JupyterCon in New York 2018

Containerizing notebooks for serverless execution (sponsored by AWS)

January 10, 2020

Kevin McCormick explains the story of two approaches which were used internally at AWS to accelerate new ML algorithm development, and easily package Jupyter notebooks for scheduled execution, by creating custom Jupyter kernels that automatically create Docker containers, and dispatch them to either a distributed training service or job execution environment.

Advanced data science, part 2: Five ways to handle missing data in Jupyter notebooks

January 9, 2020

Missing data plagues nearly every data science problem. Often, people just drop or ignore missing data. However, this usually ends up with bad results. Matt Brems explains how bad dropping or ignoring missing data can be and teaches you how to handle missing data the right way by leveraging Jupyter notebooks to properly reweight or impute your data.

All the cool kids are doing it; maybe we should too? Jupyter, gravitational waves, and the LIGO and Virgo Scientific Collaborations

January 9, 2020

Will Farr shares examples of Jupyter use within the LIGO and Virgo Scientific Collaborations and offers lessons about the (many) advantages and (few) disadvantages of Jupyter for large, global scientific collaborations. Along the way, Will speculates on Jupyter's future role in gravitational wave astronomy.

Beyond interactive: Scaling impact with notebooks at Netflix

January 9, 2020

Netflix is reimagining what a Jupyter notebook is, who works with it, and what you can do with it. Michelle Ufford shares how Netflix leverages notebooks today and describes a brief vision for the future.

Building an Enterprise/Cloud Analytics Platform with Jupyter Enterprise Gateway

January 9, 2020

Data science and analytics departments are now common place for enterprises determined to maximize their operations. While Jupyter Notebooks have significantly decreased the cost of admission into this space, enterprises are finding that data science at scale is difficult within the current framework. Jupyter Enterprise Gateway is designed to address these scalability issues for the enterprise.

Canadians land on Jupyter

January 9, 2020

Over the past 18 months, Ian Allison and James Colliander have deployed Jupyter to more than 8,000 users at universities across Canada. Ian and James offer an overview of the Syzygy platform and explain how they plan to scale and deliver the service nationally and how they intend to make Jupyter integral to the working experience of students, researchers, and faculty members.

Citizen data science: An enterprise use case from inside the US intelligence community

January 9, 2020

Dave Stuart explains how Jupyter was used inside the US Department of Defense and the greater intelligence community to empower thousands of "citizen data scientists" to build and share analytics in order to meet the communitys dynamic challenges.

Data science as a catalyst for scientific discovery

January 9, 2020

Michelle Gill explains how data science methodologies and tools can be used to link information from different scientific fields and accelerate discovery in a variety of areas, including the biological sciences.

Data science in US and Canadian higher education

January 9, 2020

Laura Noren offers an overview of a research project on the various infrastructure models supporting data science in research settings in terms of funding, educational uses, and research utilization. Laura then shares some of the findings, comparing the national federation model currently established in Canada to the more grassroots efforts in many US universities.

Democratizing data

January 9, 2020

We are generating vast amounts of data, but it's not the data itself that is valuableit's the information and knowledge that can come from this data. Tracy Teal explains how to bring people to data and empower them to address their questions, reach their potential, and solve issues that are important in science, scholarship, and society.

Design and analysis of the worlds most advanced microprocessors using Jupyter notebooks

January 9, 2020

Kerim Kalafala and Nicholai L'Esperance share their experiences using Jupyter notebooks as a critical aid in designing the next generation of IBM Power and Z processors, focusing on analytics on graphs consisting of hundreds of millions of nodes. Along the way, Kerim and Nicholai explain how they leverage Jupyter notebooks as part of their overall design system.

Designing for interaction

January 8, 2020

Scott Sanderson explores how interactivity can and should influence the design of software libraries, details how the needs of interactive users differ from the needs of application developers, and shares techniques for improving the usability of libraries in interactive environments without sacrificing robustness in noninteractive environments.

How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future

January 8, 2020

Massive collections of data on the Earth's changing environment, collected by satellite sensors and generated by Earth system models, are being exposed via web APIs by multiple providers. Tyler Erickson highlights the use of JupyterLab and Jupyter widgets in analyzing complex high-dimensional datasets, providing insights into how our Earth is changing and what the future might look like.

I don't like notebooks.

January 8, 2020

I have been using and teaching Python for many years. I wrote a best-selling book about learning data science. And here's my confession: I don't like notebooks. (There are dozens of us!) I'll explain why I find notebooks difficult, show how they frustrate my preferred pedagogy, demonstrate how I prefer to work, and discuss what Jupyter could do to win me over.

Jupyter for every high schooler

January 8, 2020

In an effort to broaden graduates' mathematical toolkit and address gender equity in STEM education, Rob Newton has led the implementation of Python projects across his school's entire ninth-grade math courses. Now every student in the ninth grade completes three python projects that introduce programming and integrate them with the ideas developed in class.

Jupyter graduates

January 8, 2020

For the last four years, Douglas Blank has used nothing but Jupyter in the classroomfrom a first-year writing course to a course on assembly language, from biology to computer science, from lectures to homework. Join in to learn how Douglas has leveraged Jupyter and discover the successes and failures he experienced along the way. Nicole Petrozzo then offers a student's perspective.

Jupyter in the enterprise

January 7, 2020

IBM has leveraged the Jupyter stack in many of its products to offer industry-leading and business-critical services to its clients. Luciano Resende explores some of the open source initiatives that IBM is leading in the Jupyter ecosystem to address enterprise requirements in the community.

Jupyter in the modern enterprise data and analytics ecosystem: Trends, experiments, and opportunities

January 7, 2020

Gerald Rouselle reviews some of the trends in modern data and analytics ecosystems for large enterprises and shares some of the key challenges and opportunities for Jupyter adoption. He also details some recent examples and experiments in incorporating Jupyter in commercial products and platforms.

Jupyter notebooks and the intersection of data science and data engineering

January 7, 2020

David Schaaf explains how data science and data engineering can work together in cross-functional teamswith Jupyter notebooks at the center of collaboration and the analytic workflowto more effectively and more quickly deliver results to decision makers.

Jupyter trends in 2018

January 7, 2020

Jupyter is built on a set of extensible, reusable building blocks, expressed through various open protocols, APIs, and standards. For many use cases, these are combined to provide extensible software architecture for interactive computing with data. Paco Nathan shares a few somewhat unexpected things that emerged in 2018.

Jupyter's configuration system

January 7, 2020

Jupyter's straightforward, out-of-the-box experience has been important for its success in widespread adoption. But good defaults only go so far. Join Afshin Darian, M Pacer, Min Ragan-Kelley, and Matthias Bussonnier to go beyond the defaults and make Jupyter your own.

JupyterHub for domain-focused integrated learning modules

January 7, 2020

The Data Science Modules program at UC Berkeley creates short explorations into data science using notebooks to allow students to work hands-on with a dataset relevant to their course. Mariah Rogers, Ronald Walker, and Julian Kudszus explain the logistics behind such a program and the indispensable features of JupyterHub that enable such a unique learning experience.

Keynote by Dan Romuald Mbanga

January 7, 2020

Keynote by Dan Romuald Mbanga

Machine learning at scale with Kubernetes

January 7, 2020

Christopher Cho demonstrates how Kubernetes can be easily leveraged to build a complete deep learning pipeline, including data ingestion and aggregation, preprocessing, ML training, and serving with the mighty Kubernetes APIs.

nbinteract: Shareable interactive web pages from notebooks

January 7, 2020

The nbinteract package converts Jupyter notebooks with widgets into interactive, standalone HTML pages. Its built-in support for function-driven plotting makes authoring interactive pages simpler by allowing users to focus on data, not callbacks. Sam Lau and Caleb Siu offer an overview of nbinteract and walk you through the steps to publish an interactive web page from a Jupyter notebook.

Pangeo: Big data climate science in the cloud

January 6, 2020

Climate science is being flooded with petabytes of data, overwhelming traditional modes of data analysis. The Pangeo project is building a platform to take big data climate science into the cloud using SciPy and large-scale interactive computing tools. Join Ryan Abernathey and Yuvi Panda to find out what the Pangeo team is building and why and learn how to use it.

PayPal Notebooks: Data science and machine learning at scale, powered by Jupyter

January 6, 2020

Hundreds of PayPal's data scientists, analysts, and developers use Jupyter to access data spread across filesystem, relational, document, and key-value stores, enabling complex analytics and an easy way to build, train, and deploy machine learning models. Romit Mehta and Praveen Kanamarlapudi explain how PayPal built its Jupyter infrastructure and powerful extensions.

Rapid data science exploration for cybersecurity

January 6, 2020

The key to successful threat detection in cybersecurity is fast response. George Williams, Harini Kannan, and Alex Comerford offer an overview of specialized extensions they have built for data scientists working in cybersecurity that can be used and deployed via JupyterHub.