Project: Data Science

This repository is designed as a starting point for data science projects, providing a structured project for data analysis and modeling.

Overview

A data science project typically involves several key steps, consequently were created the following resources:

Notebook: To develop and test ideas, data scientists typically use Jupyter Notebooks. Implementations should locate within the notebook directory. A starter file named __main__.ipynb is provided in that folder to help users get started.
Data: All datasets should be stored in the data directory. To get started, was provided a sample dataset meteorite_landings.csv located in this folder. The dataset was loaded using Python within the Dataset section of the __main__.ipynb notebook.
Libraries: Any custom libraries or modules created should be placed in the notebook/lib directory. A sample library named data_profiling.py was provided to demonstrate how to create reusable code for notebooks.
Results: The results of your analysis should be stored in the res directory. This can include reports, visualizations, or any other output generated by your notebooks.
Requirements: Any additional Python packages required for your project should be listed in the requirements.txt file. This allows for easy installation of dependencies using pip.
Virtual Environment: To not affect your global Python environment, a virtual environment is generally integrated. For this template was considered the usage of venv.

Important

Usage:

bash cmd.sh {setup|clear}

If you haven't built the project yet, you can do so by running:

bash cmd.sh setup

If you want to remove all generated files and start from scratch, you can run:

bash cmd.sh clear

This project is distributed under GNU General Public License version 3. You can find the complete text of the license in the project repository.