This repository contains code and resources related to statistical data analysis and visualization. It covers a wide range of topics including descriptive statistics, exploratory data analysis (EDA), inferential statistics, and various visualization techniques using Python.
In this repository, you will find:
- Jupyter Notebooks demonstrating different statistical analysis techniques.
- Python scripts for data preprocessing, analysis, and visualization.
- Sample datasets for practicing statistical analysis.
- Documentation and tutorials on how to perform various statistical analyses.
- Examples of data visualization using popular libraries such as Matplotlib and Seaborn.
- Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation, etc.
- Exploratory Data Analysis (EDA): Data Cleaning, Handling Missing Values, Outlier Detection, etc.
- Inferential Statistics: Hypothesis Testing, Confidence Intervals, Regression Analysis, etc.
- Data Visualization: Scatter Plots, Histograms, Box Plots, Heatmaps, Pair Plots, etc.
To run the code examples and notebooks in this repository, you will need:
- Python 3.x
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
- (Additional libraries may be required for specific examples)
- Clone this repository to your local machine using
git clone. - Install the required dependencies using
pip install -r requirements.txt. - Explore the Jupyter Notebooks and Python scripts in the repository.
- Use the sample datasets provided to practice different statistical analyses and visualization techniques.
- Refer to the documentation and tutorials for detailed explanations of each topic.
Contributions to this repository are welcome! If you have any suggestions, bug fixes, or new examples to add, please feel free to open an issue or create a pull request.