{"id":25870814,"url":"https://github.com/sroman0/data-analytics","last_synced_at":"2026-04-15T20:31:21.063Z","repository":{"id":247558972,"uuid":"805715403","full_name":"sroman0/Data-analytics","owner":"sroman0","description":"Data Analytics Exercises is a collection of comprehensive university-level exercises aimed at enhancing skills in data analytics. 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The exercises are designed to enhance understanding and proficiency in various data analysis techniques and tools.\n\n## Table of Contents\n\n- [Project Overview](#project-overview)\n- [Features](#features)\n- [Getting Started](#getting-started)\n  - [Prerequisites](#prerequisites)\n  - [Installation](#installation)\n  - [Running the Exercises](#running-the-exercises)\n- [Project Structure](#project-structure)\n- [Contributing](#contributing)\n- [Authors](#authors)\n- [License](#license)\n\n## Project Overview\n\nThe exercises in this repository cover a range of topics within data analytics, including data manipulation, statistical analysis, data visualization, and more. They are intended to provide hands-on experience with real-world datasets and scenarios, facilitating the development of practical skills in data analysis.\n\n## Features\n\n- **Diverse Topics**: Exercises encompass various aspects of data analytics, from basic data manipulation to advanced statistical modeling.\n- **Real-World Datasets**: Engage with authentic datasets to simulate real-world data analysis challenges.\n- **Incremental Complexity**: Exercises are structured to progressively increase in complexity, catering to both beginners and advanced learners.\n- **Hands-On Learning**: Focused on practical applications rather than just theoretical concepts.\n- **Code Examples**: Each exercise includes commented code to guide you through the solution process.\n\n## Getting Started\n\nTo begin working with these exercises, follow the instructions below.\n\n### Prerequisites\n\nEnsure you have the following software installed:\n\n- [Python](https://www.python.org/downloads/) (version 3.6 or higher)\n- [Jupyter Notebook](https://jupyter.org/install) or [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html)\n- [pip](https://pip.pypa.io/en/stable/installation/) (Python package installer)\n- (Optional) [virtualenv](https://virtualenv.pypa.io/en/stable/) for creating isolated Python environments\n\n### Installation\n\n1. **Clone the Repository**: Clone this repository to your local machine using the following command:\n\n   ```bash\n   git clone https://github.com/sroman0/Data-analytics.git\n   ```\n\n2. **Navigate to the Directory**: Change into the project directory:\n\n   ```bash\n   cd Data-analytics\n   ```\n\n3. **Create a Virtual Environment (Optional but Recommended)**:\n\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n   ```\n\n### Running the Exercises\n\nTo run the exercises:\n\n1. **Launch Jupyter Notebook**: Start the Jupyter Notebook server:\n\n   ```bash\n   jupyter notebook\n   ```\n\n2. **Open an Exercise**: In the Jupyter interface, navigate to the desired exercise notebook and open it.\n\n3. **Execute the Notebook**: Follow the instructions within the notebook, executing each cell sequentially.\n\n4. **Modify and Experiment**: Feel free to modify the code to better understand the concepts and experiment with different datasets.\n\n## Project Structure\n\nThe repository is organized as follows:\n\n```\nData-analytics/\n├── Esercizi/\n│   ├── exercise_01.ipynb\n│   ├── exercise_02.ipynb\n│   └── ... (other exercise notebooks)\n├── .gitattributes\n├── .gitignore\n├── LICENSE\n├── README.md\n└── requirements.txt\n```\n\n- `Esercizi/`: Contains individual exercise notebooks, each focusing on a specific topic in data analytics.\n- `.gitattributes`: Git attributes configuration file.\n- `.gitignore`: Specifies files and directories to be ignored by git.\n- `LICENSE`: The license under which the project is distributed.\n- `README.md`: This file, providing an overview and instructions for the project.\n- `requirements.txt`: Lists the required Python packages.\n\n## Contributing\n\nContributions to enhance the quality and scope of these exercises are welcome. To contribute:\n\n1. **Fork the Repository**: Create a personal fork of the project.\n2. **Create a Feature Branch**: Develop your feature or fix in a new branch.\n\n   ```bash\n   git checkout -b feature/your-feature-name\n   ```\n\n3. **Commit Changes**: Commit your changes with clear and descriptive messages.\n\n   ```bash\n   git commit -m \"Add feature: description of the feature\"\n   ```\n\n4. **Push to Your Fork**: Push your changes to your forked repository.\n\n   ```bash\n   git push origin feature/your-feature-name\n   ```\n\n5. **Submit a Pull Request**: Open a pull request to merge your changes into the main repository. Make sure to provide a detailed description of the changes and the reason for the contribution.\n\n*Please ensure that your contributions align with the project's objectives and maintain consistency in style and format.*\n\n## Authors\n\n- **sroman0**: [GitHub Profile](https://github.com/sroman0)\n\n## License\n\nThis project is licensed under the **GNU General Public License v3.0**.  \nSee the [LICENSE](https://github.com/sroman0/Data-analytics/blob/main/LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsroman0%2Fdata-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsroman0%2Fdata-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsroman0%2Fdata-analytics/lists"}