Open Data Scientist

Abstract

1. Introduction

An AI-powered data analysis assistant that follows the ReAct (Reasoning + Acting) framework to perform comprehensive data science tasks. The agent can execute Python code either locally via Docker or in the cloud using Together Code Interpreter (TCI).

2. Objective

Open AI data scientist agent that automates complex data analysis tasks using the ReAct framework. Execute Python code locally or in the cloud, upload datasets, and generate detailed analytical reports with minimal setup.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/togethercomputer/open-data-scientist.git
cd open-data-scientist

1. launch docker service:
2. Stop services:
3. Command Line Interface (CLI): The easiest way to get started is using the command line interface
4. Python API: For programmatic usage, you can also use the Python API directly

pip install open-data-scientist

# Install uv (faster alternative to pip)
   curl -LsSf https://astral.sh/uv/install.sh | sh

   # Create and activate virtual environment
   uv venv --python=3.12
   source .venv/bin/activate
   uv pip install -e .

cd interpreter
   docker-compose up --build -d

docker-compose down

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by togethercomputer and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.