The purpose of this project is to gain insights into the trends of the job market in the fields of Artificial Intelligence (AI), Data Science, and Big Data. By understanding these trends, we aim to optimize the recruitment and talent management processes. The project utilizes real-world employment data, specifically focusing on the aforementioned domains. Through data analysis, we will draw valuable conclusions that can aid decision-making in hiring and talent acquisition strategies.
- Introduction
- Project Structure
- Technologies Used
- Installation
- Usage
- Data Source
- Contributing
- License
├── data/
│ ├── job_opportunities.csv
│ └── job_opportunities_diagram.png
├── docker/
│ ├── Dockerfile.python
│ └── requirements.txt
├── notebook/
│ └── dev.ipynb
├── docker-compose.yml
├── README.md
└── ...
- Docker
- Python
- Jupyter Notebook
- SQL Server 2019
- SQL Alchemy
- Matplotlib
- Pandas
- Wordcloud
- Plotly
- Bokeh
- CurrencyConverter
- Clone the repository from GitHub:
git clone https://github.com/Sefdine/Job_opportunities.git- Navigate to the project directory:
cd Job_opportunities- Build the Docker containers:
docker compose build- Start the Docker containers:
docker compose up -d- Access the Jupyter Notebook:
Open your web browser and go to http://localhost:8888.
- Explore the project:
In the Jupyter Notebook, you will find the data analysis code in the notebook/ folder. Execute the cells to perform the analysis and generate insights into the job market trends.
The employment data used in this project is located in the data/ directory. It is provided in a CSV format and contains relevant information regarding job opportunities in AI, Data Science, and Big Data. The dataset has been obtained from kaggle.
Contributions to this project are welcome! If you have suggestions for improvements or new features, please feel free to submit a pull request.
This project is licensed under the MIT License.
Feel free to explore the code and the data in the repository. If you have any questions or feedback, please don't hesitate to contact me.
Happy analyzing! 🚀