CoCalc Collaborative Calculation and Data Science

CoCalc
Freemium

Home: https://cocalc.com

Social:
  • #Jupyter Notebook
  • #latex
  • #SageMath
  • #Linux Terminal
  • #gpu computing
  • #Collaboration

What is CoCalc?

CoCalc is an online platform designed for collaborative calculation and data science. It provides a unified environment where users can work together in real-time on various computational documents, including Jupyter Notebooks, LaTeX files, SageMath Worksheets, and more.

The platform supports a wide range of programming languages and integrates Generative AI Large Language Models. CoCalc features powerful compute servers with optional GPU support, enhancing computational capabilities for tasks like machine learning and data analysis. CoCalc simplifies scientific computing and data science collaboration in research and educational settings.

Features

  • Real-time Collaboration: Enables simultaneous editing and updates on computational documents.
  • Jupyter Notebooks: CoCalc's own implementation with synchronization, TimeTravel, and side chat.
  • LaTeX Editor: Full-featured editor with preview, search, and error reporting.
  • Linux Terminal: Collaborative terminal with numerous programming languages and tools.
  • GPU Support: Access to powerful compute servers with various GPU options.
  • Generative AI Integration: Integration with Large Language Models for code generation, error fixing, and more.
  • Course Management System: Tools for managing assignments, grading, and student collaboration.
  • Multiple Programming Languages: Support for Python, SageMath, R, Julia, and others.
  • Computational Whiteboard: Collaborative whiteboard with Jupyter notebook cell support.
  • On-Premises Options: Ability to run CoCalc on your own computer or cluster.

Use Cases

  • Teaching courses involving scientific computing.
  • Collaborative research projects.
  • Data analysis and visualization.
  • Developing and testing code in multiple languages.
  • Creating and sharing interactive computational documents.
  • Running computationally intensive simulations.
  • Machine learning model development and training.
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