This repository lists helpful resources for data scientists to start incorporating LLMs into their daily workflows. Engineers have been using Github-Copilot to increase their efficiency by more than 50%, why should data scientists be left behind?
The resources are structured into 2 formats:
- Examples in the form of jupyter notebooks
- Learning Path which lists the steps you can take to start using LLMs in your daily workflow
Examples are present in the form of notebooks:
- Code Generation: data science salaries case study
- Information Extraction: JioMart case study
- Text Summarization: Delta Air Lines twitter customer support case study
- Query Generation: Google analytics case study
Contributions are welcome! You could contribute other example notebooks following a similar pattern. Please use the template followed in existing notebooks.
- Start with a title section with a summary of analysis being performed
- Add a
Data Sourcessection with links/details to the data files required to replicate the notebook - Add a
Environment Setupsection with instructions to setup the environment required to run the notebook. List down the packages used along with versions