Author: Najeeb Ullah(烏奈吉) , Master’s Student — Smart Healthcare Management National Taipei University, Taiwan
- Python Programming Assignments
1.1. This repository contains my Python programming assignments and practical exercises completed as part of my computer programming coursework.
1.2 The collection demonstrates the implementation of fundamental programming concepts, structured problem solving, and introductory data analysis using Python.
1.3 The work reflects my progressive learning and hands-on experience in software development, computational thinking, and data handling techniques.
1.4 Repository Objectives
1.4. Practice core Python programming concepts
1.6 Develop logical and algorithmic problem-solving skills
1.7. Apply programming knowledge to structured assignments
1.8. Explore basic data analysis using scientific Python libraries
- Contents
2.1. The repository includes the following topics:
2.2. Python basics and practice exercises
2.3. Boolean logic and expressions
2.4. Input and output operations
2.5. Data structures and containers
2.6. Function scope (global and local variables)
2.7. Conditional statements and loops
- Python modules
3.1. File handling and CSV processing
3.2. Numerical computing with NumPy
3.3. Data manipulation with Pandas
3.4. Pandas Series and mapping methods
- Skills Demonstrated
4.1. Programming fundamentals
4.2. Problem-solving and logical reasoning
4.3. File input/output operations
4.4. Data structure manipulation
4.5. Scientific computing
4.6. Data analysis with Python
- Technologies Used
5.1. Python
5.2. NumPy
5.3. Pandas
5.4. Jupyter Notebook
- Academic Context
5.1 These assignments were completed as part of computer programming coursework to build foundational programming knowledge and practical coding skills.
- Learning Outcome
6.1. Through these assignments, I developed:
6.2. Strong understanding of Python syntax and logic
6.3. Ability to structure and solve computational problems
6.4. Experience handling structured data programmatically
6.5. Practical exposure to data analysis tools
- Future Improvements
7.1. Add more advanced Python projects
7.2. Implement machine learning examples
7.3. Include visualization and data analysis case studies
- How to Use
8.1. Open any notebook (.ipynb) file to view code and outputs
8.2. Run cells sequentially in Jupyter Notebook
8.3. Install required libraries if needed
8.4. Run cells sequentially in Jupyter Notebook
8.5. Install required libraries if needed