This repository contains a collection of university exercises focused on data analytics. The exercises are designed to enhance understanding and proficiency in various data analysis techniques and tools.
The 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.
- Diverse Topics: Exercises encompass various aspects of data analytics, from basic data manipulation to advanced statistical modeling.
- Real-World Datasets: Engage with authentic datasets to simulate real-world data analysis challenges.
- Incremental Complexity: Exercises are structured to progressively increase in complexity, catering to both beginners and advanced learners.
- Hands-On Learning: Focused on practical applications rather than just theoretical concepts.
- Code Examples: Each exercise includes commented code to guide you through the solution process.
To begin working with these exercises, follow the instructions below.
Ensure you have the following software installed:
- Python (version 3.6 or higher)
- Jupyter Notebook or JupyterLab
- pip (Python package installer)
- (Optional) virtualenv for creating isolated Python environments
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Clone the Repository: Clone this repository to your local machine using the following command:
git clone https://github.com/sroman0/Data-analytics.git
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Navigate to the Directory: Change into the project directory:
cd Data-analytics -
Create a Virtual Environment (Optional but Recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
To run the exercises:
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Launch Jupyter Notebook: Start the Jupyter Notebook server:
jupyter notebook
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Open an Exercise: In the Jupyter interface, navigate to the desired exercise notebook and open it.
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Execute the Notebook: Follow the instructions within the notebook, executing each cell sequentially.
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Modify and Experiment: Feel free to modify the code to better understand the concepts and experiment with different datasets.
The repository is organized as follows:
Data-analytics/
├── Esercizi/
│ ├── exercise_01.ipynb
│ ├── exercise_02.ipynb
│ └── ... (other exercise notebooks)
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt
Esercizi/: Contains individual exercise notebooks, each focusing on a specific topic in data analytics..gitattributes: Git attributes configuration file..gitignore: Specifies files and directories to be ignored by git.LICENSE: The license under which the project is distributed.README.md: This file, providing an overview and instructions for the project.requirements.txt: Lists the required Python packages.
Contributions to enhance the quality and scope of these exercises are welcome. To contribute:
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Fork the Repository: Create a personal fork of the project.
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Create a Feature Branch: Develop your feature or fix in a new branch.
git checkout -b feature/your-feature-name
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Commit Changes: Commit your changes with clear and descriptive messages.
git commit -m "Add feature: description of the feature" -
Push to Your Fork: Push your changes to your forked repository.
git push origin feature/your-feature-name
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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.
Please ensure that your contributions align with the project's objectives and maintain consistency in style and format.
- sroman0: GitHub Profile
This project is licensed under the GNU General Public License v3.0.
See the LICENSE file for details.