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The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
 
Murray Hill A
Add Deploying a cloud-based JupyterHub for students and researchers to your personal schedule
9:00am Deploying a cloud-based JupyterHub for students and researchers Carol Willing (Cal Poly San Luis Obispo), Min Ragan-Kelley (Simula Research Laboratory), Erik Sundell (IT-Gymnasiet Uppsala)
Add How to Build on top of Jupyter’s Protocols to your personal schedule
1:30pm How to Build on top of Jupyter’s Protocols Kyle Kelley (Netflix)
Murray Hill B
Add An introduction to Julia in Jupyter to your personal schedule
9:00am An introduction to Julia in Jupyter Jane Herriman (Julia Computing, Inc.)
Add Advanced Data Science to your personal schedule
1:30pm Advanced Data Science Bruno Gonçalves (New York University), Matt Brems (General Assembly)
Gramercy A
Gramercy B
Add JupyterLab Tutorial to your personal schedule
1:30pm JupyterLab Tutorial Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS)
10:30am Morning Break | Room: Concourse Foyer
3:00pm Afternoon Break | Room: Concourse Foyer
12:30pm Lunch | Room: Americas Hall 1
Add Jupyter Poster Session to your personal schedule
5:00pm Jupyter Poster Session | Room: Americas Hall 1
8:00am Morning Coffee | Room: South Corridor
9:00am-12:30pm (3h 30m) JupyterHub deployments, Reproducible research and open science, Training and education
Deploying a cloud-based JupyterHub for students and researchers
Carol Willing (Cal Poly San Luis Obispo), Min Ragan-Kelley (Simula Research Laboratory), Erik Sundell (IT-Gymnasiet Uppsala)
This tutorial will let you provide a group of your colleagues or students with easy access to Jupyter notebooks and JupyterLab without asking them to install anything on their computers. You will configure and deploy a cloud-based JupyterHub using Kubernetes. You will learn how to customize and extend it for your needs.
1:30pm-5:00pm (3h 30m) Core architecture
How to Build on top of Jupyter’s Protocols
Kyle Kelley (Netflix)
Learn how to build on top of jupyter's protocols by creating a new web application from the ground up, learning about Jupyter's REST and streaming APIs, message spec, and the notebook format.
9:00am-12:30pm (3h 30m) Community, Training and education
An introduction to Julia in Jupyter
Jane Herriman (Julia Computing, Inc.)
This introductory workshop assumes no prior exposure to Julia. It should be accessible (and hopefully useful!) to scientists, engineers, and anyone else with technical computing needs. We will use Jupyter notebooks to show you why Julia is special, demonstrate how easy it is to learn Julia, and get you writing your first Julia programs.
1:30pm-5:00pm (3h 30m)
Advanced Data Science
Bruno Gonçalves (New York University), Matt Brems (General Assembly)
This two-part tutorial presents a sequence of advanced topics in Data Science, based on using Jupyter.
9:00am-12:30pm (3h 30m) Data visualization, Reproducible research and open science, Training and education
Human in the Loop: Understanding model interpretation with Jupyter and Skater
Pramit Choudhary (DataScience.com)
Just predicting the target labels for a datascience use-case is not enough. It is important to understand the “why”, “what” & “how” about the model’s behavior. In the tutorial, we will explore algorithms(posthoc and rule extraction) to faithfully interpret ML models globally and locally with jupyter's interactiveness and “Skater”, an opensource library to demystify inner working of ML models
1:30pm-5:00pm (3h 30m) Training and education
I do, We do, You Do: Supporting active learning with notebooks
Rachael Tatman (Kaggle)
A practical introduction on incorporating notebooks into the classroom using active learning techniques.
9:00am-12:30pm (3h 30m) Community, Reproducible research and open science, Training and education
Preparing your Jupyter notebook for computationally reproducible publication: A hands-on, BYONotebook tutorial for researchers
April Clyburne-Sherin (Code Ocean)
This is a practical tutorial to prepare Jupyter notebooks for computationally reproducible publication. We start with introductory information about computational reproducibility but the bulk of the tutorial is guided work. Best practices for publishing notebooks are covered, with participants preparing their research for reuse, creating documentation, and submitting their notebook to share.
1:30pm-5:00pm (3h 30m)
JupyterLab Tutorial
Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS)
For the last two years, the Jupyter team has been working on the new Jupyter frontend: JupyterLab. While JupyterLab does, of course, allow the use of Jupyter Notebooks, it goes beyond the classic Jupyter Notebook by providing a flexible and extensible web application with a set of reusable components.
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-6:30pm (1h 30m)
Jupyter Poster Session
The Jupyter Poster Session is an opportunity for you to discuss your work with other attendees and presenters. Posters will be presented Wednesday evening in a friendly, networking setting so you can mingle with the presenters and discuss their work one on one.
8:00am-9:00am (1h)
Break: Morning Coffee