The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
Schedule: Usage and application sessions
9:00am - 5:00pm Tuesday, August 21 & Wednesday, August 22
2-Day Training
Location: Concourse B
Level: Intermediate
Zachary Glassman (The Data Incubator)
Average rating:
(4.50, 2 ratings)
Zachary Glassman leads a hands-on dive into building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications using real-world datasets.
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9:00am–12:30pm Wednesday, August 22, 2018
Location: Gramercy A
Level: Intermediate
Noemi Derzsy (AT&T Labs)
Networks, also known as graphs, are one of the most crucial data structures in our increasingly intertwined world. Social friendship networks, the web, financial systems, and infrastructure are all network structures. Noemi Derzsy explains how to generate, manipulate, analyze, and visualize graph structures that will help you gain insight about relationships between elements in your data.
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1:30pm–5:00pm Wednesday, August 22, 2018
Location: Murray Hill B
This two-part tutorial presents a sequence of advanced topics in Data Science, based on using Jupyter.
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11:05am–11:45am Thursday, August 23, 2018
Location: Nassau
Level: Intermediate
Ian Foster (Argonne National Laboratory | University of Chicago)
Average rating:
(5.00, 1 rating)
The Globus service simplifies the utilization of large and distributed data on the Jupyter platform. Ian Foster explains how to use Globus and Jupyter to seamlessly access notebooks using existing institutional credentials, connect notebooks with data residing on disparate storage systems, and make data securely available to business partners and research collaborators.
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11:55am–12:35pm Thursday, August 23, 2018
Location: Beekman/Sutton North
Level: Non-technical
Mariah Rogers (UC Berkeley Division of Data Sciences),
Julian Kudszus (UC Berkeley Division of Data Sciences)
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.
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11:55am–12:35pm Thursday, August 23, 2018
Location: Sutton Center/Sutton South
Level: Non-technical
Yuvi Panda (Data Science Education Program (UC Berkeley))
Running infrastructure is challenging for an open source community. Yuvi Panda shares lessons drawn from the small community that operates MyBinder.org, covering the social and technical processes for keeping MyBinder.org reliable in the most open, transparent, and inclusive way possible, using pretty graphs about the state of MyBinder.org that anyone can see in real time.
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1:50pm–2:30pm Thursday, August 23, 2018
Location: Sutton Center/Sutton South
Level: Intermediate
Matthew Seal (Netflix)
Using an nteract project, papermill, Matthew Seal walks you through how Netflix uses notebooks to track user jobs and make a simple interface for work submission. You’ll get an inside peek at how Netflix is tackling the scheduling problem for a range of users who want easily managed workflows.
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1:50pm–2:30pm Thursday, August 23, 2018
Location: Nassau
Level: Intermediate
Average rating:
(2.00, 3 ratings)
Notebook authors often consider only the interactive experience of creating computable documents. However, the dynamic state of a notebook is a minor period in its lifecycle; the majority is spent as a file at rest. Tony Fast and Nick Bollweg explore conventions that create notebooks with value long past their inception as documents, software packages, test suites, and interactive applications.
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2:40pm–3:20pm Thursday, August 23, 2018
Location: Nassau
Level: Non-technical
Viral Shah (Julia Computing),
Jane Herriman (Julia Computing),
Stefan Karpinski (Julia Computing, Inc.)
Julia and Jupyter share a common evolution path: Julia is the language for modern technical computing, while Jupyter is the development and presentation environment of choice for modern technical computing. Viral Shah and Jane Herriman discuss Julia's journey and the impact of Jupyter on Julia's growth.
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2:40pm–3:20pm Thursday, August 23, 2018
Location: Concourse A: Business Summit
Level: Non-technical
Dave Stuart (Department of Defense )
Average rating:
(4.00, 1 rating)
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 community’s dynamic challenges.
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4:10pm–4:50pm Thursday, August 23, 2018
Location: Murray Hill
Level: Beginner
Thorin Tabor (University of California, San Diego)
Average rating:
(4.50, 2 ratings)
Making Jupyter accessible to all members of a research organization, regardless of their programming ability, empowers it to best utilize the latest analysis methods while avoiding bottlenecks. Thorin Tabor offers an overview of the GenePattern Notebook, which offers a wide suite of enhancements to the Jupyter environment to help bridge the gap between programmers and nonprogrammers.
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11:05am–11:45am Friday, August 24, 2018
Location: Murray Hill
Level: Intermediate
Average rating:
(4.00, 1 rating)
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.
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11:05am–11:45am Friday, August 24, 2018
Location: Concourse A: Business Summit
Level: Intermediate
Jinli Ma (Synchrony Financial)
Average rating:
(1.50, 2 ratings)
In the corporate tax world, Microsoft Excel—the king of spreadsheets—is the default tool for tracking information and managing tasks, but tax professionals are often annoyed by slowly updating or broken linked or referenced cells within or between spreadsheets. Jinli Ma explains how the Jupyter Notebook does a better job than Microsoft Excel with the original issued discount calculation process.
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11:55am–12:35pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Beginner
Ian Allison (Pacific Institute for the Mathematical Sciences),
James Colliander (Pacific Institute for the Mathematical Sciences)
Average rating:
(4.00, 1 rating)
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.
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11:55am–12:35pm Friday, August 24, 2018
Location: Murray Hill
Level: Intermediate
Tyler Erickson (Google)
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.
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11:55am–12:35pm Friday, August 24, 2018
Location: Nassau
Level: Beginner
Joel Grus (Allen Institute for Artificial Intelligence)
Average rating:
(5.00, 1 rating)
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.
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11:55am–12:35pm Friday, August 24, 2018
Location: Concourse A: Business Summit
Level: Beginner
Catherine Ordun (Booz Allen Hamilton)
Average rating:
(5.00, 5 ratings)
Many US government agencies are just getting started with machine learning. As a result, data scientists need to de-"black box" models as much as possible. One simple way to do this is to transparently show how the model is coded and its results at each step. Notebooks do just this. Catherine Ordun walks you through a notebook built for RNNs and explains how government agencies can use notebooks.
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1:50pm–2:30pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Beginner
Douglas Blank (Comet.ML)
For the last four years, Douglas Blank has used nothing but Jupyter in the classroom—from 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.
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1:50pm–2:30pm Friday, August 24, 2018
Location: Sutton Center/Sutton South
Level: Beginner
Many of us believe that gender diversity in open source projects is important. (If you don’t, this isn’t going to convince you.) But what things are correlated with improved gender diversity, and what can we learn from similar historic industries? Holden Karau and Matt Hunt explore the diversity of different projects, examine historic EEOC complaints, and detail parallels and historic solutions.
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1:50pm–2:30pm Friday, August 24, 2018
Location: Murray Hill
Level: Beginner
Seth Lawler (Dewberry)
Average rating:
(5.00, 1 rating)
Creating flood maps for coastal and riverine communities requires geospatial processing, statistical analysis, finite element modeling, and a team of specialists working together. Seth Lawler explains how using the feature-rich JupyterLab to develop tools, share code with team members, and document workflows used in the creation of flood maps improves productivity and reproducibility.
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2:40pm–3:20pm Friday, August 24, 2018
Location: Murray Hill
Level: Intermediate
Randy Zwitch (MapD)
MapD Core is an open source analytical SQL engine that has been designed from the ground up to harness the parallelism inherent in GPUs. This enables queries on billions of rows of data in milliseconds. Randy Zwitch offers an overview of the MapD kernel extension for the Jupyter Notebook and explains how to use it in a typical machine learning workflow.
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4:10pm–4:50pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Intermediate
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.
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4:10pm–4:50pm Friday, August 24, 2018
Location: Murray Hill
Level: Beginner
Sean Gorman (DigitalGlobe)
Satellite imagery can be a critical resource during disasters and humanitarian crises. While the community has improved data sharing, we still struggle to create reusable data science to solve problems on the ground. Sean Gorman offers an overview of GBDX Notebooks, a step toward creating an open data science community built around Jupyter to stream imagery and share analysis at scale.
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4:10pm–4:50pm Friday, August 24, 2018
Location: Nassau
Level: Beginner
Lindsay Richman (McKinsey & Co.)
Average rating:
(4.50, 2 ratings)
JupyterLab and Plotly both provide a rich set of tools for working with data. When combined, they create a powerful computational environment that enables users to produce versatile, robust visualizations in a fast-paced setting. Lindsay Richman demonstrates how to use JupyterLab, Plotly, and Plotly's Python-based Dash framework to create dynamic charts and interactive reports.
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5:00pm–5:40pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Beginner
Damián Avila (Anaconda, Inc.)
RISE has evolved into the main slideshow machinery for live presentations within the Jupyter notebook. Damián Avila explains how to install and use RISE. You'll also discover how to customize it and see some of its new capabilities. Damián concludes by discussing the migration from RISE into a new JupyterLab-RISE extension providing RISE-based capabilities in the new JupyterLab interface.
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5:00pm–5:40pm Friday, August 24, 2018
Location: Sutton Center/Sutton South
Level: Beginner
John Miller (Honeywell UOP)
John Miller offers an overview of the Emacs IPython Notebook (EIN), a full-featured client for the Jupyter Notebook in Emacs, and shares a brief history of its development.
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5:00pm–5:40pm Friday, August 24, 2018
Location: Murray Hill
Level: Intermediate
Joshua Patterson, Leo Meyerovich, and Keith Kraus demonstrate how to use PyGDF and other GoAi technologies to easily analyze and interactively visualize large datasets from standard Jupyter notebooks.
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5:00pm–5:40pm Friday, August 24, 2018
Location: Nassau
Level: Intermediate
Scott Sanderson (Quantopian)
Average rating:
(4.67, 3 ratings)
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.
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