🐍 Python Data Science Practice

Welcome to my Python Data Science Practice Repository.

This repository contains my practice work, mini-projects, notes, and exercises while learning Python for Data Science. The goal is to build strong programming skills and develop a solid foundation in Python before moving toward advanced Data Science topics.

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📌 About This Repository

This repository documents my learning journey in Python programming for Data Science.

It includes:

• Python mini-projects
• Python practice programs
• Python libraries practice
• Personal learning notes

These resources help me improve problem-solving skills, programming fundamentals, and practical coding experience.

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📂 Repository Structure

python-data-science-practice
│
├── python-projects
│   ├── automated-web-opener-controller
│   ├── chat-bot
│   ├── guess-the-number
│   ├── jarvis-ai-assistant
│   ├── password-generator
│   ├── railway-announcement-system
│   ├── smart-email-assistant
│   ├── snake-water-gun
│   ├── third-umpire-drs-system
│   └── tic-tac-toe-game
│
├── python-practice-files
│   ├── advanced-python
│   ├── conditional-expressions
│   ├── dictionary-and-tuples
│   ├── file-input-output
│   ├── functions-recurtions
│   ├── inheritence-and-more-on-oops
│   ├── modules comments & pip
│   ├── lists-and-tuples
│   ├── loops-in-python
│   ├── modules comments & pip
│   ├── oops
│   ├── strings
│   └── variables-and-datatypes
│
├── libraries-practice-notebooks
│   ├── numpy-practice.ipynb
│   ├── pandas-practice.ipynb
│   ├── matplotlib-practice.ipynb
│   └── seaborn-practice.ipynb
│
└── README.md

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🧠 Skills Practiced

Through these projects and practice files, I work on improving my understanding of:

• Python Fundamentals
• Conditional Statements
• Loops and Functions
• Object-Oriented Programming
• Random Module
• File Handling
• Automation with Python
• Logic Building and Problem Solving

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🛠 Technologies Used

• Python 3
• Python Standard Library
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook
• Command Line Interface (CLI)

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🚀 Data Science Learning Roadmap

My structured roadmap for learning Data Science is:

1. Python Programming

2. Python Libraries

   • NumPy
   • Pandas
   • Matplotlib
   • Seaborn

3. Statistics

4. Exploratory Data Analysis (EDA)

5. Feature Engineering

6. SQL for Data Analysis

7. Machine Learning

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👨‍💻 Author

Anurag Gupta

Aspiring Data Scientist currently focusing on the following learning roadmap:

• Python Programming
• Python Libraries
• Statistics
• Exploratory Data Analysis (EDA)
• Feature Engineering
• SQL for Data Analysis
• Machine Learning

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⭐ This repository represents my learning journey in Python programming for Data Science.