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 2025-2026 winter semester is now closed.
We look forward to receiving your applications in spring 2026!
© EDUBA-team
Duration of the course (lectures): 10 weeks/30 hours
Lecturer: Dr. Olga Matsuga (DNU)
Language: Ukrainian
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: Tuesdays, Wednesdays (1/2) (7.10.2025-10.12.2025): 15:30-17:30
© EDUBA-team
Duration of the course (lectures): 10 weeks/30 hours
Lecturer: Dr. Maryna Ivanchenko (DNU)
Language: Ukrainian
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, Wednesdays (7.10.2025-10.12.2025): 16:00-17:30
© EDUBA-team
Duration of the course (lectures): 10 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 (9.10.2025-18.12.2025): 16:30-18:00
