
*Metrics Collection:
*Response time measurements
*Throughput analysis
*Security scan results
*Code quality scores

*Optimization Strategies:
*Query optimization
*Caching strategies
*Connection pooling
*Asynchronous processing

DAY 2 DELIVERABLES:
*PR: pagination + caching + security fixes
*Security review checklist
*Benchmark notes and metrics
*PR review summary


*Hands-on Lab 2: Pagination Implementation
*Analyze listing endpoint
*Implement cursor-based pagination
*Add query parameter handling
*Create pagination tests
*Validate performance improvements

*Hands-on Lab 3: Caching Layer
*Design in-memory cache strategy
*Implement caching logic
*Add cache invalidation
*Write invalidation tests
*Benchmark performance gains

*Project Task: Security Hardening
*Review auth/validation on API routes
*Implement security enhancements
*Update security documentation
*Create security review checklist
*Create PR: pagination + caching + security


*Setup Implementation:
*Verify Copilot access/license
*Install GitHub Copilot extension
*Enable Copilot Chat
*Configure IDE settings
*Review organization policies

*Security and Governance:
*Data handling policies
*Secure prompting practices
*Avoiding credential exposure
*Managing sensitive information
*Content filtering options


*Complete Development Lifecycle:
*Issue analysis and planning
*Solution design
*Implementation strategy
*Testing approach
*Documentation requirements
*Release preparation

*Best Practices Consolidation:
*Lessons from Days 7-8
*Prompt library organization
*Workflow optimization
*Team collaboration patterns


*Primary Use Cases:
*Real-time code suggestions
*Test generation
*Documentation creation
*Code explanation and learning
*Boilerplate generation
*API integration assistance

*Enterprise Capabilities:
*Organization policy controls
*IP indemnification
*Usage analytics
*Security and compliance features


*Introduction to GitHub Copilot:
*Evolution and development by GitHub/Microsoft
*OpenAI Codex foundation
*Enterprise vs. Individual vs. Business plans
*Integration ecosystem

*Core Capabilities:
*Code completion engine
*Chat interface features
*Context awareness mechanisms
*Multi-language support

*Competitive Landscape:
*Comparison with Amazon CodeWhisperer
*Comparison with Tabnine
*GitHub's market position


*Project Selection:
*Choose from COPILOT-issues.md backlog
*Analyze complexity and scope
*Plan end-to-end approach
*Rolling Lab: Full Cycle Execution
*Issue -> Planning -> Implementation -> Testing -> Documentation -> PR -> Merge
*Continuous Copilot integration
*Regular progress checkpoints
*Demo preparation

*Deliverable Creation:
*Capstone PR with complete documentation
*Final showcase recording
*Enablement package preparation


*Team Presentations:
*Capstone project demos
*Technical deep-dives
*Before/after comparisons
*ROI metrics presentation

*Enablement Pack Creation:
*Prompt library compilation
*Checklist collection

*Best practices documentation
*Troubleshooting guides
*Quick reference cards


*Hands-on Lab 1: Test Generation
*Clone starter repository: repo-copilot-app
*Select API controller
*Generate unit tests via Copilot
*Execute and debug generated tests
*Refine test quality

*Hands-on Lab 2: Code Documentation
*Identify complex function
*Use Chat to explain functionality
*Generate comprehensive docstrings
*Add inline comments
*Create README sections

*Hands-on Lab 3: Endpoint Scaffolding
*Design new endpoint structure
*Use Copilot for implementation
*Scaffold test suite
*Implement basic validation

*Project Task: Metrics Endpoint with Rate Limiting
*Implement /metrics endpoint
*Add rate limiting logic
*Create comprehensive tests
*Document API specifications
*Ensure CI passes
*Create PR


*Effective Copilot Prompting:
*Comment-driven development
*Natural language specifications
*Example-based prompting
*Iterative refinement techniques

*Quality Control:
*Reviewing AI-generated code
*Testing and validation strategies
*Security considerations
*Performance implications
*
*DAY 1 DELIVERABLES:
*PR with /metrics endpoint + tests + docs
*CI green confirmation
*Prompt/response snippets collection
*Commit log
*CI badge screenshot


*Overall Program Retrospective:
*Cross-tool comparisons (Qodo vs. Windsurf vs. Copilot)
*Use case alignment
*Tool selection criteria
*Integration opportunities

*Learning Metrics:
*Productivity improvements
*Code quality enhancements
*Test coverage increases
*Time savings quantification

*Next Steps & Resources:
*Advanced learning paths
*Community resources
*GitHub Copilot updates and roadmap
*Enterprise adoption strategies
*Continuous improvement framework

*Final Q&A and Feedback:
*Open discussion
*Addressing remaining questions
*Program evaluation
*Certificate distribution

*DAY 3 DELIVERABLES:
*Capstone PR
*Final showcase recording
*Enablement pack (complete)
*Final report draft
*Prompt library
*Complete checklist collection
*Training completion certificates


*Hands-on Lab 1: Security-Focused Review
*Analyze vulnerable PR

*Identify security issues:
*Hardcoded secrets
*SQL injection vulnerabilities
*XSS vulnerabilities
*Authentication bypasses
*Apply Copilot-suggested fixes
*Implement security best practices
*Add validation layers

*Secure Coding Patterns:
*Input validation
*Output encoding
*Authentication and authorization
*Secrets management
*Error handling security


*AI-Assisted Review Process:
*Leveraging Copilot for PR reviews
*Identifying code smells
*Security vulnerability detection
*Best practice recommendations

*Review Workflow Integration:
*Setting up automated checks
*Custom review checklists
*Collaborative review processes

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*Introduction to Prompt Engineering
*Overview of Prompt Engineering
*Importance and applications in AI
*Understanding AI Models
*Types of AI models (e.g., GPT, BERT)
*How models interpret prompts
*Crafting Effective Prompts
*Key principles for prompt design
*Techniques for clarity and specificity
*Hands-on practice: Writing basic prompts

 Analyzing Prompt Responses
*Evaluating the effectiveness of prompts
*Techniques for refining prompts based on outputs
*Contextual and Conditional Prompting
*Using context to improve responses
*Conditional prompts for specific tasks
*Hands-on practice: Contextual prompt crafting
*Case Studies and Real-World Applications
*Examining successful prompt engineering examples
*Discussion of industry applications (e.g., chatbots, content generation)

 Ethical Considerations in Prompt Engineering
*Addressing bias and fairness in AI responses
*Best practices for ethical prompt design
*Project Work
*Group project: Developing prompts for a specific application
*Presentations of group projects and peer feedback
* Future Trends in Prompt Engineering
*Emerging technologies and trends in AI
*The evolving role of prompt engineers in AI development


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Master Prompt Engineering | AI Prompting Techniques Course

*Project Task 5: UserService Refactor
*Complete repository pattern implementation
*Ensure all tests pass
*Improve test reliability metrics
*Create changelog entry
*Document architectural changes
*Create PR#5

*Hands-on Lab 1: Cascade Refactor Planning
*Analyze UserService module
*Identify seams and interfaces
*Design repository structure
*Plan dependency injection approach
*Create refactoring checklist

*Hands-on Lab 2: Implementation
*Create repository interfaces
*Implement concrete repositories
*Refactor UserService to use repositories
*Update dependency injection configuration
*Modify existing tests
*Add new tests for repositories


*Feature Selection:
*Review WIN-Feature.md
*Select "Export to CSV" feature
*Analyze requirements and scope
*Rolling Lab: Full Feature Implementation
*Service layer implementation
*CLI command creation
*CSV generation logic
*Error handling and validation
*Comprehensive test suite
*Documentation updates
*Release notes drafting

*Periodic Commits:
*Atomic commit strategy
*Meaningful commit messages
*Branch management


*Deliverable Creation:
*Record feature demo (5-10 minutes)
*Complete reviewer checklist
*Finalize PR#6 documentation
*Prepare CLI help output
*Generate PR review summary

*Team Showcase:
*Feature demonstrations
*Technical deep-dives
*Lessons learned sharing
*Q&A session


*Developer Experience (DX) Focus:
*Documentation quality review
*Script and automation assessment
*Onboarding considerations

*Continuous Improvement:
*Advanced Windsurf features to explore
*Integration with team workflows
*Performance optimization opportunities

*DAY 3 DELIVERABLES:
*Capstone PR#6
*Demo clip
*Reviewer checklist completed
*Release notes draft
*CLI help output
*PR review summary


*Implementing Robust Systems:
*Structured logging patterns
*Error handling strategies
*Observability considerations
*Performance monitoring

*DAY 2 DELIVERABLES:
*PR#5 with refactor + test updates
*Changelog entry
*Refactor checklist completed
*Before/after structure diagram
*Regression safety documentation

*Primary Use Cases:
*Intelligent code planning and architecture
*Multi-file refactoring
*Complex feature implementation
*Debugging and troubleshooting
*Documentation generation

*Cascade AI Capabilities:
*Natural language to code
*Codebase understanding
*Task decomposition
*Dependency tracking
*Test-driven development assistance

*Repository Pattern Introduction:
*Benefits of repository pattern
*Separation of concerns
*Testability improvements
*Dependency injection principles

*Refactoring Strategy:
*Identifying code smells
*Planning refactoring approach
*Maintaining backward compatibility
*Incremental refactoring techniques

*Hands-on Lab 3: Agent-Guided Debugging
*Identify flaky test
*Use Cascade to analyze root cause
*Implement fix with AI guidance
*Verify test stability
*Document debugging process

*Advanced Debugging Techniques:
*Breakpoint strategies
*Log analysis with AI
*Race condition detection
*Memory leak identification

*Hands-on Lab 2: CLI Development with Cascade
*Task: Implement 'report:lint' command
*Use Cascade for planning
*Generate command structure
*Add auto-lint functionality
*Create test hooks
*Verify execution

*Hands-on Lab 3: Multi-File Editing
*Identify files requiring changes
*Use Cascade for coordinated edits
*Maintain consistency across files
*Run tests from IDE terminal
*Debug failures with AI assistance

*Project Task 4: Health-Check Endpoint
*Design health-check API endpoint
*Implement service layer logic
*Create comprehensive tests
*Update README documentation
*Ensure CI pipeline passes

*Setup Implementation:
*Download and install Windsurf IDE
*Authentication and account setup
*Project workspace configuration
*Terminal access verification
*Agent permissions and settings

*Hands-on Lab 1: Environment Familiarization
*Clone starter repository: repo-windsurf-refactor
*Navigate Windsurf interface
*Activate Cascade assistant
*Explore code reading features
*Run initial task breakdown exercise

*Effective Cascade Prompting:
*Structuring complex requests
*Providing context effectively
*Iterative refinement techniques
*Multi-step task management

*IDE Workflow Optimization:
*Keyboard shortcuts and efficiency
*Terminal integration best practices
*File navigation strategies
*Git workflow within Windsurf

*DAY 1 DELIVERABLES:
*PR#4 with health-check endpoint + tests
*Updated README
*Passing CI confirmation
*Cascade prompt log
*PR diff documentation
*Test run screenshots


*End-to-End Feature Planning:
*Requirements analysis
*Component identification
*API design considerations
*Data model planning
*Test strategy definition

*Cascade Orchestration:
*Managing complex multi-file changes
*Maintaining context across sessions
*Iterative development approach
*Code review preparation

*Introduction to Windsurf IDE
*Purpose-built AI-native IDE
*Cascade AI assistant capabilities
*Difference from traditional IDEs
*Integration philosophy

*Core Technology:
*Cascade AI engine architecture
*Multi-file reasoning capabilities
*Context management systems
*Terminal and tooling integration

*Comparison with Other AI IDEs:
*Cursor AI comparison
*VS Code Copilot comparison
*Unique Windsurf advantages

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*Reducing hallucination risks
*Maintaining data accuracy
*Ethical AI usage
*Validation techniques
*Balancing speed and accuracy

*Image generation for business
*No-code app development
*AI agent ecosystem
*Workflow automation strategies

*Task + Objective: Defining precise tasks and clear objectives
*Role + Context: Establishing professional context and background information
*Output Format: Specifying desired response structure and presentation requirements
*Guardrails: Setting explicit boundaries to prevent undesired outputs
*Includes practical exercises like:
*Crafting prompts for various business scenarios
*Analyzing and improving existing prompts
*Hands-on prompt engineering workshops

*Document Management (PDF analysis, report summarization)
*Data Analysis (Excel insights, visual representations)
*Communication Tools (email drafting, multilingual communication)
*Research and Insight Generation (credible information sourcing)

*Chain of Thought Prompting
*Step-Back Prompting
*Zero-Shot vs. Few-Shot Prompting
*Context Optimization
*Covers business application scenarios such as:
*Report generation
*Data analysis
*Email drafting
*Strategic planning
*Research compilation

*Advanced document and data management
*Comprehensive data analysis techniques
*Communication and workflow tool mastery
*In-depth research and insight generation strategies

*Basics of Large Language Models
*Differences between traditional search and AI interaction
*Potential risks like hallucination and digression
*Understanding AI's capabilities and limitations

*Practical prompt engineering test
*Case study analysis
*Individual and group exercises
*Feedback and continuous improvement strategies

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*Introduction: Position CrewAI as an opinionated, higher-level framework for orchestrating role-playing autonomous agents.
*Core Abstractions: 
*Agents: Defined by role, goal, backstory, and tools.
*Tasks: The specific, descriptive assignments for each agent.
*Crews & Processes: How agents collaborate, focusing on Sequential and Hierarchical processes.

*Models: Understanding the difference between LLMs and ChatModels and how to integrate various providers (e.g., OpenAI, Anthropic, open-source models via Ollama).
*Prompts: Mastering PromptTemplates for creating reusable, secure, and effective prompts.
*Output Parsers: A critical component for architects. Learn how to enforce schemas and structure LLM responses into reliable data formats (like JSON or Pydantic models) to ensure clean data contracts within a larger system.
*LCEL (LangChain Expression Language): A deep dive into the | (pipe) syntax for building declarative, streamable, and batch-able data flows.
*Hands-on Lab 1: Building a Production-Ready RAG Pipeline

*The Architect's Choice: A detailed discussion on when to use which framework.
*CrewAI: Ideal for rapid prototyping and use cases that fit a collaborative, role-playing model.
*LangGraph: The choice for ultimate control, complex custom state, fine-grained logic, and integrating human-in-the-loop workflows.
*Hands-on Lab 5: Building a Research Team with CrewAI

*State: The central, persistent memory of the graph. We will explore different ways to model state.
*Nodes: The functions or actions that manipulate the state.
*Edges: The directed connections between nodes, with a focus on Conditional Edges, which enable routing, loops, and complex logic.
*Hands-on Lab 3: Refactoring an Agent into a LangGraph

*Concept: Moving beyond single-shot LLM calls to Agentic AI—systems that can perceive, reason, plan, and act to achieve a goal.
*Architectural Discussion: Contrasting traditional software architectures with agentic architectures. We will cover key challenges such as non-
determinism, state management, security, and observability.
*Introduction to LangChain: Positioning LangChain as a foundational framework for composing LLM applications.

*The ReAct Framework: Deconstruct the core Reason-Act loop that gives an agent its autonomy.
*Tools: Define an agent's capabilities. We will cover how to create custom tools and, most importantly, how to write effective tool descriptions so the LLM can reason about their use.
*Agent Executor: Understand the runtime that orchestrates the agent's interaction with its tools.
*Observability with LangSmith: Introduce LangSmith as an essential tool for debugging and tracing an agent's "thought process." Discuss why this is non-negotiable for production systems.
*Hands-on Lab 2: Building a Research Assistant Agent

*Problem Statement: Discuss the architectural limitations of a basic AgentExecutor. When do you need loops, conditional branching, human-in- the-loop validation, or multi-agent collaboration?
*Conceptual Introduction to LangGraph: Position LangGraph as a library for building robust agentic state machines. It provides the control and flexibility needed for enterprise-grade workflows.

*Multi-Agent Collaboration: Design systems using the Agent Supervisor pattern, where one node acts as a manager that delegates tasks to specialized worker agents (other nodes or sub-graphs).
*Human-in-the-Loop: A critical pattern for safety and validation. Learn how to implement checkpoints in a graph where execution pauses and awaits human input or approval.
*Self-Correction Loops: Build graphs where one agent node critiques or reflects on the output of another, forcing a revision cycle until a quality threshold is met.
*Hands-on Lab 4: Building a Multi-Agent "Plan and Execute" Graph

*Whiteboarding Session: Design a holistic, production-grade architecture for an agentic application.
*Key Components & Concerns:
*API Layer & Task Ingestion: How users interact with the system (e.g., FastAPI, message queues like RabbitMQ or Kafka for async jobs). 
*Orchestration Service: The containerized LangGraph/CrewAI runtime. Discuss scaling strategies.
*State Management: The critical choice between in-memory, Redis, or a relational database for managing graph state, especially for long- running agents.
*Secure Tool Execution: A deep dive into security. Discuss sandboxing tool execution using Docker, WebAssembly (WASM), or serverless functions to mitigate risks.
*Evaluation & Monitoring: How do you test and monitor a non-deterministic system? Introduce evaluation frameworks (e.g., RAGAs) and the concept of a "golden dataset" for regression testing.
*Capstone Project: Design & Build
*Final Wrap-up, Future Trends & Q&A

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*CI/CD Integration Deep Dive
*Automated test execution in pipelines
*Quality gates and thresholds
*Failure analysis and reporting

*Coverage Analysis:
*Interpreting coverage reports
*Identifying untested code paths
*Prioritizing test creation

*DAY 2 DELIVERABLES:
*PR#2 with feature flag + tests
*Coverage report delta
*Prompt templates (spec-first/TDD)
*Refactor checklist
*Backward compatibility notes

*Retrospective:
*What worked well
*Challenges encountered
*Improvements for ongoing use

*Next Steps:
*Continued learning resources
*Community engagement
*Advanced features to explore
*Integration with next tool (Windsurf)

*DAY 3 DELIVERABLES:
*Capstone PR#3
*Before/after metrics report
*Demo recording
*Capstone presentation deck (3 slides)
*Merged PR or sign-off documentation

*Introduction to Qodo (formerly Codium)
*Evolution from Codium to Qodo
*AI-powered code integrity platform overview
*Integration with development workflows
*Privacy and security architecture
*Core Technology Stack
*Large Language Models (LLMs) used in Qodo
*Code analysis engine
*Test generation algorithms
*Integration with Git providers (GitHub, GitLab, Bitbucket)
*Licensing and Enterprise Considerations
*Individual vs. Team vs. Enterprise plans
*Data handling and privacy policies
*On-premise vs. cloud deployment options

*Hands-on Lab 2: AI Code Review Workflow
*Create sample pull request
*Trigger Qodo AI review
*Analyze suggestions and recommendations
*Apply automated fixes
*Review security and best practice alerts

*Hands-on Lab 3: Automated Test Generation
*Select existing module for test coverage
*Generate unit tests using Qodo
*Review and customize generated tests
*Execute test suite
*Analyze coverage reports

*Project Task 1: Fix Failing Tests
*Identify failing tests in baseline
*Use Qodo suggestions to diagnose issues
*Apply AI-recommended fixes
*Improve test coverage
*Create PR: Fix failing tests and improve coverage via Qodo

*Team Presentations:
*Individual capstone demos
*Lessons learned sharing
*Best practices documentation
*Q&A and peer feedback

*Building Organizational Assets:
*Team playbooks
*Prompt libraries
*Review checklists
*Troubleshooting guides

*Project Task 2: Feature Flag with Testing
*Create feature branch: feature/flags
*Implement 'betaMode' feature flag
*Write comprehensive unit tests
*Ensure backward compatibility
*Document migration path
*Create PR#2 with change summary

*Backward Compatibility Strategies:
*A/B testing approaches
*Gradual rollout patterns
*Rollback procedures

*Project Selection:
*Choose issue from QODO-issues.md backlog
*Analyze requirements and acceptance criteria
*Plan implementation approach
*Rolling Lab: Full Cycle Implementation
*Issue -> Design -> Code -> Test -> Review -> PR
*Continuous Qodo integration throughout
*Documentation at each stage
*Regular checkpoint reviews

*Deliverable Creation:
*Capstone PR#3 with full documentation
*Before/after metrics compilation
*Demo recording (5-10 minutes)
*Capstone presentation (3 slides)

*Hands-on Lab 1: Spec-First Prompting
*Write specification for service module
*Generate comprehensive test suite from spec
*Review test coverage across scenarios
*Refine and optimize tests

*Hands-on Lab 2: Cross-Module Testing
*Generate tests across two interdependent modules
*Handle dependencies and mocking
*Ensure integration test coverage
*Validate test isolation

*Primary Use Cases:
*Automated unit test generation
*AI-powered code review
*Test coverage improvement
*Bug detection and code quality enhancement
*Technical debt reduction

*ROI Metrics:
*Time savings in test writing (60-70% reduction)
*Defect detection rate improvements
*Code coverage increase benchmarks
*Developer productivity metrics

*Complete Development Cycle:
*Issue selection and analysis
*Planning and decomposition
*Implementation with AI assistance
*Test generation and validation
*Code review and refinement
*PR creation and merge process

*Quality Metrics Framework:
*Before/after measurements
*Quantifying improvements
*Building business cases for AI tools

*Hands-on Lab 3: Review-Driven Improvements
*Apply Qodo review suggestions
*Enhance error handling patterns
*Implement defensive coding practices
*Strengthen input validation

*Concept Overview:
*Specification-driven test generation
*Test-Driven Development (TDD) with AI assistance
*Creating effective test specifications

*Advanced Prompting Techniques:
*Crafting detailed test scenarios
*Edge case identification
*Boundary condition testing
*Mock and stub generation strategies

*Setup Checklist Implementation:
*IDE extension installation (VS Code, IntelliJ, etc.)
*Git provider authentication
*Repository connection and configuration
*Privacy settings and data governance
*Team workspace setup (if applicable)

*Hands-on Lab 1: Environment Setup
*Clone starter repository: repo-qodo-quality
*Install Qodo extension
*Authenticate with Git provider
*Verify connectivity and permissions
*Run baseline test suite

*Understanding AI-Generated Code Quality
*How to evaluate AI suggestions
*When to accept vs. modify recommendations
*Best practices for prompt engineering with Qodo
*Integration with CI/CD pipelines
*Documentation and Knowledge Sharing
*Creating lab manual entries
*Building team quick-reference guides
*Establishing review protocols

*DAY 1 DELIVERABLES:
*PR#1 merged or reviewed
*Test run logs
*Qodo review report
*Day 1 lab manual
*Qodo quick-reference guide
*Commit history documentation

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Artificial Intelligence (AI) Engineering is a rapidly growing field focused on developing innovative AI solutions that enhance business operations and decision-making processes. With the increasing demand for AI professionals, key skills in AI Engineering include machine learning, data science, and natural language processing (NLP). Our comprehensive, hands-on AI Engineering courses and training cover essential topics such as data preprocessing, model training, and ethical AI practices. By gaining practical experience in building and managing real-world AI applications, you'll be well-prepared for a rewarding career in this transformative industry. Explore our courses today to unlock your potential in AI Engineering!

Artificial Intelligence Engineering

*Validating Job Definitions
*Promoting Jobs to Production Environment
*Scheduling Approvals and Audit Trails
*Assessment: Scenario-Based Exercises + Practical Evaluation

*Viewing and Filtering Active Jobs
*Monitoring Job Status, Dependencies, and Conditions
*Handling Job Failures and Reruns
*Managing Job Outputs and Logs
*Working with Alerts

*Scheduling by Time, Event, or Dependency
*Managing Smart Tables and Groups
*Creating Cyclic and Repetitive Jobs
*Working with Variables and Parameters

*Performing Manual Job Actions (Hold, Rerun, Force, Delete, etc.)
*Using the Monitoring Domain effectively
*Understanding Job Flow Visualization
*Incident Handling Best Practices

*Viewing Logs and Alerts
*Generating Basic Reports
*Basic Troubleshooting Techniques
*Assessment: Hands-on Lab + Quiz

*Control-M Job Lifecycle
*Creating and Editing Job Definitions
*Understanding Calendars, Tables, and Folders
*Setting up Dependencies and Conditions

*Detailed Overview of Control-M/Enterprise Manager
*Control-M/Server and Control-M/Agent Architecture
*Communication and Configuration

*Batch Flow Design Best Practices
*Utilizing Control-M Workload Policy
*Analyzing Workload Impact

*Managing High Availability and Failover
*Configuring Alerts and Notifications
*Security and Audit Controls
*Assessment: Hands-On Lab + Final Evaluation

*Integrating Control-M with Other Tools (e.g., ServiceNow, SAP, AWS)
*Control-M Automation API Overview
*Using Control-M Self Service Portal

*Overview of Workload Automation
*Control-M Architecture and Components
*Understanding the Control-M GUI and Interfaces
*Role of an Operator

*Control-M Logs and Diagnostic Tools
*Handling Performance and Resource Issues
*Backup and Recovery Procedures
*Version Management and Upgrades

*Installing and Configuring Control-M Components
*User & Security Management
*Defining Data Centers and Nodes

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*Classes and objects
*Attributes and methods
*Inheritance and encapsulation

*Types of errors
*Try, except blocks
*Raising and handling exceptions

*math, datetime, random, etc.
*Working with external libraries (intro)

*Mini projects to apply concepts
*Practice tests for certification readiness

*Defining and calling functions
*Function arguments and return values
*Creating and importing modules

*Numbers, strings, lists, tuples, sets, dictionaries
*Type conversion and input/output

*History and features
*Setting up the environment
*Writing your first Python program

*Conditional statements (if, elif, else)
*Loops (for, while)
*Loop control (break, continue, pass)

*Lists, tuples, dictionaries, and sets
*Indexing, slicing, and built-in methods

*Debugging techniques
*Writing simple test cases

*Reading from and writing to files
*Working with different file formats

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*RESTful APIs and JSON
*Authentication and authorization
*Caching and performance optimization
*Testing and debugging

*Build a complete web application using Rails

*Basic syntax and data types
*Control structures and loops
*Classes, objects, and modules
*Error handling and exceptions

*MVC architecture
*Routing and controllers
*Models and database interaction
*Views and templates

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*Setting Up Rational Rhapsody 
*Step-by-step guidance on installation and configuration.

*UML Basics
*Use Case (UC) Diagram
*Activity Diagram
*Class Diagram
*Sequence Diagram
*State Chart Diagram

*Hands-On Exercises
*Exercise 1: Hello World – Build a simple model to understand core concepts.
*Exercise 2: Count Down – Implement a countdown system to explore functionality.

*Understanding Properties 
*Learn how to configure and utilize properties in Rhapsody.

*Practical Applications
*Exercise 3: Dishwasher – Model a basic dishwasher system.
*Exercise 4: Dishwasher System – Expand the model to include system-level interactions.

*Essential Prerequisites 
*Overview of required knowledge and tools for the course.

*Course Agenda 
*Detailed breakdown of the training schedule and objectives.




*Requirements Analysis and Traceability 
*Techniques to capture and trace system requirements.

*Defining Functional Flow 
*Use activity diagrams to map system processes.

*Use Case Scenarios 
*Develop scenarios to define system interactions.

*System Modeling
*Modeling ports and interfaces.
*Modeling system behavior using state machines.

*Model Animation and Validation
*State-Chart animation.
*Animated Sequence and State Chart execution.


*Verification and validation through model execution.

*Architectural Integration
*Merging functional models into an architectural framework.
*Allocating functions to structural components.
*Hand-off of a subsystem to another team or process.

*Reverse Engineering 
*Techniques to reconstruct models from existing code.

*Rational Rhapsody Framework Overview 
*Understand the underlying framework and its applications.

*Generating Reports 
*Create detailed reports for documentation and analysis.

*Testing with Rational Rhapsody TestConductor
*Introduction to TestConductor for automated testing.
*Practical testing workflows.

*Triggered Operations 
*Explore advanced operations triggered by system events.

*Configuration Management Basics 
*Introduction to version control and model management.

*Advanced Exercises
*Stopwatch Exercise – Build and test a stopwatch model.
*Speed Camera Exercise – Model and simulate a speed camera system.

*Best Practices
*Useful tips for efficient modeling.
*Avoiding common mistakes in Rational Rhapsody.

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*Networking
*OSI Layers
*Various Protocols of Networking in Linux
*Linux Firewall

*Kernel Configuration
*Boot Management
*Grub Bootloader configurations
*Red Hat Package Manager
*Libraries

*Process Management
*System Calls
*Bash Operations
*Expect Script
*Python Scripting

*Linux Installations
*Packages
*Architecture
*File System
*User Management
*Scripting Data

*Linux Security Administration
*Shell Scripting
*Securing Shell
*Hands on

*Users in Linux
*User Configuration
*Group Administration
*Sudo
*Default File Permissions
*Access control Lists (ACLs)
*Hands on

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*What is Generic List
*What is Generic Map in Java
*Generic Classes & Methods in Java
*For loop Generic
*Generic Wild Card

*What are Enums?
*Operators in Java
*Modifiers-File Level, Access level and Non-access level
*Data Types, Variables, Literals, Type Conversion, Casting
*Control Statements in Java
*Method and Types of methods
*Variables and Types of Variables
*Constructors and Types of Constructors

*Introduction to Thread
*How to create and start a Thread
*Sleep Thread
*Synchronization in Threads

*Arrays
*Multi-Dimensional Arrays
*Sorting and Searching Arrays
*For-Each

*What are Abstract Classes?
*What are Interfaces?
*Difference between an Abstract Class and Interface

*String Operations and Operators
*String Objects
*Passing by Value
*Immutable Strings

*What is Java?
*Editions and Versions of Java
*Java History
*Installing Java, JDK and JVM
*IDE-Eclipse
*Writing your first java program

*What are Collections?
*List
*Set
*Queue
*Priority Queue
*Maps
*Hashmap

*What are Annotations?
*Why annotations are used
*Built-in Java Annotations
*Custom Annotations
*Placements in Annotations

*What is Object-Oriented Programming?
*Instantiating Objects
*Using Objects
*Creating Object Types
*Default Constructors
*Building Constructors
*What is Class?
*What is Object?
*Class vs Object
*Static Variables
* Static Method
*“this” keyword
*Garbage Collection
*Java Memory Leaks
*Java Packages
*Java Classpath

*What is Exception?
*Types of Exception
*Throw, Throws and Throwable
*Creating Exception
*try-catch block
*Finally block

*Encapsulations
*Access Modifiers
*What is Inheritance?
*Method Overloading
*Method Overriding
*Difference between Method Overloading and Method Overriding
*What is Polymorphism?
*Benefits of Polymorphism
*Run-time and Compile-time Polymorphism

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Oracle Certified Associate, Java SE 8 Program

*Differentiate WLAN architectures
*Describe the stages of the WLAN life cycle

*Explain RF modulation and how it relates to WLAN data rates
*Describe the relationship between SNR and MCS

*Define the features and benefits
*Identify popular use cases
*Categorize the product options
*Summarize the installation
*Review the Edge management
*Troubleshoot the device and connectivity

*Define a SSIDs, BSSIDs, and their functions
*Review additional WLAN configuration options
*Explain WLAN security options and how they are configured in a Juniper Mist WLAN configuration object
*Describe data rates and how they are configured in Juniper Mist
*Explain SSID strategies for multiband deployments

*List Wi-Fi Assurance SLEs and their classifiers

*Describe the purpose of the 802.11 standard and its physical later amendments

*Explain WLAN policies and how they are configured

*Describe the properties of an RF wave
*Convert dBm to Milliwatts using RF math
*Explain factors that contribute to RF signals and how they relate to WLANs

*Describe site, AP, and client events
*Explain the packet capture functionality of the Juniper Mist system
*Describe the 802.11 MAC header and list 802.11 MAC frame types

*Assess Juniper Mist's application of artificial intelligence
*Describe the reactive and proactive troubleshooting methodologies

*Describe the 2.4-GHz, 5-GHz, and 6-GHz frequency bands used for WLANs and their channels

*Explain the difference between organization-level and site-Ievel configuration objects
*Define Juniper Mist configuration objects and their uses

*Describe the 802.11 state machine and steps required for an 802.11 station to connect to an access point
*Explain the protocols used in a client's connection to the network

*Summarize access points and connectivity
*Describe the boot procedure for a Juniper Access Point, its requirements, and the process of adding a Juniper Access Point to the Juniper Mist cloud
*Describe common AP configuration settings
*Use the Juniper Access Points dashboard to get information about an Access Point

*Explain the difference between Marvis natural language and Marvis query language

*Describe WLAN security threats detected by the Juniper Mist WLAN system

*Explain custom dashboard and report options

*Examine the Juniper Mist architecture
*Create a Juniper Mist account
*Summarize Juniper Mist subscriptions
*Summarize the MSP dashboard

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Instructor-led Practical Oriented Training

*Advanced Data Modelling
*Exploring MongoDB Shell & Server
*Using MongoDB Compass

*MongoDB Setup and Installation
*Understanding MongoDB Architecture & Ecosystem

*Introduction to Databases
*SQL vs NoSQL
*Introduction to MongoDB
*JSON vs MongoDB NoSQL

*Basics of MongoDB
*CRUD Operations (Overview)
*Schemas & Relationships

*Best Practices & Use Cases
*Q&A and Recap

*Working with Indexes
*Advanced Index Concepts

*Deep Dive into Query Operations
*Deep Dive into Read Operations
*Deep Dive into Update Operations
*Deep Dive into Delete Operations

*Backup and Repair Operations
*Tools for Managing Large Data Sets
*Deployment Options for MongoDB Applications

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*Modeling Behavior with SysML (1.5 hours): Activity, Sequence, and State Machine Diagrams for Smart Home Security System.
*Project Work Session 1 - Part A (1.5 hours): Initial hands-on modeling using open-source or conceptual tools.

*Project Session 2B: Final Feedback
*Action Plan Creation

*Continued Traceability (1 hour): Automated linking between requirements, activities, and test cases.
*Introduction to SysML (1 hour): Use Case, Block Definition, Activity, and other diagrams; ATM example.
*Modeling Structure with SysML (1.5 hours): BDD and IBD using a Smart Home Security System example.

*Key Takeaways
*Post-Assessment & Survey
*Flexible Deep Dive or 1:1 Consultations

*Ask the Professor
*Model Management Plans & Defined Roles
*MBSE Design Patterns

*Performing an MBSE Critique (2 hours): Critique frameworks and walkthroughs.
*Current Debates & Future Trends (1 hour): Digital Twins, AI/ML in MBSE, interoperability.

*Review & Q&A (1 hour): Clarifying content from Week 1.
*Implementation Challenges of MBSE (2 hours): Real-world challenges, Boeing case study.

*Project Work Session 1 - Part B (2 hours): Refining behavioral models with instructor guidance.
*Graded Activity / Self-Assessment (1 hour): Quiz or exercise for concept reinforcement.

*Integrating MBSE into System Lifecycle
*Agile + MBSE, MBSE Tooling Ecosystem
*Project Session 2A: Critique Application

*Welcome & Course Kick-off (30 min): Course navigation, collaboration tools, learning objectives, and setting expectations.
*MBSE: Beyond Documents (1 hour): Key concepts, SE vs. MBSE, MBSE benefits and use cases.
*Qualities of Excellent Models & Scoping (1 hour): Attributes like accuracy, clarity, completeness; example of drone system.
*Models as Data Repositories & Traceability (1 hour): Models as queryable data sources; safety-critical function traceability example.

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Model-Based Systems Engineering (MBSE) Training

*A Short History of Web Application Development
*SPA Technologies
*Creating a React App
*React Basics
*The Home Page
*Routing
*Vacations Page—Visual Design
*Vacations Page—Server Integration Sending Information to the Server State Management
*Deployment Options

*The JavaScript Revolution
*Introducing Express
*Server-Side and Client-Side Applications
*A Brief History of Express
*Node: A New Kind of Web Server
*The Node Ecosystem

*Middleware Principles
*Middleware Examples
*Common Middleware
*Third-Party Middleware

*Sending Client Data to the Server
*HTML Forms
*Encoding
*Different Approaches to Form Handling
*Form Handling with Express
*Using Fetch to Send Form Data
*File Uploads
*File Uploads with Fetch
*Improving File Upload UI

*Scaffolding
*The Meadowlark Travel Website
*Initial Steps
*Views and Layouts
*Static Files and Views
*Dynamic Content in Views
*Getting Organized
*File and Directory Structure
*Best Practices Version Control npm Packages
*Project Metadata
*Node Modules

*Choosing a Template Engine
*Pug: A Different Approach
*Handlebars Basics
*Comments
*Blocks
*Server-Side Templates
*Views and Layouts
*Using Layouts (or Not) in Express
*Sections
*Partials
*Perfecting Your Templates

*Execution Environments
*Environment-Specific Configuration
*Running Your Node Process
*Scaling Your Website
*Scaling Out with App Clusters
*Handling Uncaught Exceptions
*Scaling Out with Multiple Servers
*Monitoring Your Website
*Third-Party Uptime Monitors
*Stress Testing

*Social Media
*Social Media Plugins and Site Performance
*Searching for Tweets
*Rendering Tweets
*Geocoding
*Geocoding with Google
*Geocoding Your Data
*Displaying a Map
*Weather Data

*The QA Plan
*Is It Worth It?
*Logic Versus Presentation
*The Types of Tests
*Overview of QA Techniques
*Installing and Configuring Jest
*Unit Testing
*Mocking
*Refactoring the Application for Testability
*Writing Our First Test
*Test Maintenance
*Code Coverage
*Integration Testing
*Linting
*Continuous Integration

*Performance Considerations
*Content Delivery Networks
*Designing for CDNs
*Server-Rendered Website
*Single-Page Applications
*Caching Static Assets
*Changing Your Static Content

*Filesystem Persistence
*Cloud Persistence
*Database Persistence
*A Note on Performance
*Abstracting the Database Layer
*Setting Up MongoDB
*Mongoose
*Database Connections with Mongoose
*Creating Schemas and Models
*Seeding Initial Data
*Retrieving Data
*Adding Data
*PostgreSQL
*Adding Data
*Using a Database for Session Storage

*The Principles of Maintenance
*Have a Longevity Plan
*Use Source Control
*Use an Issue Tracker
*Exercise Good Hygiene
*Don’t Procrastinate
*Do Routine QA Checks
*Monitor Analytics
*Optimize Performance
*Prioritize Lead Tracking
*Prevent “Invisible” Failures
*Code Reuse and Refactoring
*Private npm Registry
*Middleware

*The First Principle of Debugging
*Take Advantage of REPL and the Console
*Using Node’s Built-in Debugger
*Node Inspector Clients
*Debugging Asynchronous Functions
*Debugging Express

*Externalizing Credentials
*Cookies in Express
*Examining Cookies
*Sessions
*Memory Stores
*Using Sessions
*Using Sessions to Implement Flash Messages
*What to Use Sessions For

*The Parts of a URL
*HTTP Request Methods
*Request Headers
*Response Headers
*Internet Media Types
*Request Body
*The Request Object
*The Response Object
*Getting More Information
*Boiling It Down
*Rendering Content
*Processing Forms
*Providing an API

*Routes and SEO
*Subdomains
*Route Handlers Are Middleware
*Route Paths and Regular Expressions
*Route Parameters
*Organizing Routes
*Declaring Routes in a Module
*Grouping Handlers Logically
*Automatically Rendering Views

*HTTPS
*Generating Your Own Certificate
*Using a Free Certificate Authority
*Purchasing a Certificate
*Enabling HTTPS for Your Express App
*A Note on Ports
*HTTPS and Proxies
*Cross-Site Request Forgery
*Authentication
*Authentication Versus Authorization
*The Problem with Passwords
*Third-Party Authentication
*Storing Users in Your Database
*Authentication Versus Registration and the User Experience
*Passport
*Role-Based Authorization
*Adding Authentication Providers

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Software development is a multifaceted journey that demands meticulous planning, creative design, expert coding, rigorous testing, and ongoing maintenance. It's a dynamic process that aims to deliver websites that not only meet but exceed user expectations, making it an indispensable component of today's digital landscape.

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“ The hands-on projects helped our team put theory into practice. Thanks to this training, we've achieved seamless collaboration, faster releases, and a more resilient infrastructure. ”