• 📋 About This Repository
• 🏗️ Repository Structure
• ⚙️ Prerequisites
• 🚀 Getting Started
• 📚 Topics Covered
• 🤝 Contributing
• 📜 License

Welcome to the ultimate Python for Data Science learning repository! This comprehensive collection of Jupyter notebooks covers everything from basic Python programming to advanced data analysis, visualization, machine learning, and data structures & algorithms. Whether you're a complete beginner or looking to upskill, this repo has something for you!

Python-For-DataScience/
├── 01. Python Basics/
├── 02. Loops and Functions/
├── 03. Data Structures/
├── 04. Exception & File Handling/
├── 05. Numpy/
├── 06. Pandas/
├── 07. Capstone Project (Predictive Analysis)/
├── 08. MatPlotLib/
├── 10. Exploratory Analysis and Visualization of MovieLens Dataset/
├── 12. OOP's/
└── 13. DSA/

To run these notebooks locally, you'll need:

• Python 3.7 or higher
• Jupyter Notebook or JupyterLab

git clone https://github.com/Khanz9664/Python-For-DataScience.git
cd Python-For-DataScience

pip install jupyterlab numpy pandas matplotlib

Option 1: Jupyter Notebook

jupyter notebook

Option 2: JupyterLab (Recommended)

jupyter lab

• Basic Python syntax
• Variables and data types
• Operators and expressions
• Hands-on practice problems

• For loops and while loops
• Function definitions and calls
• Parameters and return values
• Lambda functions
• Decorators
• Comprehensive function challenges

• Lists: Creation, indexing, slicing, methods
• Tuples: Immutability, operations
• Sets: Unique elements, set operations
• Dictionaries: Key-value pairs, methods
• Strings: Manipulation, formatting
• Comprehensions: List, dict, set comprehensions

• Try-except blocks and error handling
• File I/O operations
• Working with CSV, JSON, and binary files
• Logging and debugging

• Numpy arrays and operations
• Array indexing and slicing
• Mathematical operations
• Linear algebra
• Image manipulation with Numpy

• DataFrames and Series
• Data cleaning and manipulation
• Merging and joining datasets
• Groupby operations
• Working with real-world datasets

• End-to-end predictive modeling project
• Data preprocessing
• Model building and evaluation

• Data visualization fundamentals
• Line plots, bar charts, histograms
• Scatter plots and heatmaps
• Customizing plots

• Exploratory Data Analysis (EDA)
• Visualization of movie ratings data
• Complete project with documentation

• Classes and objects
• Inheritance and polymorphism
• Encapsulation
• Real-world OOP implementations

• Arrays & Lists: Searching and sorting algorithms
  • Linear Search
  • Binary Search
  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • Merge Sort
  • Quick Sort
• Strings: String operations and algorithms
• Practice Problems: 19+ hands-on coding challenges

Contributions are welcome! Feel free to:

• Open issues for suggestions or bugs
• Submit pull requests with improvements
• Add new notebooks or enhance existing ones

This repository is open for learning and exploration.

Engineering Design © 2026 Shahid Ul Islam.
Built with passion for Mathematical Rigor and Technical Excellence.