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Plan Szkolenia
Foundations of Agile Thinking
- The Agile Manifesto and its relevance beyond software
- Comparing agile with traditional waterfall and plan-driven models
- Scrum roles, events, and artifacts mapped to academic project cycles
- Kanban and flow-based management for research and teaching teams
- Choosing agile hybrids suitable for engineering and design environments
Agile Planning and Collaboration
- Writing user stories and defining acceptance criteria for engineering problems
- Backlog prioritization techniques: MoSCoW, value vs. effort, risk-driven ordering
- Sprint planning and estimation with non-software teams
- Retrospectives and continuous improvement in an academic setting
- Collaboration tools and boards for multi-disciplinary participants
Introduction to DevOps Culture
- Defining DevOps: breaking silos between development and operations
- The CALMS model: Culture, Automation, Lean, Measurement, Sharing
- DevOps in research labs, civil engineering teams, and architecture studios
- Building a blameless culture and feedback loops in educational institutions
- Ethics, security, and compliance considerations in academic DevOps adoption
Version Control and Collaborative Code Management
- Git fundamentals for reproducible engineering and design work
- Branching strategies: trunk-based, feature branches, and GitFlow simplified
- Pull requests, peer review, and code ownership in teaching teams
- Managing non-code assets: CAD files, BIM models, simulation datasets
- Repository organization for course materials and student projects
Continuous Integration and Build Automation
- CI concepts and their application to compiled and scripted engineering tools
- Setting up automated builds for software, simulations, and documentation
- Pipeline stages: compile, package, lint, and pre-flight checks
- Popular CI platforms overview: GitHub Actions, GitLab CI, Jenkins
- Handling large artifacts, dependency caching, and parallel execution
Software Quality and Static Analysis
- Defining software quality: maintainability, reliability, usability, efficiency
- Code metrics: cyclomatic complexity, coupling, cohesion, and duplication
- Static analysis tools for Python, Java, C++, and common engineering scripts
- Documentation as quality: docstrings, README standards, and living docs
- Integrating quality gates into CI pipelines without blocking student progress
Testing Strategies and Test Design
- The testing pyramid: unit, integration, system, and acceptance testing
- Writing unit tests for engineering calculations, simulations, and utilities
- Test-driven development (TDD) and behavior-driven development (BDD) fundamentals
- Mocking external systems: sensors, APIs, finite-element solvers
- Structuring test suites for multi-disciplinary team projects
Test Automation and Continuous Testing
- Automating test execution within CI/CD pipelines
- Test reporting, coverage thresholds, and flaky test management
- Property-based testing and fuzzing for engineering algorithms
- Regression testing strategies for evolving course assignments
- Performance and load testing for simulation and rendering workloads
Continuous Delivery and Deployment Concepts
- CD fundamentals: delivery vs. deployment, environments, and promotion
- Deployment patterns: blue-green, canary, and feature toggles
- Applying CD principles to publish research artifacts, course sites, and apps
- Container basics with Docker for reproducible engineering environments
- Infrastructure as Code introduction: managing lab and cloud setups declaratively
Observability, Monitoring, and Feedback
- Logging, metrics, and tracing for academic software and simulations
- Setting up lightweight monitoring for student projects and research tools
- Using feedback data to iterate on teaching materials and lab assignments
- Dashboards and alerting appropriate for educational contexts
- Post-deployment verification and rollback procedures
Security and Quality Best Practices
- Secure coding fundamentals: input validation, authentication, and secrets management
- Dependency scanning and vulnerability management in open-source stacks
- License compliance for software used in teaching and publication
- Data privacy considerations when handling student and research data
- Building a security-aware culture in engineering and design programs
Translating Practices into Teaching Modules
- Designing agile project assignments for systems, civil, design, and architecture students
- Creating rubrics that assess process quality alongside product quality
- Setting up template repositories with pre-configured CI for student use
- Scaffolding DevOps concepts progressively across a semester
- Evaluating student teams using real-world quality and automation metrics
Toolchain Selection and Academic Constraints
- Evaluating free and open-source tools for budget-conscious departments
- Integrating with existing LMS, file storage, and lab infrastructure
- Managing technical debt in long-running research codebases
- Onboarding students and faculty with varying technical backgrounds
- Maintaining sustainability when key contributors graduate or rotate
Wymagania
- A basic understanding of software development concepts
- Familiarity with general engineering or design workflows
- Experience using computers for academic or project-based work
Audience
- Professors and lecturers from Systems Engineering, Civil Engineering, Design, and Architecture programs
- Academic staff seeking to modernize their teaching with industry-relevant practices
- Research leads and lab coordinators integrating technology into curriculum
42 godzin
Opinie uczestników (2)
Craig był bardzo zaangażowany w szkolenie, zawsze zapewniał, że jesteśmy skupieni, dostosowywał przykłady do naszej codziennej pracy i zawsze udzielał odpowiedzi na zadane pytania, nawet jeśli informacja nie była zawarta w prezentacji.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Szkolenie - DevOps Foundation®
Przetłumaczone przez sztuczną inteligencję
Wysoki poziom zaangażowania i wiedzy trenera
Jacek - Softsystem
Szkolenie - DevOps Engineering Foundation (DOEF)®
Przetłumaczone przez sztuczną inteligencję
