Python SCMS - Lecture Codes and Solutions

📚 Purpose

This repository serves as a comprehensive collection of all Python codes, solutions, and topics covered during our SCMS (Scientific Computing Modelling and Simulations) Python lectures. It's designed to be a reference hub for students to access, review, and practice the concepts discussed in class.

🎯 What You'll Find Here

This repository contains:

• Lecture Code Examples: All Python code snippets and examples demonstrated during lectures
• Problem Solutions: Step-by-step solutions to problems discussed and solved in class
• Topic Coverage: Comprehensive coverage of Python concepts relevant to SCMS applications
• Practice Materials: Additional exercises and examples for hands-on learning

📁 Repository Structure

📓 Jupyter Notebooks (Currently Available)

• python_tutorial.ipynb - Comprehensive tutorial notebook covering Python fundamentals from basics to functions, including:
  • Variables and data types
  • Basic operations and input/output
  • Strings, lists, tuples, and dictionaries
  • Control flow (conditionals and loops)
  • Functions and practical examples
• problems.ipynb - Collection of solved problems and exercises from class discussions, featuring:
  • Level 1: Basic problems with data types and conditionals
  • Level 2: List and tuple operations
  • Level 3: Dictionary manipulations
  • Advanced challenges including LeetCode-style problems

🔧 Topics Covered

Based on the lecture content, this repository covers:

• Python Fundamentals

  • Variables and Data Types
  • Input/Output Operations
  • Type Conversion and Casting

• Programming Concepts

  • Problem-solving approaches
  • Code organization and structure
  • Best practices in Python programming

• SCMS Applications

  • Python applications in Scientific Computing
  • Mathematical modeling and simulations
  • Data handling and computational problem-solving
  • Algorithm implementation and optimization

🚀 How to Use This Repository

1. Start with Fundamentals: Begin with python_tutorial.ipynb to learn Python basics from variables to functions
2. Practice with Problems: Use problems.ipynb to practice solved examples and try similar problems
3. Progressive Learning: The tutorial covers concepts step-by-step with hands-on examples and exercises
4. Run the Code: Clone the repository and run the Jupyter notebooks locally to experiment with the code
5. Solve Exercises: Try the practice problems included in both notebooks to reinforce your learning

💡 Learning Approach

• Each notebook contains both code examples and explanations
• Problems are solved step-by-step with clear reasoning
• Code is well-commented for better understanding
• Examples progress from basic to advanced concepts

🤝 Contributing

This repository is maintained as a class resource. If you find any issues or have suggestions for improvements, feel free to discuss them during our lectures.

📞 Support

For questions about the code or concepts covered in this repository, please bring them up during our regular class sessions.

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Happy Learning! 🐍📊

This repository is part of the SCMS (Scientific Computing Modelling and Simulations) Python programming course and is regularly updated with new content from our lectures.