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Course System Architecture And Design: The Content Of The Course Is Aimed At Teaching The Principles Of Proper Construction Of Information Systems. Monolithic And Microservice Architecture: The Benefits Of Dividing The System Into Small Independent Components. Client-Server Technologies: Logic Of Data Exchange Between Frontend And Backend. Database Architecture: Design Of Sql And Nosql Databases, Data Replication And Optimization. Api Design: Creating Restful Or Graphql Interfaces For System Interaction. Full-Stack Development Technologies: Within The Framework Of The Program, Students Develop The Skills To Create A Working Prototype Of An Information System: Modern Web Stack: Html5, Css3, Javascript And Corresponding Frameworks: React, Vue Or Angular. Backend Development: Server-Side Programming Languages—Python, Node.Js Or Java—And Implementation Of Business Logic. Mobile Architecture: Cross-Platform Solutions For Ios And Android: Flutter Or React Native. Cybersecurity And Data Protection: The Program Involves Studying Methods For Identifying Information System Vulnerabilities And Ways To Eliminate Them, Including: Secure Development (Devsecops): Ensuring Security At The Stage Of Writing Code, Taking Into Account The Owasp Top 10 Standard. Infrastructure Security: Firewalls, Ids/Ips And Vpn Systems. Cryptography And Authentication: Data Encryption, Multi-Factor Authentication (Mfa) And Role-Based Access Control (Rbac). Incident Response: Scenarios For Identifying Hacker Attacks And Restoring The System. Infrastructure And Cloud Solutions: The Course Content Covers The Deployment And Management Environment Of Information Systems. Containerization And Orchestration: System Management Using Docker And Kubernetes. Cloud Services: Building A Secure Architecture On Aws, Azure Or Google Cloud Platforms. Ci/Cd Processes: Automated Assembly, Testing And Implementation Of The System. Number Of Course Participants 4., Advanced Training Course “Digital Pedagogy And Edtech Technologies”. Scientific Validity: The Course Is Based On The Connectivism Paradigm, Which Considers Digital Networks As The Main Tool For Storing And Transmitting Knowledge. The Platforms Architecture Is Based On Human-Computer Interaction (Hci) Principles And Cognitive Load Theory To Provide Scientifically Proven User Interface (Ui/Ux) Performance. Tools Such As Miro And Padlet Model Collaborative Environments Within The Framework Of Social Constructivist Theory, Realizing The Phenomenon Of “Collective Intelligence.” At The Technical Level, The Module Is Guided By The Principles Of Integration Of Various Services (Interoperability) And Security Of Data Exchange Based On Lti Standards And Api Technologies. As A Result, The Educational Environment Is Designed Not As A Simple Set Of Tools, But As A Scientifically Based Holistic Intellectual Ecosystem. Edtech Platforms And Interactive Environment • Designs The Architecture Of A Comprehensive Digital Educational Environment And Provides Its Technical Support; • Integrates Edtech Tools Of Various Levels Into A Single Technology Stack; • Develops High-Level Interactive And Adaptive Content That Increases Student Engagement. Artificial Intelligence And Prompt Engineering This Block Is Aimed At Developing Skills In Professional Interaction With Neural Networks And Integrating Artificial Intelligence Solutions Into The Technological Infrastructure. The Training Process Examines Methods For Performing Complex Technical Tasks Using Advanced Prompt Engineering Strategies: Zero-Shot, Few-Shot, Chain-Of-Thought. Number Of Course Participants 9., Advanced Training Course “Applied Mathematics, Programming And Ai Technologies.” Scientific And Methodological Validity: The Course Is Based On The Methodological Principles Of Modern Stem Education And Data Science. The Structure Of The Program Is Based On A Didactic Model Of Combining Fundamental Theoretical Knowledge With Practical Algorithmization. Scientific Depth Of Content: Course Content Covers Complex Concepts Of Discrete Mathematics, Probability Theory, And Mathematical Statistics. The Mathematical Apparatus Of Neural Networks And Machine Learning Is Studied In Depth: Gradient Descent, Matrix Calculations And Other Methods. Availability Of A Didactic Module: The Program Includes A Special Module “Modern Methods Of Teaching Mathematics And Computer Science.” It Offers A Technique For Explaining Complex Abstract Concepts Through Visual Modeling And Interactive Python Libraries. Integration Of The Research Component: Course Participants Not Only Use Ready-Made Code, But Also Master The Elements Of Research Work Through Generating And Testing Hypotheses Based On Data (Hypothesis Testing), As Well As Building Their Own Mathematical Models. Digital Technologies Module: This Block Is Aimed At Mastering Cloud Computing (Google Colab), Version Control Systems (Github) And Specialized Frameworks For Data Processing (Pandas, Numpy, Tensorflow). Innovative And Project-Based Learning: The Course Should Introduce Innovative Pedagogical Technologies - Project-Based Learning, Case-Study, Flipped Classroom, And Also Consider Ways Of Using Them In Teaching Mathematics. Number Of Course Participants 5.