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