setting. From the basics of Business Administration and Mathematics, Soft Skills, and International Marketing to modern technologies such as Machine Learning, Deep Learning, ICT security, and Android development, the courses impart both theoretical knowledge and practical skills. Students can build up their knowledge flexibly online, while teaching staff strengthen their professional qualifications in a targeted manner. This creates versatile learning paths that prepare students for the demands of the digital and globalized working world.
Registration for the online courses for the 2026-2027 winter semester is now open.
We look forward to receiving your applications!
© EDUBA-team
Course duration (lectures): 10 weeks/30 hours
Instructor: 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.
Schedule: Wednesdays and Fridays (1/2) (October 7, 2026–December 9, 2026): 16:00–18:00
© EDUBA-team
Course duration (lectures): 8 weeks/30 hours
Instructor: 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 fundamentals 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 ranging from fully connected, convolutional, and recurrent networks to GANs, Transformers, and large language models.
Schedule: Tuesdays, Thursdays (October 1, 2026–November 19, 2026): 16:30–18:30
© EDUBA-team
Course duration (lectures): 12 weeks/30 hours
Instructor: 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.
Schedule: Thursdays, Saturdays (October 3, 2026–December 19, 2026): 16:30–18:00
Course duration (lectures): October–December
Practical training: 2 hours per week
Instructor: Prof. Dr. Dmitry Kachan (HSA)
Language: English
ECTS: -/1
Exam type: Project
Learning outcomes: Students 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 hands-on 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 involves developing 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.
Schedule: Mondays (12.10.2026-14.12.2026): 14:30-16:00
