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Python Skills Outline

· 2 min read
Kyeongsup Choi

1. Beginner​

Skills:​

  • Basic understanding of Python syntax and data structures (lists, tuples, dictionaries, sets).
  • Ability to write simple programs using variables, loops, conditionals, and functions.
  • Understanding basic concepts like input/output, string manipulation, and basic error handling.
  • Limited experience with libraries (e.g., math, random).

Example Tasks:​

  • Writing a program to print Fibonacci numbers.
  • Simple file handling (e.g., reading and writing text files).
  • Using loops to iterate over data structures.

2. Intermediate​

Skills:​

  • Deeper understanding of data structures and algorithms.
  • Familiarity with object-oriented programming (OOP) principles: classes, inheritance, polymorphism, encapsulation.
  • Ability to use third-party libraries and frameworks (e.g., Pandas, NumPy, Flask).
  • Understanding of error handling using exceptions.
  • Familiarity with modules, packages, and Python's standard library.

Example Tasks:​

  • Writing a web scraper using libraries like BeautifulSoup or Scrapy.
  • Creating a simple web application using Flask or Django.
  • Data manipulation and analysis using Pandas and NumPy.
  • Implementing algorithms like sorting or searching.

3. Advanced​

Skills:​

  • Proficient in working with complex data structures (e.g., generators, iterators).
  • Expert in OOP, design patterns, and advanced Python concepts (e.g., decorators, context managers).
  • Understanding concurrency and parallelism (using threading, multiprocessing, async/await).
  • Proficiency in performance optimization (e.g., time complexity, memory usage).
  • Experience with debugging, testing (unit tests, integration tests), and version control (e.g., Git).

Example Tasks:​

  • Developing a large-scale application with efficient data handling.
  • Building and maintaining APIs with complex architectures.
  • Writing unit tests and utilizing continuous integration (CI/CD).
  • Implementing machine learning models with libraries like TensorFlow or PyTorch.

4. Expert​

Skills:​

  • Mastery of Python internals, such as memory management, garbage collection, and bytecode.
  • Ability to contribute to Python core development or design custom libraries and tools.
  • Deep understanding of multithreading, asynchronous programming, and distributed systems.
  • Familiarity with low-level programming concepts (e.g., interfacing Python with C/C++).
  • Knowledge of various domains such as web development, machine learning, automation, data science, and scripting.

Example Tasks:​

  • Designing complex, scalable systems and APIs for production.
  • Implementing and optimizing large-scale machine learning pipelines.
  • Contributing to open-source Python projects or writing custom Python extensions.