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The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY
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Schedule: Usage and application sessions
Examples of usage of Jupyter in the field, whether in research, education or industry.
9:00am–12:30pm Wednesday, August 23, 2017
Location: Concourse A
Level: Beginner
Andreas Mueller (Columbia University)
Average rating:
(5.00, 2 ratings)
Andreas Müller walks you through a variety of real-world datasets using Jupyter notebooks together with the data analysis packages pandas, seaborn, and scikit-learn. You'll perform an initial assessment of data, deal with different data types, visualization, and preprocessing, and build predictive models for tasks such as health care and housing.
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9:00am–12:30pm Wednesday, August 23, 2017
Location: Concourse F
Level: Intermediate
Laurent Gautier (Verily)
Average rating:
(5.00, 1 rating)
Python is popular for data analysis, but restricting yourself to Python means missing a wealth of libraries or capabilities available in R or SQL. Laurent Gautier walks you through a pragmatic, reasonable, and good-looking polyglot approach, all thanks to R visualizations.
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1:30pm–5:00pm Wednesday, August 23, 2017
SOLD OUT
Location: Concourse E
Level: Intermediate
Average rating:
(4.50, 2 ratings)
It can be difficult to assemble the right set of packages from the Python scientific software ecosystem to solve complex problems. James Bednar and Philipp Rudiger walk you step by step through making and deploying a concise, fast, and fully reproducible recipe for interactive visualization of millions or billions of data points using very few lines of Python in a Jupyter notebook.
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1:30pm–5:00pm Wednesday, August 23, 2017
SOLD OUT
Location: Concourse F
Level: Intermediate
Aaron Kramer (DataScience.com)
Average rating:
(3.00, 2 ratings)
Modern natural language processing (NLP) workflows often require interoperability between multiple tools. Aaron Kramer offers an introduction to interactive NLP with SpaCy within the Jupyter Notebook, covering core NLP concepts, core workflows in SpaCy, and examples of interacting with other tools like TensorFlow, NetworkX, LIME, and others as part of interactive NLP projects.
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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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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: 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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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: 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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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: 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: 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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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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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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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: 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.
Read more.
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.
Read more.
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