smart_data_science

Personal side project to streamline the most common tasks of data science solutions in an efficient manner. This project is based on my experience as a lead data scientist in the industry and financial services sectors, where I have gained expertise in delivering effective data-driven insights and solutions

Installation in Dev / Editor mode

Note: A Debian/Ubuntu Machine, VM or container is highly recommended

Step 0: One-time Machine setup only valid for all Data Science Projects

Create or use a Machine with Conda, Git and Poetry as closely as defined in .devcontainer/Dockerfile:

• This Dockerfile contains a non-root user so the same configuration can be applied to a WSL Ubuntu Machine and any Debian/Ubuntu CLoud Machine (Vertex AI workbench, Azure VM ...)
• In case of having an Ubuntu/Debian machine with non-root user (e.g.: Ubuntu in WSL, Vertex AI VM ...), just install the tools from "non-root user" (no sudo) section of the Dockerfile (sudo apt-get install \ may be required)
• A pre-configured Cloud VM usually has Git and Conda pre-installed, those steps can be skipped
• The development container defined in .devcontainer/Dockerfile can be directly used for a fast setup (Docker required). With Visual Studio Code, just open the root folder of this repo, press F1 and select the option Dev Containers: Open Workspace in Container. The container will open the same workspace after the Docker Image is built.

Step 1. Enter to the root path of the repo and use or create a new conda environment for development:

$ conda create -n dev python=3.10 -y && conda activate dev

Step 2. Install all the Dependencies and the package in editor mode:

$ make setup

Installation for Production/Usage (Not published in PyPi yet)

$ conda create -n smart python=3.10 -y && conda activate smart
$ pip install dist/smart-data-science-0.1.1-py3-none-any.whl

Installation for Production/Usage (after the package is published in PyPi)

$ pip install smart_data_science

Usage

• Still under development. Please refer to the notebooks and examples folders for usage examples

Contributing

Check out the contributing guidelines

License

smart_data_science was created by Angel Martinez-Tenor. It is licensed under the terms of the MIT license.

Credits

smart_data_science was created from a Data Science Template developed by Angel Martinez-Tenor. The template was built upon py-pkgs-cookiecutter [template] (https://github.com/py-pkgs/py-pkgs-cookiecutter)