Welcome to the "EDA and Statistics" repository! This repository is a comprehensive collection of Jupyter notebooks and Python scripts that focus on Exploratory Data Analysis (EDA) and statistical techniques. It serves as a valuable resource for anyone looking to deepen their understanding of data analysis, visualization, and statistical methods using Python/R.
Exploratory Data Analysis (EDA) and statistics are fundamental components of the data science process. EDA helps you understand data, uncover patterns, detect anomalies, and form hypotheses, while statistical analysis provides the tools to test these hypotheses. This repository contains code and examples that demonstrate these concepts in a clear and practical manner.
The repository is organized as follows:
- Exploratory Data Analysis: Jupyter notebooks and scripts that cover various EDA techniques, such as data cleaning, visualization, and summary statistics.
- Statistics: Notebooks and code that focus on key statistical methods, including hypothesis testing, regression analysis, and probability distributions.
The required Python libraries for each project are listed in their respective folders. Please refer to the requirements.txt or installation instructions within each project folder for specific dependencies.
You can explore the various EDA and statistical analysis techniques by running the Jupyter notebooks provided in this repository. Each notebook includes explanations and examples that will help you understand and apply the methods.
Contributions are welcome! If you have ideas for improvement or additional examples, feel free to fork the repository and submit a pull request.