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
 
Concourse A
Add Deploying JupyterHub for students and researchers to your personal schedule
9:00am Deploying JupyterHub for students and researchers Min Ragan-Kelley (Simula Research Laboratory), Carol Willing (Cal Poly San Luis Obispo), Yuvi Panda (Wikimedia Foundation), Ryan Lovett (Department of Statistics, UC Berkeley)
Add JupyterLab tutorial to your personal schedule
1:30pm JupyterLab tutorial Steven Silvester (Continuum Analytics), Jason Grout (Bloomberg)
Concourse B
Add Data analysis and machine learning in Jupyter to your personal schedule
9:00am Data analysis and machine learning in Jupyter Andreas Mueller (Columbia University)
Concourse E
Add Deploying interactive Jupyter dashboards for visualizing hundreds of millions of datapoints, in 30 lines of Python to your personal schedule
1:30pm Deploying interactive Jupyter dashboards for visualizing hundreds of millions of datapoints, in 30 lines of Python James Bednar (Continuum Analytics), Philipp Rudiger (Continuum Analytics)
Concourse G
Add Jupyter widgets: Interactive controls for Jupyter to your personal schedule
9:00am Jupyter widgets: Interactive controls for Jupyter Sylvain Corlay (QuantStack)
Add How to cross the asteroid belt to your personal schedule
1:30pm How to cross the asteroid belt Safia Abdalla (nteract)
10:30am Morning Break | Room: Concourse Foyer
3:00pm Afternoon Break | Room: Concourse Foyer
12:30pm Lunch | Room: America's Hall 1
Add Jupyter Poster Session to your personal schedule
5:00pm Jupyter Poster Session | Room: TBD
9:00am-12:30pm (3h 30m) JupyterHub deployments
Deploying JupyterHub for students and researchers
Min Ragan-Kelley (Simula Research Laboratory), Carol Willing (Cal Poly San Luis Obispo), Yuvi Panda (Wikimedia Foundation), Ryan Lovett (Department of Statistics, UC Berkeley)
JupyterHub, a multiuser server for Jupyter notebooks, enables you to offer a notebook server to everyone in a group—which is particularly useful when teaching a course, as students no longer need to install software on their laptops. Min Ragan-Kelley, Carol Willing, Yuvi Panda, and Ryan Lovett get you started deploying and customizing JupyterHub for your needs.
1:30pm-5:00pm (3h 30m) Core architecture
JupyterLab tutorial
Steven Silvester (Continuum Analytics), Jason Grout (Bloomberg)
Steven Silvester and Jason Grout lead a walkthrough of JupyterLab as a user and as an extension author, explore the capabilities of JupyterLab, and a offer a demonstration of how to create a simple extension to the environment.
9:00am-12:30pm (3h 30m) Usage and application
Data analysis and machine learning in Jupyter
Andreas Mueller (Columbia University)
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.
1:30pm-5:00pm (3h 30m) Usage and application
Interactive natural language processing with SpaCy and Jupyter
Aaron Kramer (DataScience.com)
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.
9:00am-12:30pm (3h 30m) Usage and application
Data analysis in Jupyter notebooks with SQL, Python, and R
Laurent Gautier (Verily)
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.
1:30pm-5:00pm (3h 30m) Usage and application
Deploying interactive Jupyter dashboards for visualizing hundreds of millions of datapoints, in 30 lines of Python
James Bednar (Continuum Analytics), Philipp Rudiger (Continuum Analytics)
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.
9:00am-12:30pm (3h 30m) Jupyter subprojects
Jupyter widgets: Interactive controls for Jupyter
Sylvain Corlay (QuantStack)
With Jupyter widgets, you can build user interfaces with graphical controls inside a Jupyter notebook, documentation, and web pages. Jupyter widgets also provide a framework for building custom controls. Sylvain Corlay demonstrates how to use Jupyter widgets effectively for interactive computing, explores the ecosystem of custom controls, and walks you through building your own control.
1:30pm-5:00pm (3h 30m) Core architecture
How to cross the asteroid belt
Safia Abdalla (nteract)
Have you wondered what it takes to go from a Jupyter user to a Jupyter pro? Wonder no more. Safia Abdalla explores the core concepts of the Jupyter ecosystem, including the extensions ecosystem, the kernel ecosystem, and the frontend architecture, leaving you with an understanding of the possibilities of the Jupyter ecosystem and practical skills on customizing the Jupyter Notebook experience.
10:30am-11:00am (30m)
Break: Morning Break
3:00pm-3:30pm (30m)
Break: Afternoon Break
12:30pm-1:30pm (1h)
Break: Lunch
5:00pm-7:00pm (2h)
Jupyter Poster Session
Posters will be presented Wednesday evening in a networking setting where attendees can mingle with the presenters to discuss their Jupyter work one-on-one.