Data Science Learning - Repository

Welcome to my Data Science learning repository! This repository contains various notebooks, notes, and resources I've compiled while learning data science concepts, Python libraries, and statistical methods.

Contents

Jupyter Notebooks

• Python.ipynb - Core Python programming concepts and fundamentals
• NumPy.ipynb - Numerical computing with NumPy arrays and operations
• Pandas.ipynb - Data manipulation and analysis using Pandas
• Matplotlib_Seaborn_Plotly_Cufflinks.ipynb - Data visualization techniques with multiple libraries:
  • Matplotlib for basic plotting
  • Seaborn for statistical visualizations
  • Plotly for interactive plots
  • Cufflinks for Pandas integration with Plotly
• Stats_College_1.ipynb - Statistical concepts and college-level statistics

Documents & Resources

• College_Exam.md - College exam notes and study materials
• clt_lln_observation.pdf - Observations on Central Limit Theorem (CLT) and Law of Large Numbers (LLN)
• Initial Public Offering.xlsx - IPO data analysis spreadsheet

Technologies & Libraries

This repository covers the following Python libraries and tools:

• Python - Core programming language
• NumPy - Numerical computing library
• Pandas - Data manipulation and analysis
• Matplotlib - Static data visualization
• Seaborn - Statistical data visualization
• Plotly - Interactive plotting library
• Cufflinks - Connects Pandas with Plotly

Getting Started

Prerequisites

Make sure you have Python installed (Python 3.7+ recommended). You'll also need Jupyter Notebook or JupyterLab to run the notebooks.

Installation

1. Clone this repository:

   git clone https://github.com/ShardenduMishra22/Data-Science.git
   cd Data-Science

2. Install required dependencies:

   pip install numpy pandas matplotlib seaborn plotly cufflinks jupyter

3. Launch Jupyter Notebook:

   jupyter notebook

Learning Path

Recommended order for going through the notebooks:

1. Python.ipynb - Start with Python basics
2. NumPy.ipynb - Learn numerical computing
3. Pandas.ipynb - Master data manipulation
4. Matplotlib_Seaborn_Plotly_Cufflinks.ipynb - Explore data visualization
5. Stats_College_1.ipynb - Apply statistical concepts

Topics Covered

• Python programming fundamentals
• Array operations and numerical computing
• Data cleaning and preprocessing
• Data analysis and transformation
• Statistical analysis
• Data visualization techniques
• Central Limit Theorem and Law of Large Numbers
• Real-world data applications

Contributing

This is a personal learning repository, but suggestions and improvements are welcome! Feel free to open an issue or submit a pull request.

License

This project is open source and available for educational purposes.

Contact

Shardendu Mishra

• GitHub: @ShardenduMishra22

---

Last Updated: October 2025