Data Engineering And security
winter school

AITL

About the School

DES is not just an IT school but a true center of innovation at the Ivan Franko National University of Lviv. Every year, all interested students from all over Ukraine gather here to gain new knowledge from leading IT experts. DES offers an opportunity to delve into the world of up-to-date technology, unlock your potential, and receive skills that open doors to exciting projects and career heights.

Our objective is to introduce as many young people as possible to the perspectives and new opportunities in the IT industry. Thanks to accessible and high-quality information, our students, with the help of specialists from famous IT companies, can not only gain knowledge but also reinforce it through practice. 

During such difficult times for the country, the support and development of such projects is a direct contribution to the intellectual potential of the young generation and, consequently, to the technical development of Ukraine.

The school curriculum focuses on the in-depth study of core innovative educational programs:

121. Software Engineering
121. Software Engineering
121. High-Performance Computing
121. High-Performance Computing
125. Cybersecurity and Information Protection
125. Cybersecurity and Information Protection
112. Statistics. Data Analysis
112. Statistics. Data Analysis

Experience DES 2020-2024

Exp

Map of Last Year’s Participants

Map

Participant Profile DES

Participant #1
Sofiya Dovhanych

2nd-year student
Faculty of Mechanics and Mathematics
English level: Upper Intermediate

Participant #1
Maks Fyk

3rd-year student
English level: Intermediate

Audience for disseminating information about DES and its partners

Circle

Join Us in 2025!

Duration: January 20 - February 2, 2025

Format: Online/ possible in-person meetings

Features: Two parallel study streams (Core A - first and second students; Core B - for participants with IT experience, including third- and fourth-year students).

Course Structure: Two parallel learning streams (Core A - 1st and 2nd-year students; Core B - participants with IT experience, including 3rd and 4th-year students).

School Language: Ukrainian/English

Target Audience: Students of technical specialties in Ukraine and abroad.

Topics: Software development, Soft Skills, Mobile development, Web development, High-performance computing, Databases & Data warehouses, Cloud services and technologies, Cloud computing, Big Data, Data analysis & Data processing, Machine & Deep Learning, Machine Learning toolchain, Advanced Мachine Learning, Generative AI, Real-world AI applications, Application of AI in cyber security and data processing, Virtual reality, Metaverse, Digital twins, Artificial Intelligence of Things, Cyber Security.

Our contacts: marketing@lnu.edu.ua

"The future belongs to those who can not only adapt to change but also shape its direction." — Tim Berners-Lee, inventor of the World Wide Web.

School Topics

Soft Skills

  • Communication
  • Collaboration and teamwork
  • Time management and organization
  • Empathy / Emotional intelligence
  • Owning up to errors
  • Problem solving and creativity
  • People skills and management
  • Innovation
  • Analytical thinking

Software Development

  • C, C++, Java, Go, Python
  • Digital Immune System
  • Superapps
  • Platform Engineering
  • AI Code Generation, Copilot
  • Decentralized applications, Web3
  • Version Control Systems: Git, Data Version Control (DVC), etc.

Mobile Development

  • Native development for Android and iOS
  • Cross-platform development using Flutter, Qt
  • Kotlin Multiplatform for Cross-Platform Mobile Development
  • React Native for mobile
  • Integration of artificial intelligence systems in mobile development
  • Distribution of mobile applications

Web Development

  • Web development using Flask / Django, React.js, etc.
  • Authentication methods for web services
  • Organization of infrastructure and deployment of web services
  • Web analytics, Social network analysis, Crawlers, analytical platforms
  • Integration of artificial intelligence systems in web development

JavaScript

  • High-performance computing

High-Performance Computing

  • Fundamentals of parallel, hybrid and distributed computing
  • Getting Started with Jetson Xavier NX Developer Kit
  • Getting started with Google Coral's TPU USB Accelerator or/and Google Coral Development Board

Databases & Data Warehouses

  • Relational, non-relational, distributed databases
  • Data warehouse, ETL, Data Workflows
  • NoSQL: Key-Value, Column-based, Document-based, Graph databases
  • Database usage for Data Science, Data Analysis and Machine Learning

Cloud Services and Technologies

  • Amazon Web Services
  • Google Cloud Platform
  • MS Azure

Advanced Machine Learning

  • Management systems of artificial intelligence
  • Enhanced intelligence WIPO Technology
  • End-to-end Machine learning projects/models to solve practical problems
  • Adaptive AI
  • AI Trust, Risk and Security Management (AI TRiSM)
  • AutoML
  • Multi-modal learning
  • Democratized AI
  • Generative AI
  • Generative models, text and speech generation, artificial art

Big Data

  • Big data frameworks: Spark, Kafka, Hadoop, Databricks
  • Big Data in AWS
  • Big Data in GCP
  • Big Data in Azure
  • Big Data Visualization

Data Analysis & Data Processing

  • Data analysis, Data analytics, Statistical data analysis, Predictive Analytics
  • Business analytics, Web analytics, Biostatistics, Time Series Analysis
  • Crawlers
  • Optimization tasks
  • Recommender systems
  • Data processing and data visualization
  • Data mining: RapidMiner, Weka
  • Analytics platforms: Microsoft Power BI, Tableau, SAP Analytics

Machine & Deep Learning

  • Machine and deep learning, Neural networks
  • Supervised Learning
  • Reinforcement Learning
  • Unsupervised Learning
  • Data sources (Kaggle, etc.)
  • ML Hubs (Hugging Face, etc.)
  • CV, Image recognition and classification
  • NLP, Speech recognition, Audio recognition, Text recognition
  • Emotion detection, Pose detection
  • Deep learning for forecasting
  • Machine Learning toolchain
  • Basic libraries: Numpy, Pandas, Scikit-learn, Seaborn, matplotlib, sktime, skforecast
  • TensorFlow, Keras, PyTorch, Apache MXNet
  • CV Libraries and frameworks, OpenCV

Organizers

The organizer of the Winter School is Ivan Franko National University of Lviv.

Artificial Intelligence Technology Lab

University Marketing and Development Center.

Роман Шувар
Department of System Design, Faculty of Electronics and Computer Technologies
Роман Шувар
Олег Бугрій
Department of Mathematical Statistics and Differential Equations, Faculty of Mechanics and Mathematics
Олег Бугрій
Петро Венгерський
Department of Cyber Security, Faculty of Applied Mathematics and Informatics
Петро Венгерський

Project Competition

1) As part of the DES School, a team project competition is traditionally held for school participants. Projects with a well-founded idea, such as a computer application, that provides economic and/or social benefits for society/users are accepted. The team must develop a detailed plan for implementing and executing this idea. A project with partial technical implementation or deployment by the end of the school will be highlighted. Project defenses will be held on Friday (February 2). Details on the award ceremony for the competition winners will be announced later.

2) Each team has up to 15 minutes for project defense (up to 7 minutes for the presentation and up to 8 minutes for the Q&A session with the jury). Preparation continues while the previous team answers questions.

3) A mandatory element of the project defense is a presentation file (pdf, pptx, etc.), which is submitted in advance to the School's organizing committee. This file should include:
    • Project title and brief description;
    • Team members and mentor (if any);
    • Project topic relevance;
    • Analysis/competition of similar developments in the market;
    • Project results (main presentation part);
    • Social significance or commercial value;
    • Future project development plans post-school.

4) Besides the main presentation file, the team can use additional files/tools, etc.

5) To save time and avoid technical issues during project presentations, teams should send the main project presentation file to Oleh Buhrii on Slack by THURSDAY after school sessions end.

6) Ideally, each team member should participate in presenting their work, highlighting their role in the team.

7) Each mentor evaluates their team, emphasizing the following:
    • Quality of teamwork;
    • Psychological atmosphere within the team;
    • Achievement of the team’s goals.