JupyterCon New York 2018

JupyterCon New York 2018

by O'Reilly Media, Inc.
Released August 2018
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781492025801

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Video description

JupyterCon New York 2018 gathered thousands of data scientists, business analysts, educators, and developers from around the globe to learn about recent developments in the Project Jupyter ecosystem. 100+ Jupyter experts spoke at the conference, providing guidance on how to use Jupyter Notebooks, JupyterLab, JupyterHub, and other Jupyter toolsets. This video compilation is a complete recording of each of the 14 keynote speeches, eight tutorials, and 64 technical sessions delivered at the conference.

If you're a business leader who wants to transform your data into a competitive advantage, an educator looking for a better way to teach, or a researcher who needs powerful tools for sharing and communicating data analysis, the JupyterCon NY 2018 video compilation is a great place to get started.

Highlights include:

  • Keynote speeches by Luciano Resende (IBM Watson) on how IBM leverages the Jupyter stack to offer business critical services; Will Farr (Stony Brook University) on the benefits of using Jupyter for large, global scientific collaborations; and Mark Hansen (Columbia Journalism School) on why today's new generation of journalists must understand and use the newest tools in data analysis.
  • Tutorials by Rachael Tatman (Kaggle) on how to teach a class more effectively using live coding in Jupyter notebooks; Jason Grout (Bloomberg) and Matthias Bussonnier (UC Berkeley BIDS) on how to transition from classic Jupyter Notebooks to the JupyterLab; and Carol Willing (Cal Poly San Luis Obispo) on how to deploy cloud-based JupyterHubs.
  • Total access to the JupyterCon Strata Business Summit—these sessions featured concise, high-level executive briefings on the most important Jupyter developments in business from Jupyter leaders at Capital One, IBM, Oracle, Teradata, PayPal, Two Sigma, Capsule8, GE, and more.
  • Multiple Jupyter usage sessions, including Dave Stuart (Department of Defense) on a "citizen data scientist" program at DoD; and Yuvi Panda (UC Berkeley) on how UCB's MYBinder team develops JupyterHub infrastructure code in a collaborative manner.
  • 15+ reproducible research and open science sessions, including Viral Shah (Julia Computing) on Julia's path toward becoming the default language for all forms of data science; and Adam Thornton (LSST) on combining JupyterLab, JupyterHub, and Kubernetes to enable the analysis of very large datasets.
  • Education and training sessions, including Laura Noren's (Obsidian Security) comparison of data science research infrastructure in Canada versus the US.
  • Jupyter extensions sessions, including Stephanie Stattel (Bloomberg LP) and Paul Ivanov (Bloomberg LP) on how to design and combine extensions to build custom JupyterLab solutions.
  • Data visualization sessions, including Lindsay Richman's (McKinsey & Co.) on using JupyterLab, Plotly, and a Python-based Dash framework to create dynamic charts and interactive reports.
  • Enterprise and organizational adoption sessions, including Diogo Castro (CERN) on SWAN—CERN's Jupyter-based interactive data analysis service.
  • Dozens of other sessions relating to topics such as Jupyter communities, kernels, and JupyterHub deployments.
  • More than 70 hours of video to view at your own pace and schedule.

Table of contents

  1. Keynotes
    1. Disease Prediction Using the World's Largest Clinical Lab Dataset (sponsored by Amazon Web Services) - Cristian Capdevila (Prognos)
    2. Beyond Interactive: Scaling Impact with Notebooks at Netflix Michelle Ufford (Netflix)
    3. Jupyter in the Enterprise - Luciano Resende (IBM Watson)
    4. Jupyter Notebooks and the Intersection of Data Science and Data Engineering - David Schaaf (Capital One)
    5. Why Contribute to Open Source? - Julia Meinwald (Two Sigma Investments)
    6. Jupyter Trends in 2018 - Paco Nathan (derwen.ai)
    7. Sea Change: What Happens When Jupyter Becomes Pervasive at a University? - Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory)
    8. The Future of Data-driven Discovery in the Cloud - Ryan Abernathey (Columbia University)
    9. Democratizing Data - Tracy Teal (The Carpentries)
    10. Sustaining Wonder: Jupyter and the Knowledge Commons - Carol Willing (Cal Poly San Luis Obispo)
    11. All the Cool Kids are Doing it; Maybe We Should Too? Jupyter, Gravitational Waves, and the LIGO and Virgo Scientific Collaborations - Will Farr (Stony Brook University)
    12. Keynote by Dan Romuald Mbanga - Dan Romuald Mbanga (Amazon Web Services)
    13. The Reporter’s Notebook - Mark Hansen (Columbia Journalism School | The Brown Institute for Media Innovation)
    14. Data Science as a Catalyst for Scientific Discovery - Michelle Gill, Ph.D. (BenevolentAI)
  2. Sponsored
    1. Visualizing machine learning models in the Jupyter Notebook (sponsored by Bloomberg LP) - Chakri Cherukuri (Bloomberg LP)
    2. Scaling notebooks for deep learning workloads (sponsored by IBM Watson) - Luciano Resende (IBM Watson)
    3. Containerizing notebooks for serverless execution (sponsored by AWS) - Kevin McCormick (Amazon Web Services)
    4. Notebooks at Netflix: From analytics to engineering (sponsored by Netflix) - Michelle Ufford (Netflix) Kyle Kelley (Netflix)
  3. JupyterCon Business Summit
    1. Enterprise usage of Jupyter: The business case and best practices for leveraging open source - Brian Granger (Cal Poly San Luis Obispo)
    2. Using Jupyter notebooks in highly regulated environments - David Schaaf (Capital One), Shivraj Ramanan (Capital One)
    3. Open source software and the allocation of capital - Matt Greenwood (Two Sigma Investments)
    4. Using Jupyter to Empower Enterprise Analysts - Dave Stuart (Department of Defense )
    5. Jupyter, sensitive data, and public policy - Julia Lane (Center for Urban Science and Progress and Wagner School, NYU)
    6. Business Summit roundtable: The current environment—Compliance, ethics, ML model interpretation, GDPR, and more - Joel Horwitz (IBM), David Schaaf (Capital One), Dan Romuald Mbanga (Amazon Web Services), Dave Stuart (Department of Defense )
    7. PayPal Notebooks: Data science and machine learning at scale, powered by Jupyter - Romit Mehta (PayPal), Praveen Kanamarlapudi (PayPal)
  4. Core architecture
    1. Real-time collaboration with Jupyter notebooks using CoCalc - William Stein (SageMath, Inc. | University of Washington)
    2. Making beautiful objects with Jupyter - M Pacer (Netflix)
  5. Training and education
    1. Flipped learning with Jupyter: Experiences, best practices, and supporting research - Lorena Barba (George Washington University), Robert Talbert (Grand Valley State University)
    2. JupyterHub for domain-focused integrated learning modules - Mariah Rogers (UC Berkeley Division of Data Sciences), Julian Kudszus (UC Berkeley Division of Data Sciences)
    3. Jupyter for every high schooler - Rob Newton (Trinity School)
    4. Data science in US and Canadian higher education - Laura Noren (Obsidian Security)
    5. I don't like notebooks. - Joel Grus (Allen Institute for Artificial Intelligence)
    6. The Jupyter Notebook as a transparent way to document machine learning model development: A case study from a US defense agency - Catherine Ordun (Booz Allen Hamilton)
    7. Reproducible education: What teaching can learn from open science practices - Elizabeth Wickes (School of Information Sciences, University of Illinois at Urbana-Champaign)
    8. Current RISE capabilities and its evolution into the future - Damián Avila (Anaconda, Inc.)
  6. Reproducible research and open science
    1. Scaling collaborative data science with Globus and Jupyter - Ian Foster (Argonne National Laboratory | University of Chicago)
    2. SoS: A polyglot notebook and workflow system for both interactive multilanguage data analysis and batch data processing - Bo Peng (The University of Texas, MD Anderson Cancer Center)
    3. Reproducible data dependencies for Jupyter: Distributing massive, versioned image datasets from the Allen Institute for Cell Science - Jackson Brown (Allen Institute for Cell Science), Aneesh Karve (Quilt)
    4. Reproducible science with the Renku platform - Sandra Savchenko-de Jong (Swiss Data Science Center)
    5. Explorations in reproducible analysis with Nodebook - Kevin Zielnicki (Stitch Fix)
    6. Designing for interaction - Scott Sanderson (Quantopian)
  7. Community
    1. Learn by doing: Using data-driven stories and visualizations in the (high school and college) classroom - Carol Willing (Cal Poly San Luis Obispo), Jessica Forde (Jupyter), Erik Sundell (IT-Gymnasiet Uppsala)
    2. Binder: Lowering the bar to sharing interactive software - Tim Head (Wild Tree Tech)
    3. What things are correlated with gender diversity: A dig through the ASF and Jupyter projects - Holden Karau (Google), Matt Hunt (Bloomberg)
    4. nbinteract: Shareable interactive web pages from notebooks - Sam Lau (UC Berkeley), Caleb Siu (UC Berkeley)
  8. Data visualization
    1. Going native: C++ as a first-class citizen of the Jupyter ecosystem - Sylvain Corlay (QuantStack), Johan Mabille (QuantStack), Wolf Vollprecht (QuantStack), Martin Renou
    2. Reproducible quantum chemistry in JupyterLab - Chris Harris (Kitware)
    3. Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook - Nicolas Fernandez (Icahn School of Medicine)
    4. Using JupyterLab for flood map development: Approaches for improving productivity and reproducibility - Seth Lawler (Dewberry)
    5. Supporting reproducibility in Jupyter through dataflow notebooks - David Koop (University of Massachusetts Dartmouth)
    6. JupyterLab and Plotly: A data visualization power couple - Lindsay Richman (McKinsey Co.)
  9. Extensions and customization
    1. SWAN: CERN's Jupyter-based interactive data analysis service - Diogo Castro (CERN)
    2. "If the data will not come to the astronomer. . .": JupyterLab and a sea change in astronomical analysis - Adam Thornton (LSST)
    3. Terraforming Jupyter: Changing JupyterLab to suit your needs - Stephanie Stattel (Bloomberg LP), Paul Ivanov (Bloomberg LP)
    4. Scheduled notebooks: A means for manageable and traceable code execution - Matthew Seal (Netflix)
    5. Jupyter widgets - Maarten Breddels (Maarten Breddels), Sylvain Corlay (QuantStack)
    6. Jupyter's configuration system - Afshin Darian (Two Sigma | Project Jupyter), M Pacer (Netflix), Min Ragan-Kelley (Simula Research Laboratory), Matthias Bussonnier (UC Berkeley BIDS)
    7. JupyterLab - Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter)
  10. Usage and application
    1. The reincarnation of a notebook - Tony Fast (Ronin), Nick Bollweg (Georgia Tech Research Institute)
    2. The journey to Julia 1.0: The "Ju" in Jupyter - Viral Shah (Julia Computing), Jane Herriman (Julia Computing), Stefan Karpinski
    3. GenePattern Notebook: Jupyter beyond the programmer - Thorin Tabor (University of California, San Diego)
    4. Canadians land on Jupyter - Ian Allison (Pacific Institute for the Mathematical Sciences), James Colliander (Pacific Institute for the Mathematical Sciences)
    5. How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future - Tyler Erickson (Google)
    6. Using the MapD kernel for the Jupyter Notebook - Randy Zwitch
    7. The Emacs Ipython Notebook - John Miller (Honeywell UOP)
  11. Tutorials
    1. JupyterLab tutorial - Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS) Part 1
    2. JupyterLab tutorial - Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS) Part 2
    3. JupyterLab tutorial - Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS) Part 3
    4. JupyterLab tutorial - Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS) Part 4
    5. An introduction to Julia in Jupyter - Jane Herriman (Julia Computing) Part 1
    6. An introduction to Julia in Jupyter - Jane Herriman (Julia Computing) Part 2
    7. An introduction to Julia in Jupyter - Jane Herriman (Julia Computing) Part 3
    8. An introduction to Julia in Jupyter - Jane Herriman (Julia Computing) Part 4
    9. Preparing your Jupyter notebook for computationally reproducible publication: A hands-on BYONotebook tutorial for researchers - April Clyburne-Sherin (Code Ocean) Part 1
    10. Preparing your Jupyter notebook for computationally reproducible publication: A hands-on BYONotebook tutorial for researchers - April Clyburne-Sherin (Code Ocean) Part 2
    11. Preparing your Jupyter notebook for computationally reproducible publication: A hands-on BYONotebook tutorial for researchers - April Clyburne-Sherin (Code Ocean) Part 3
    12. Preparing your Jupyter notebook for computationally reproducible publication: A hands-on BYONotebook tutorial for researchers - April Clyburne-Sherin (Code Ocean) Part 4
    13. How to build on top of Jupyter’s protocols - Kyle Kelley (Netflix) Part 1
    14. How to build on top of Jupyter’s protocols - Kyle Kelley (Netflix) Part 2
    15. How to build on top of Jupyter’s protocols - Kyle Kelley (Netflix) Part 3
    16. How to build on top of Jupyter’s protocols - Kyle Kelley (Netflix) Part 4
    17. Advanced data science: Data visualization in Jupyter using matplotlib and seaborn - Bruno Gonçalves (New York University) - Part 1
    18. Advanced data science: Five ways to handle missing data in Jupyter notebooks - Matt Brems (General Assembly) - Part 2
    19. 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) Part 1
    20. 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) Part 2
    21. 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) Part 3
    22. 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) Part 4
    23. I Do, We Do, You Do: Supporting active learning with notebooks - Rachael Tatman (Kaggle) Part 1
    24. I Do, We Do, You Do: Supporting active learning with notebooks - Rachael Tatman (Kaggle) Part 2

Product information

  • Title: JupyterCon New York 2018
  • Author(s): O'Reilly Media, Inc.
  • Release date: August 2018
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492025801