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https://github.com/lansarbac2020/datascienceproject-2
https://github.com/lansarbac2020/datascienceproject-2
csv-files data-science db etl ssis-packages
Last synced: 4 days ago
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- Host: GitHub
- URL: https://github.com/lansarbac2020/datascienceproject-2
- Owner: Lansarbac2020
- Created: 2024-12-20T18:47:55.000Z (19 days ago)
- Default Branch: master
- Last Pushed: 2025-01-01T10:38:23.000Z (8 days ago)
- Last Synced: 2025-01-01T11:28:06.900Z (8 days ago)
- Topics: csv-files, data-science, db, etl, ssis-packages
- Homepage:
- Size: 50.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DataScienceProject-2
This repository contains data science projects that involve various data processing techniques including ETL (Extract, Transform, Load) processes, working with CSV files, and leveraging SSIS packages.
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Project Overview
This project aims to demonstrate the use of data science techniques to process and analyze data. The main focus is on:
- Extracting data from various sources
- Transforming data to meet specific requirements
- Loading data into a database or other storage systems
## Features
- **CSV Files**: Handling and processing CSV files for data extraction.
- **Data Science**: Implementing data analysis and visualization techniques.
- **Database Integration**: Storing and retrieving data from databases.
- **ETL Processes**: Using SSIS packages to automate ETL tasks.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Lansarbac2020/DataScienceProject-2.git
```
2. Navigate to the project directory:
```bash
cd DataScienceProject-2
```
3. Install the required dependencies:
```bash
# Assuming a requirements.txt file exists
pip install -r requirements.txt
```
## Usage
To use this project, follow these steps:
1. Prepare the data sources (e.g., CSV files) and place them in the appropriate directory.
2. Run the main script or Jupyter Notebook to process the data.
3. Analyze the results and visualize the data as needed.
## Contributing
Contributions are welcome! If you would like to contribute to this project, please follow these guidelines:
1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes and push the branch.
4. Open a pull request with a detailed description of your changes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Contact
For any questions or inquiries, please contact the repository owner:
- **GitHub Profile**: [Lansarbac2020](https://github.com/Lansarbac2020)
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