4:47
Disease Prediction Using the World's Largest Clinical Lab Dataset - Cristian Capdevila (Prognos)
O'Reilly
5:26
Beyond Interactive: Scaling Impact with Notebooks at Netflix - Michelle Ufford (Netflix)
5:31
Jupyter in the Enterprise - Luciano Resende (IBM Watson)
13:52
Jupyter Notebooks and the Intersection of Data Science - David Schaaf (Capital One)
13:54
Why Contribute to Open Source? - Julia Meinweld (Two Sigma Investments)
9:19
Jupyter Trends in 2018 - Paco Nathan (derwen.ai)
14:35
When Jupyter Becomes Pervasive at a University? Fernando Perez (UC Berkeley)
14:11
The Future of Data-driven Discovery in the Cloud - Ryan Abernathey (Columbia University)
14:14
Democratizing Data - Tracy Teal (The Carpentries)
15:14
Sustaining Wonder: Jupyter and the Knowledge Commons - Carol Willing (Cal Poly San Luis Obispo)
14:51
Jupyter & Gravitational Waves - Will Farr (Stony Brook University)
17:23
Keynote by Dan Romuald Mbanga (Amazon Web Services)
14:41
The Reporter's Notebook - Mark Hansen (Columbia Journalism School)
11:22
Data Science as a Catalyst for Scientific Discovery Michelle Gill, Ph.D. (BenevolentAI)
39:24
Visualizing machine learning models in the Jupyter Notebook- Chakri Cherukuri (Bloomberg LP)
28:26
Scaling notebooks for deep learning workloads - Luciano Resende (IBM Watson)
35:31
Containerizing notebooks for serverless execution (sponsored by AWS)
40:23
Notebooks at Netflix: From analytics to engineering- Michelle Ufford (Netflix)
30:40
Enterprise usage of Jupyter: The business case and best practices for leveraging open source
38:53
Using Jupyter notebooks in highly regulated environments
39:48
Open source software and the allocation of capital- Matt Greenwood (Two Sigma Investments)
37:22
Using Jupyter to Empower Enterprise Analysts - Dave Stuart (Department of Defense)
38:54
Jupyter, sensitive data, and public policy- Julia Lane
51:32
Business Summit roundtable: The current environment
46:22
PayPal Notebooks: Data science and machine learning at scale, powered by Jupyter
44:26
Real-time collaboration with Jupyter notebooks using CoCalc- William Stein (SageMath, Inc)
35:39
Making beautiful objects with Jupyter- M Pacer (Netflix)
49:12
Flipped learning with Jupyter: Experiences, best practices, and supporting research
40:52
Jupyter for every high schooler- Rob Newton (Trinity School)
40:43
Data science in US and Canadian higher education- Laura Noren (Obsidian Security)
56:13
I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
55:04
The Jupyter Notebook as a transparent way to document machine learning model development
40:45
Reproducible education: What teaching can learn from open science practices
41:14
Current RISE capabilities and its evolution into the future- Damián Avila (Anaconda, Inc.)
36:43
Scaling collaborative data science with Globus and Jupyter - Ian Foster
46:08
SoS: A polyglot notebook and workflow system...- Bo Peng (The University of Texas)
35:56
Reproducible data dependencies for Jupyter - Jackson Brown, Aneesh Karve
36:30
Reproducible science with the Renku platform- Sandra Savchenko-de Jong (Swiss Data Science Center)
41:44
Explorations in reproducible analysis with Nodebook- Kevin Zielnicki (Stitch Fix)
53:58
Designing for interaction- Scott Sanderson (Quantopian)
Learn by doing: Using data-driven stories and visualizations in the classroom
41:33
Binder: Lowering the bar to sharing interactive software- Tim Head (Wild Tree Tech)
33:11
What things are correlated with gender diversity: A dig through the ASF and Jupyter projects
35:08
nbinteract: Shareable interactive web pages from notebooks
39:32
Going native: C++ as a first-class citizen of the Jupyter ecosystem
33:47
Reproducible quantum chemistry in JupyterLab - Chris Harris (Kitware)
30:03
Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook
40:47
Supporting reproducibility in Jupyter through dataflow notebooks
41:52
JupyterLab and Plotly: A data visualization power couple- Lindsay Richman (McKinsey & Co.)
43:26
SWAN: CERN's Jupyter-based interactive data analysis service - Diogo Castro (CERN)
43:18
"If the data will not come to the astronomer. . ." - Adam Thornton (LSST)
36:11
Terraforming Jupyter: Changing JupyterLab to suit your needs
37:14
Scheduled notebooks: A means for manageable and traceable code execution- Matthew Seal (Netflix)
38:29
Jupyter widgets- Maarten Breddels (Maarten Breddels), Sylvain Corlay (QuantStack)
39:14
Jupyter's configuration system
29:54
JupyterLab- Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter)
36:10
The reincarnation of a notebook- Tony Fast (Ronin), Nick Bollweg (Georgia Tech Research Institute)
35:50
The journey to Julia 1.0: The "Ju" in Jupyter
33:43
GenePattern Notebook: Jupyter beyond the programmer
36:40
Canadians land on Jupyter - Ian Allison, James Colliander
35:36
How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future
29:29
Using the MapD kernel for the Jupyter Notebook- Randy Zwitch (MapD)
43:46
The Emacs Ipython Notebook- John Miller (Honeywell UOP)
35:21
JupyterHub for integrated learning modules - Mariah Rogers, Julian Kudszus (UC Berkeley)
7:29
Charles Smith (Netflix) interviewed at JupyterCon NY 2018
12:34
Paul Ivanov (Bloomberg) interviewed at JupyterCon NY 2018
23:55
Dan Mbanga (AWS) interviewed at JupyterCon NY 2018
Using JupyterLab for flood map development - Seth Lawler (Dewberry)
