https://github.com/sroman0/data-analytics
Data Analytics Exercises is a collection of comprehensive university-level exercises aimed at enhancing skills in data analytics. The repository includes practical notebooks covering data manipulation, exploratory data analysis (EDA), statistical analysis, data visualization, and machine learning fundamentals.
https://github.com/sroman0/data-analytics
data-analysis data-analytics data-science data-visualization education exercises exploratory-data-analysis hands-on-practice jupyter-notebook machine-learning python statistics
Last synced: 3 months ago
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Data Analytics Exercises is a collection of comprehensive university-level exercises aimed at enhancing skills in data analytics. The repository includes practical notebooks covering data manipulation, exploratory data analysis (EDA), statistical analysis, data visualization, and machine learning fundamentals.
- Host: GitHub
- URL: https://github.com/sroman0/data-analytics
- Owner: sroman0
- License: gpl-3.0
- Created: 2024-05-25T09:02:27.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T19:38:44.000Z (3 months ago)
- Last Synced: 2025-02-19T20:27:17.008Z (3 months ago)
- Topics: data-analysis, data-analytics, data-science, data-visualization, education, exercises, exploratory-data-analysis, hands-on-practice, jupyter-notebook, machine-learning, python, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 9.56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Analytics Exercises
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.
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Running the Exercises](#running-the-exercises)
- [Project Structure](#project-structure)
- [Contributing](#contributing)
- [Authors](#authors)
- [License](#license)
## Project Overview
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.
## Features
- **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.
## Getting Started
To begin working with these exercises, follow the instructions below.
### Prerequisites
Ensure you have the following software installed:
- [Python](https://www.python.org/downloads/) (version 3.6 or higher)
- [Jupyter Notebook](https://jupyter.org/install) or [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html)
- [pip](https://pip.pypa.io/en/stable/installation/) (Python package installer)
- (Optional) [virtualenv](https://virtualenv.pypa.io/en/stable/) for creating isolated Python environments
### Installation
1. **Clone the Repository**: Clone this repository to your local machine using the following command:
```bash
git clone https://github.com/sroman0/Data-analytics.git
```
2. **Navigate to the Directory**: Change into the project directory:
```bash
cd Data-analytics
```
3. **Create a Virtual Environment (Optional but Recommended)**:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
### Running the Exercises
To run the exercises:
1. **Launch Jupyter Notebook**: Start the Jupyter Notebook server:
```bash
jupyter notebook
```
2. **Open an Exercise**: In the Jupyter interface, navigate to the desired exercise notebook and open it.
3. **Execute the Notebook**: Follow the instructions within the notebook, executing each cell sequentially.
4. **Modify and Experiment**: Feel free to modify the code to better understand the concepts and experiment with different datasets.
## Project Structure
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.
## Contributing
Contributions to enhance the quality and scope of these exercises are welcome. To contribute:
1. **Fork the Repository**: Create a personal fork of the project.
2. **Create a Feature Branch**: Develop your feature or fix in a new branch.
```bash
git checkout -b feature/your-feature-name
```
3. **Commit Changes**: Commit your changes with clear and descriptive messages.
```bash
git commit -m "Add feature: description of the feature"
```
4. **Push to Your Fork**: Push your changes to your forked repository.
```bash
git push origin feature/your-feature-name
```
5. **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.*
## Authors
- **sroman0**: [GitHub Profile](https://github.com/sroman0)
## License
This project is licensed under the **GNU General Public License v3.0**.
See the [LICENSE](https://github.com/sroman0/Data-analytics/blob/main/LICENSE) file for details.