Pybites Python Certification Assessment Criteria

Core Concepts

• Fundamental terms and definitions (e.g., compilation vs. interpretation).
  • Python’s logic and structure (e.g., loops, conditional statements).
  • Keywords
  • Instructions
  • Indentation
•  Understanding of literals, variables, and numeral systems.
•  Familiarity with operators and data types.

Developer Toolkit Proficiency

•  Use of linters and auto-formatters.
• Setting up and navigating IDEs.
  •  Version Control: Basic Git commands
  •  Using platforms like GitHub or GitLab
  •  Collaborative practices: code reviews, pull requests
• Basic prompt engineering skills (ChatGPT / Copilot).

Programming Essentials

• Execution of I/O operations.
  •  Mastery of control flow mechanisms: Conditional blocks
  •  Loops (for, while)
  •  Data collections handling: Lists and tuples
  •  Dictionaries
  •  Strings

Working With Functions And Classes

• Decomposition and function creation.
• Using built-in functions effectively.
• Designing user-defined functions.
• Define a simple class (and when to use functions vs classes).
• Interaction between functions and their environment.
  •  Advanced function concepts: Generators

Debugging & Code Quality

• Basic debugging techniques.
• Writing maintainable code.
• Basic refactoring skills.

Integration & Deployment

• Working with virtual environments and dependency management (optionally use a package manager like Conda, Poetry, Mamba, or Pixi)
• Integrating with external libraries and/or third-party APIs.
• Knowledge of deployment strategies and considerations.

Collaboration & Methodologies

• Introduction to Agile methodologies and its applications.
• Team communication and collaboration skills.

Exception Handling

• Basic exception handling techniques.
• Understanding of exception hierarchies.

Real-world Application

• Completion at least one capstone project or equivalent hands-on task.
• Code review feedback incorporation.

Advanced Python Mastery

• Foundational Knowledge: Deep understanding of Python, covering both fundamentals and advanced techniques:
  • Collections, iterators, generators, context managers, decorators, magic methods, OOP, and exception handling.
• Python Standard Library: Effective use of built-in functions and modules to streamline tasks.
• Application Design: Adhering to Pythonic patterns and best practices.
• Code Quality: Writing clean, maintainable code with an emphasis on readability, including use of type annotations.
• Testing: Incorporate test coverage using the pytest framework (using fixtures and parametrization, knowledge of TDD and mocking).
• Debugging: Demonstrable skills in identifying and resolving issues, with proficiency in tools like pdb or IDE standard debugger.
• Code Organization: Properly structuring code into modules and packages for modularity and scalability.
• Design patterns: Knowledge of common Python design patterns.

Software Development Best Practices

• Python Development Tools Mastery:
  • Virtual environments: Managing isolated Python environments (Python’s venv or a package manager like Conda, Poetry, Mamba, or Pixi).
  • Package management: Manage project requirements and dependencies (pip / pip-tools / package manager).
  • Linters: Ensuring code quality and adherence to conventions (e.g., flake8, Black, isort, ruff, mypy). Ideally automated via pre-commit.
  • Type checking: How to use type hints and how to validate them (e.g. using Mypy).
  • CI/CD: Automating build, test, and deployment processes (e.g., GitHub Actions, GitLab CI/CD).
• Advanced Software Development Techniques:
  • Technological adaptability: Evaluating and rapidly integrating new frameworks and PyPI libraries (e.g., Flask, Streamlit, Django, FastAPI, Typer, Pydantic, requests, Spacy, Pandas, Pulumi, Marvin AI, etc.)
  • Databases and ORMs: Understanding data storage, retrieval, and object-relational mapping (e.g., sqlmodel/SQLAlchemy).
  • AI Tools: Proficiency with AI-enhanced coding tools like ChatGPT, (GitHub) Copilot, etc to streamline the development process (bonus if you’ve used their APIs/ built your own AI app, e.g. using Marvin AI).

Deployment and Containerization

• Application Packaging and Hosting:
  • Understanding of packaging Python libraries and applications for distribution via PyPI.
  • Hosting and deploying applications on cloud platforms (e.g., AWS, GCP, Fly, Render, Heroku, etc).
• Containerization:
  • Building and managing application containers using Docker and orchestrating them with Docker Compose.
  • Introduction to container orchestration with Kubernetes (optional).

Version Control, Collaboration, and Soft Skills

• Version Control Mastery:
  • Proficient use of Git: Understanding of common commands, branching strategies, and resolving merge conflicts.
  • Platforms for code hosting and collaboration: Proficiency in platforms like GitHub or GitLab and release management.
  • Engage in the code reviewing process: Ability to give constructive feedback, understand pull/merge requests, and integrate code changes.
• Collaboration and Soft Skills:
  • Effective team communication: Ability to articulate technical concepts to non-technical stakeholders and facilitate discussions.
  • Emphasis on collaborative coding: Pair programming, code reviews, and group problem-solving sessions.
  • Open Source: Understanding open source contributions, and community engagement.
  • Problem-solving and critical thinking: Tackling complex technical challenges and coming up with efficient solutions.
  • Adaptability: Ability to rapidly adapt to changing requirements or feedback.
  • Experience with collaborative tools like issues, milestones, project boards, and mind mapping to manage tasks, scoping and priorities.
  • Time management: Prioritizing tasks, setting milestones, and managing individual and team deadlines.