Project Jupyter is a community of open-source developers, scientists, educators, and data scientists. Its goal is to build open-source tools and create community that facilitates scientific research, reproducible and open workflows, education, computational narratives, and data analytics. Jupyter supports over 100 programming languages, and connects data analytics tools across a range of disciplines and communities.
There are several core projects of Jupyter that the Berkeley Institute for Data Science supports:
rOpenSci is a software collective that provides R-based tools to enable access to scientific data repositories, full text of articles, and science metrics and also facilitate a culture shift in the scientific community toward reproducible research practices.
Software Carpentry is a volunteer organization whose goal is to make scientists more productive and their work more reliable by teaching them basic computing skills.
Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions. Cesium has two main components—a Python library, and a web application platform that allows interactive exploration of machine learning pipelines. Take control over the workflow in a Python terminal or Jupyter notebook with the Cesium library, or upload your time-series files, select your machine learning model, and watch Cesium do feature extraction and evaluation right in your browser with the web application.