CoCalc covers all the bases
- Data Science and Machine Learning: Upload your datafiles and analyze them using Tensorflow, scikit-learn, Keras, ... including an Anaconda environment.
- Mathematics: SymPy, SageMath, ...
- Statistics: pandas, statsmodels, rpy2 (R bridge), ...
- Visualization: matplotlib, plotly, seaborn, ...
- Teaching: learn Python online or teach a course.
Find more details in the list of installed Python libraries.
This enables you to work more effectively as a team to solve the challenges of data science, machine learning and statistics. Every collaborator is always looking at the most recent state of files, experiences and inspects the same problems at the same time, and you can get help via side chat by "@name" mention a collaborator.
Python in Jupyter Notebooks
*.ipynbfile at any time and continue working in another environment.
LaTeX support for PythonTeX/SageTeX
.texfiles containing PythonTeX or SageTeX code. The document is synchronized with your collaborators in real-time and everyone sees the very same compiled PDF.
- Manages the entire compilation pipeline for you: it automatically calls
sageto pre-process the code,
- Supports forward and inverse search to help you navigating in your document,
- Captures and shows you where LaTeX or Python errors happen,
- and via TimeTravel you can go back in time to see your latest edits in order to easily recover from a recent mistake.
- Upload or fetch your datasets,
- Use Jupyter Notebooks to explore the data, process it, and calculate your results,
- Discuss and collaborate with your research team,
- Write your research paper in a LaTeX document,
- Publish the datasets, your research code, and the PDF of your paper online, all hosted on CoCalc.
.pyfiles and Jupyter Notebooks running a Python kernel.