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Data Science Repository

Welcome to my personal data science repository! This repository contains Jupyter Notebook files focusing on fundamental data manipulation and analysis libraries, including NumPy and pandas. Here, you will find notebooks that explore the capabilities and usage of these libraries to perform various data-related tasks. Additionally, this repository will be regularly updated to include machine learning tutorials, expanding the scope to cover advanced data science concepts and techniques in the future. Stay tuned for upcoming tutorials on machine learning algorithms, model evaluation, and more!

Table of Contents

Notebooks

This section highlights the notebooks available in this repository:

  • NumPy Basics:
    This notebook provides an introduction to the essential features and functionalities of NumPy, a powerful library for numerical computing in Python. It covers topics such as array creation, indexing, slicing, mathematical operations, and more.

  • Pandas Introduction:
    This notebook serves as an introduction to pandas, a widely-used library for data manipulation and analysis. It explores pandas' key data structures, including Series and DataFrame, and demonstrates various operations such as data loading, cleaning, filtering, and basic data exploration.

Feel free to explore these notebooks to deepen your understanding of NumPy and pandas and enhance your data manipulation and analysis skills.

Getting Started

To run the notebooks locally, follow these steps:

  • Clone this repository to your local machine.
  • Install the necessary dependencies, including Python, Jupyter Notebook, NumPy, and pandas.
  • Launch Jupyter Notebook.
  • Navigate to the cloned repository and open the desired notebook (numpy_basics.ipynb or pandas_introduction.ipynb).
  • Execute the code cells in the notebook to interact with the examples and learn more about NumPy and pandas. Feel free to modify the notebooks, experiment with different scenarios, and expand your knowledge of these libraries.

Contributing

Although this is my personal data science repository, I appreciate suggestions and contributions that can enhance the quality and content of the notebooks. If you have any ideas, improvements, or bug fixes, please feel free to submit a pull request. Your contributions are valuable and will be acknowledged.