JupyterHub is an important tool for research and data-driven decisions at Globo.com. Diogo Munaro Vieira and Felipe Ferreira explain how data scientists at Globo.com—the largest media group in Latin America and second largest television group in the world—use Jupyter notebooks for data analysis and machine learning, making decisions that impact 50 million users per month.
Daina Bouquin (Harvard-Smithsonian Center for Astrophysics),
John D (CUNY Building Performance Lab)
Scott Sanderson describes the architecture of the Quantopian Research Platform, a Jupyter Notebook deployment serving a community of over 100,000 users, explaining how, using standard extension mechanisms, it provides robust storage and retrieval of hundreds of gigabytes of notebooks, integrates notebooks into an existing web application, and enables sharing notebooks between users.
JupyterLab provides a robust foundation for building flexible computational environments. Ali Marami explains how R-Brain leveraged the JupyterLab extension architecture to build a powerful IDE for data scientists, one of the few tools in the market that evenly supports R and Python in data science and includes features such as IntelliSense, debugging, and environment and data view.
Romain Menegaux and Chakri Cherukuri demonstrate how to develop advanced applications and dashboards using open source projects, illustrated with examples in machine learning, finance, and neuroscience.
Reports of a lack of reproducibility have led funders and others to require open data and code as the outputs of research they fund. Mark Hahnel and Marius Tulbure discuss the opportunities for Jupyter notebooks to be the final output of academic research, arguing that Jupyter could help disrupt the inefficiencies in cost and scale of open access academic publishing.
Y M (National Institute of Informatics)
Jupyter is useful for DevOps. It enables collaboration between experts and novices to accumulate infrastructure knowledge, while automation via notebooks enhances traceability and reproducibility. Yoshi Nobu Masatani shows how to combine Jupyter with Ansible for reproducible infrastructure and explores knowledge, workflow, and customer support as literate computing practices.
Engaging critically with data is now a required skill for students in all areas, but many traditional data science programs aren’t easily accessible to those without prior computing experience. Gunjan Baid and Vinitra Swamy explore UC Berkeley's Data Science program—2,000 students across 50 majors—explaining how its pedagogy was designed to make data science accessible to everyone.
Wes McKinney makes the case for a shared infrastructure for data science, discusses the open source community's efforts on Apache Arrow, and offers a vision for seamless computation and data sharing across languages.
While Jupyter notebooks are a boon for computational science, they are also a powerful tool in the digital humanities. Matt Burton offers an overview of the digital humanities community, discusses defactoring—a novel use of Jupyter notebooks to analyze computational research—and reflects upon Jupyter’s relationship to scholarly publishing and the production of knowledge.
Web-based textbooks and interactive simulations built in Jupyter notebooks provide an entry point for course participants to reproduce content they are shown and dive into the code used to build them. Lindsey Heagy and Rowan Cockett share strategies and tools for developing an educational stack that emerged from the deployment of a course on geophysics and some lessons learned along the way.
Lorena Barba explores how to build the ability to support reproducible research into the design of tools like Jupyter and explains how better insights on designing for reproducibility might help extend this design to our research workflows, with the machine as our active collaborator.
Drawing inspiration from narrative theory and design thinking, Karlijn Willems walks you through effectively using Jupyter notebooks to guide the data journalism workflow and tackle some of the challenges that data can pose to data journalism.
Chris Kotfila offers an overview of the GeoNotebook extension to the Jupyter Notebook, which provides interactive visualization and analysis of geospatial data. Unlike other geospatial extensions to the Jupyter Notebook, GeoNotebook includes a fully integrated tile server providing easy visualization of vector and raster data formats.
Scientific research thrives on collaborations between computational and experimental groups, who work together to solve problems using their separate expertise. Zach Sailer highlights how tools like the Jupyter Notebook, JupyterHub, and ipywidgets can be used to make these collaborations smoother and more effective.
Shreyas Cholia, Rollin Thomas, and Shane Canon share their experience leveraging JupyterHub to enable notebook services for data-intensive supercomputing on the Cray XC40 Cori system at the National Energy Research Scientific Computing Center (NERSC).
Although some claim you must start with advanced math to use deep learning, the best way for any coder to get started is with code. Rachel Thomas explains how fast.ai's Practical Deep Learning for Coders course uses Jupyter notebooks to provide an environment that encourages students to learn deep learning through experimentation.
Paco Nathan reviews use cases where Jupyter provides a frontend to AI as the means for keeping humans in the loop. This process enhances the feedback loop between people and machines, and the end result is that a smaller group of people can handle a wider range of responsibilities for building and maintaining a complex system of automation.
In recent years, open source has emerged as a valuable player in the enterprise, and companies like Jupyter and Anaconda are leading the way. Peter Wang discusses the coevolution of these two major players in the new open data science ecosystem and shares next steps to a sustainable future.
Jupyter notebooks are a great tool for exploratory analysis and early development, but what do you do when it's time to move to production? A few years ago, the obvious answer was to export to a pure Python script, but now there are other options. Andrew Therriault dives into real-world cases to explore alternatives for integrating Jupyter into production workflows.
Demba Ba discusses two new signal processing/statistical modeling courses he designed and implemented at Harvard, exploring his perspective as an educator and that of the students as well as the steps that led him to adopt the current cloudJHub architecture. Along the way, Demba outlines the potential of architectures such as cloudJHub to help to democratize data science education.
Diversity can be achieved through sharing information among members of a community. Jupyter prides itself on being a community of dynamic developers, cutting-edge scientists, and everyday users, but is our platform being shared with diverse populations? Kari Jordan explains how training has the potential to improve diversity and drive usage of Jupyter notebooks in broader communities.
Raj Singh offers an overview of PixieDust, a Jupyter Notebook extension that provides an easy way to make interactive maps from DataFrames for visual exploratory data analysis. Raj explains how he built mapping into PixieDust, putting data from Apache Spark-based analytics on maps using Mapbox GL.
Y M (National Institute of Informatics)
Interested in literate computing for reproducibility and nblineage? Or understanding the notebook lifecycle and the consequences of computational narratives? Grab this opportunity to meet Nobu.
Pramit Choudhary offers an overview of Datascience.com's model interpretation library Skater, explains how to use it to evaluate models using the Jupyter environment, and shares how it could help analysts, data scientists, and statisticians better understand their model behavior—without compromising on the choice of algorithm.
Music engages and delights. Carol Willing explains how to explore and teach the basics of interactive computing and data science by combining music with Jupyter notebooks, using music21, a tool for computer-aided musicology, and Magenta, a TensorFlow project for making music with machine learning, to create collaborative narratives and publishing materials for teaching and learning.
Patty Ryan, Lee Stott, and Michael Lanzetta explore four industry examples of Jupyter notebooks that illustrate innovative applications of machine learning in manufacturing, retail, services, and education and share four reference industry Jupyter notebooks (available in both Python and R)—along with demo datasets—for practical application to your specific industry value areas.
Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory)
Fernando Pérez opens JupyterCon with an overview of Project Jupyter, describing how it fits into a vision of collaborative, community-based open development of tools applicable to research, education, and industry.
Alexandre Archambault explores why an official Scala kernel for Jupyter has yet to emerge. Part of the answer lies in the fact that there is no user-friendly, easy-to-use Scala shell in the console (i.e., no IPython for Scala). But there's a new contender, Ammonite—although it still has to overcome a few challenges, not least being supporting by big data frameworks like Spark, Scio, and Scalding.