OBJECT-ORIENTED PYTHON

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Best way to prepare for Python interviews 👇👇

1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.

2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.

3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.

4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.

5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.

6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.

7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.

8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.

9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.

10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.

Best Resource to learn Python

Python Interview Questions with Answers

Freecodecamp Python Course with FREE Certificate

Python for Data Analysis and Visualization

Python course for beginners by Microsoft

Python course by Google

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ENJOY LEARNING 👍👍

Foundations of Computer Vision
James F. Peters, 2002