Brought to you by NumFOCUS Foundation and O’Reilly Media Inc.
The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY
Schedule: Session sessions
Thursday, August 24
11:05am–11:45am Thursday, August 24, 2017
Location: Beekman/Sutton North
Average rating:
(5.00, 2 ratings)
Kyle Kelley and Brian Granger offer a broad look at Jupyter frontends, describing their common aspects and explaining how their differences help Jupyter reach a broader set of users. They also share ongoing challenges in building these frontends (real-time collaboration, security, rich output, different Markdown formats, etc.) as well as their ongoing work to address these questions.
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11:05am–11:45am Thursday, August 24, 2017
Location: Sutton Center/Sutton South
Level: Beginner
Christine Doig and Fabio Pliger explain how they built a commercial product on top Jupyter to help Excel users access the capabilities of the rich data science Python ecosystem and share examples and use cases from a variety of industries that illustrate the collaborative workflow between analysts and data scientists that the application has enabled.
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11:05am–11:45am Thursday, August 24, 2017
Location: Murray Hill
Level: Intermediate
Andreas Mueller (Columbia University)
Average rating:
(4.00, 2 ratings)
The Jupyter Notebook can combine narrative, code, and graphics—the ideal combination for teaching anything programming related. That's why Andreas Müller chose to write his book, Introduction to Machine Learning with Python, in a Jupyter notebook. However, going from notebook to book was not easy. Andreas shares challenges and tricks for converting notebooks for print.
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11:05am–11:45am Thursday, August 24, 2017
Location: Nassau
Level: Intermediate
Shreyas Cholia (Lawrence Berkeley National Laboratory),
Rollin Thomas (Lawrence Berkeley National Laboratory),
Shane Canon (Lawrence Berkeley National Laboratory)
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).
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11:05am–11:45am Thursday, August 24, 2017
Location: Regent Parlor
William Merchan (DataScience.com)
Average rating:
(2.00, 1 rating)
Ian Swanson explores the key components of a data science platform and explains how they are enabling organizations to realize the potential of their data science teams.
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11:55am–12:35pm Thursday, August 24, 2017
Location: Beekman/Sutton North
Average rating:
(5.00, 6 ratings)
Brian Granger, Chris Colbert, and Ian Rose offer an overview of JupyterLab, which enables users to work with the core building blocks of the classic Jupyter Notebook in a more flexible and integrated manner.
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11:55am–12:35pm Thursday, August 24, 2017
Location: Sutton Center/Sutton South
Level: Beginner
Average rating:
(5.00, 1 rating)
David Taieb, Prithwish Chakraborty, and Faisal Farooq offer an overview of PixieDust, a new open source library that speeds data exploration with interactive autovisualizations that make creating charts easy and fun.
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11:55am–12:35pm Thursday, August 24, 2017
Location: Murray Hill
Level: Beginner
Carol Willing (Cal Poly San Luis Obispo)
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.
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11:55am–12:35pm Thursday, August 24, 2017
Location: Nassau
Level: Intermediate
Scott Sanderson (Quantopian)
Average rating:
(5.00, 1 rating)
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.
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11:55am–12:35pm Thursday, August 24, 2017
Location: Regent Parlor
Peter Wang (Anaconda)
Peter Wang explores open source commercial companies, offering a firsthand account of the unique challenges of building a company that is fundamentally centered around sustainable open source innovation and sharing guidelines for how to carry volunteer-based open source values forward, intentionally and thoughtfully, in a data-centric world.
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1:50pm–2:30pm Thursday, August 24, 2017
Location: Beekman/Sutton North
Level: Beginner
Thorin Tabor (University of California, San Diego)
Average rating:
(5.00, 1 rating)
Thorin Tabor offers an overview of the GenePattern Notebook, which allows Jupyter to communicate with the open source GenePattern environment for integrative genomics analysis. It wraps hundreds of software tools for analyzing omics data types, as well as general machine learning methods, and makes them available through a user-friendly interface.
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1:50pm–2:30pm Thursday, August 24, 2017
Location: Sutton Center/Sutton South
Level: Intermediate
Performing network analytics with NetworkX and Jupyter often results in difficult-to-examine hairballs rather than useful visualizations. Meanwhile, more flexible tools like SigmaJS have high learning curves for people new to JavaScript. Daina Bouquin and John DeBlase share a simple, flexible architecture that can help create beautiful JavaScript networks without ditching the Jupyter Notebook.
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1:50pm–2:30pm Thursday, August 24, 2017
Location: Murray Hill
Level: Intermediate
Average rating:
(2.00, 1 rating)
Combined with GPUs, Jupyter makes for fast development and fast execution, but it is not always easy to switch from a CPU execution context to GPUs and back. Tim Gasper and Subbu Rama share best practices for doing deep learning with Jupyter and explain how to work with CPUs and GPUs more easily by using Elastic GPUs and quick-switching between custom kernels.
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1:50pm–2:30pm Thursday, August 24, 2017
Location: Nassau
Level: Intermediate
Ryan Lovett (Department of Statistics, UC Berkeley),
Yuvi Panda (Data Science Education Program (UC Berkeley))
Average rating:
(5.00, 1 rating)
The UC Berkeley Data Science Education program uses Jupyter notebooks on a JupyterHub. Ryan Lovett and Yuvi Panda outline the DevOps principles that keep the largest reported educational hub (with 1,000+ users) stable and performant while enabling all the features instructors and students require.
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1:50pm–2:30pm Thursday, August 24, 2017
Location: Regent Parlor
Average rating:
(4.67, 3 ratings)
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.
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2:40pm–3:20pm Thursday, August 24, 2017
Location: Beekman/Sutton North
Level: Intermediate
Christopher Kotfila (Kitware)
Average rating:
(4.00, 1 rating)
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.
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2:40pm–3:20pm Thursday, August 24, 2017
Location: Sutton Center/Sutton South
Level: Beginner
Kyle Kelley (Netflix)
Average rating:
(5.00, 1 rating)
So, Netflix's data scientists and engineers. . .do they know things? Join Kyle Kelley to find out. Kyle explores how Netflix uses Jupyter and explains how you can learn from Netflix's experience to enable analysts at your organization.
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2:40pm–3:20pm Thursday, August 24, 2017
Location: Murray Hill
Level: Beginner
Zach Sailer (University of Oregon)
Average rating:
(5.00, 3 ratings)
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.
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2:40pm–3:20pm Thursday, August 24, 2017
Location: Nassau
Level: Beginner
Matt Greenwood (Two Sigma Investments)
Average rating:
(3.00, 1 rating)
Matt Greenwood introduces BeakerX, a set of Jupyter Notebook extensions that enable polyglot data science, time series plotting and processing, research publication, and integration with Apache Spark. Matt reviews the Jupyter extension architecture and how BeakerX plugs into it, covers the current set of BeakerX capabilities, and discusses the pivot from Beaker, a standalone notebook, to BeakerX.
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2:40pm–3:20pm Thursday, August 24, 2017
Location: Regent Parlor
Mac Rogers (Domino Data Lab)
Mac Rogers shares best practices for creating Jupyter dashboards and some lesser-known tricks for making Jupyter dashboards interactive and attractive.
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4:10pm–4:50pm Thursday, August 24, 2017
Location: Beekman/Sutton North
Level: Intermediate
Average rating:
(3.00, 1 rating)
JupyterHub is a multiuser server for Jupyter notebooks. Min Ragan-Kelley and Carol Willing discuss exciting recent additions and future plans for the project, including the ability to share notebooks with students and collaborators.
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4:10pm–4:50pm Thursday, August 24, 2017
Location: Sutton Center/Sutton South
Level: Intermediate
Paco Nathan (derwen.ai)
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.
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4:10pm–4:50pm Thursday, August 24, 2017
Location: Murray Hill
Level: Beginner
Hilary Parker (Stitch Fix)
Average rating:
(5.00, 2 ratings)
Traditionally, statistical training has focused on statistical methods and tests, without addressing the process of developing a technical artifact, such as a report. Hilary Parker argues that it's critical to teach students how to go about developing an analysis so they avoid common pitfalls and explains why we must adopt a blameless postmortem culture to address these pitfalls as they occur.
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4:10pm–4:50pm Thursday, August 24, 2017
Location: Nassau
Level: Beginner
Karlijn Willems (DataCamp)
Average rating:
(4.00, 4 ratings)
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.
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4:10pm–4:50pm Thursday, August 24, 2017
Location: Regent Parlor
RAJ SINGH (IBM Cloud Data Services)
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.
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5:00pm–5:40pm Thursday, August 24, 2017
Location: Beekman/Sutton North
Level: Intermediate
Maarten Breddels (Maarten Breddels)
Average rating:
(5.00, 3 ratings)
Maarten Breddels offers an overview of vaex, a Python library that enables calculating statistics for a billion samples per second on a regular n-dimensional grid, and ipyvolume, a library that enables volume and glyph rendering in Jupyter notebooks. Together, these libraries allow the interactive visualization and exploration of large, high-dimensional datasets in the Jupyter Notebook.
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5:00pm–5:40pm Thursday, August 24, 2017
Location: Sutton Center/Sutton South
Level: Non-technical
Average rating:
(5.00, 2 ratings)
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.
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5:00pm–5:40pm Thursday, August 24, 2017
Location: Murray Hill
Level: Beginner
Average rating:
(4.50, 2 ratings)
Kaggle Kernels, an in-browser code execution environment that includes a version of Jupyter Notebooks, has allowed Kaggle to flourish in new ways. Drawing on a diverse repository of user-created notebooks paired with competitions and public datasets, Megan Risdal and Wendy Chih-wen Kan explain how Kernels has impacted machine learning trends, collaborative data science, and learning.
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5:00pm–5:40pm Thursday, August 24, 2017
Location: Nassau
Level: Intermediate
Strong partnerships between the open source community and industry have driven many recent developments in Jupyter. Srinivas Sunkara and Cheryl Quah discuss the results of some of these collaborations, including JupyterLab, bqplot, and enhancements to ipywidgets that greatly enrich Jupyter as an environment for data science and quantitative financial research.
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Friday, August 25
11:05am–11:45am Friday, August 25, 2017
Location: Beekman/Sutton North
Average rating:
(4.00, 1 rating)
Matthias Bussonnier and Paul Ivanov walk you through the current Jupyter architecture and protocol and explain how kernels work (decoupled from but in communication with the environment for input and output, such as a notebook document). Matthias and Paul also offer an overview of a number of kernels developed by the community and show you how you can get started writing a new kernel.
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11:05am–11:45am Friday, August 25, 2017
Location: Sutton Center/Sutton South
Level: Intermediate
Andrew Therriault (City of Boston)
Average rating:
(5.00, 1 rating)
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.
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11:05am–11:45am Friday, August 25, 2017
Location: Murray Hill
Level: Non-technical
Average rating:
(5.00, 1 rating)
Although researchers have traditionally cited code and data related to their publications, they are increasingly using the Jupyter Notebook to share the processes involved in the act of scientific inquiry. Bernie Randles and Hope Chen explore various aspects of citing Jupyter notebooks in publications, discussing benefits, pitfalls, and best practices for creating the "paper of the future."
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11:05am–11:45am Friday, August 25, 2017
Location: Nassau
Level: Beginner
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.
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11:05am–11:45am Friday, August 25, 2017
Location: Regent Parlor
Level: Intermediate
Pramit Choudhary (h2o.ai)
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.
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11:55am–12:35pm Friday, August 25, 2017
Location: Beekman/Sutton North
Level: Non-technical
Stuart Geiger (UC Berkeley Institute for Data Science),
Charlotte Cabasse-Mazel (UC Berkeley Institute for Data Science)
Average rating:
(4.50, 2 ratings)
The concept of the ritual is useful for thinking about how the core technology of Jupyter notebooks is extended through other tools, platforms, and practices. R. Stuart Geiger, Brittany Fiore-Gartland, and Charlotte Cabasse-Mazel share ethnographic findings about various rituals performed with Jupyter notebooks.
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11:55am–12:35pm Friday, August 25, 2017
Location: Sutton Center/Sutton South
Level: Non-technical
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.
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11:55am–12:35pm Friday, August 25, 2017
Location: Murray Hill
Level: Beginner
Kaz Sato (Google)
Average rating:
(5.00, 1 rating)
Kazunori Sato explains how you can use Google Cloud Datalab—a Jupyter environment from Google that integrates BigQuery, TensorFlow, and other Google Cloud services seamlessly—to easily run SQL queries from Jupyter to access terabytes of data in seconds and train a deep learning model with TensorFlow with tens of GPUs in the cloud, with all the usual tools available on Jupyter.
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11:55am–12:35pm Friday, August 25, 2017
Location: Nassau
Level: Intermediate
Alexandre Archambault (Teads.tv)
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.
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11:55am–12:35pm Friday, August 25, 2017
Location: Regent Parlor
Christine Doig (Anaconda )
Christine Doig offers an overview of the Anaconda Project, an open source library created by Continuum Analytics that delivers lightweight, efficient encapsulation and portability of data science projects. A JupyterLab extension enables data scientists to install the necessary dependencies, download datasets, and set environment variables and deployment commands from a graphical interface.
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1:50pm–2:30pm Friday, August 25, 2017
Notebook narratives from industry: Inspirational real-world examples and reusable industry notebooks
Location: Beekman/Sutton North
Level: Intermediate
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.
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1:50pm–2:30pm Friday, August 25, 2017
Location: Sutton Center/Sutton South
Level: Intermediate
Ali Marami (R-Brain Inc)
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.
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1:50pm–2:30pm Friday, August 25, 2017
Location: Murray Hill
Level: Non-technical
Average rating:
(4.00, 1 rating)
What do the discovery of the Higgs boson, the landing of the Philae robot, the analysis of political engagement, and the freedom of human trafficking victims have in common? NumFOCUS projects were there. Join Leah Silen and Andy Terrel to learn how we can empower scientists and save humanity.
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1:50pm–2:30pm Friday, August 25, 2017
Location: Nassau
Level: Beginner
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.
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2:40pm–3:20pm Friday, August 25, 2017
Location: Beekman/Sutton North
Level: Intermediate
Christian Moscardi (The Data Incubator)
Christian Moscardi shares the practical solutions developed at the Data Incubator for using Jupyter notebooks for education. Christian explores some of the open source Jupyter extensions he has written to improve the learning experience as well as tools to clean notebooks before they are committed to version control.
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2:40pm–3:20pm Friday, August 25, 2017
Location: Sutton Center/Sutton South
Level: Beginner
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.
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2:40pm–3:20pm Friday, August 25, 2017
Location: Murray Hill
Level: Beginner
Danielle Chou (Zymergen)
Zymergen approaches biology with an engineering and data-driven mindset. Its platform integrates robotics, software, and biology to deliver predictability and reliability during strain design and development. Danielle Chou explains the integral role Jupyter notebooks play in providing a shared Python environment between Zymergen's software engineers and scientists.
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2:40pm–3:20pm Friday, August 25, 2017
Location: Nassau
Level: Non-technical
Matt Burton (University of Pittsburgh)
Average rating:
(5.00, 2 ratings)
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.
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4:10pm–4:50pm Friday, August 25, 2017
Location: Beekman/Sutton North
Level: Intermediate
Average rating:
(4.00, 1 rating)
M Pacer, Jess Hamrick, and Damián Avila explain how the structured nature of the notebook document format, combined with native tools for manipulation and creation, allows the notebook to be used across a wide range of domains and applications.
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4:10pm–4:50pm Friday, August 25, 2017
Location: Sutton Center/Sutton South
Level: Beginner
Natalino Busa (DBS)
Average rating:
(5.00, 1 rating)
Jupyter notebooks are transforming the way we look at computing, coding, and science. But is this the only "data scientist experience" that this technology can provide? Natalino Busa explains how you can create interactive web applications for data exploration and analysis that in the background are still powered by the well-understood and well-documented Jupyter Notebook.
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4:10pm–4:50pm Friday, August 25, 2017
Location: Murray Hill
Level: Intermediate
Daniel Mietchen (University of Virginia)
Jupyter notebooks are a popular option for sharing data science workflows. Daniel Mietchen shares best practices for reproducibility and other aspects of usability (documentation, ease of reuse, etc.) gleaned from analyzing Jupyter notebooks referenced in PubMed Central, an ongoing project that started at a hackathon earlier this year and is being documented on GitHub.
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4:10pm–4:50pm Friday, August 25, 2017
Location: Nassau
Level: Intermediate
Christopher Wilcox (Microsoft)
Have you thought about what it takes to host 500+ Jupyter users concurrently? What about managing 17,000+ users and their content? Christopher Wilcox explains how Azure Notebooks does this daily and discusses the challenges faced in designing and building a scalable Jupyter service.
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5:00pm–5:40pm Friday, August 25, 2017
Location: Beekman/Sutton North
Level: Beginner
It’s not enough just to give data scientists access to Jupyter notebooks in the cloud. Skipper Seabold and Lori Eich argue that to build truly data-driven organizations, everyone from data scientists and managers to business stakeholders needs to work in concert to bring data science out of the wilderness and into the core of decision-making processes.
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5:00pm–5:40pm Friday, August 25, 2017
Location: Sutton Center/Sutton South
Level: Non-technical
Kari Jordan (Data Carpentry)
Average rating:
(5.00, 4 ratings)
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.
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5:00pm–5:40pm Friday, August 25, 2017
Location: Murray Hill
Level: Intermediate
Xeus takes on the burden of implementing the Jupyter kernel protocol so that kernel authors can focus on more easily implementing the language-specific part of the kernel and support features, such as autocomplete or interactive widgets. Sylvain Corlay and Johan Mabille showcase a new C++ kernel based on the Cling interpreter built with xeus.
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5:00pm–5:40pm Friday, August 25, 2017
Location: Nassau
Level: Non-technical
Yuvi Panda (Data Science Education Program (UC Berkeley))
Average rating:
(5.00, 1 rating)
Open data by itself is not enough. You need open computational infrastructures as well. Yuvi Panda offers an overview of a volunteer-led open knowledge movement that makes all of its data available openly and explores the free, open, and public computational infrastructure recently set up for people to play with and build things on its data (using a JupyterHub deployment).
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