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© 2017 Continuum Analytics - Confidential & Proprietary© 2017 Continuum Analytics - Confidential & Proprietary
Discover th...
© 2017 Continuum Analytics - Confidential & Proprietary 2
Travis Oliphant
• President, Chief Data Scientist & Co-Founder o...
© 2017 Continuum Analytics - Confidential & Proprietary 3
Gus Cavanaugh
• Product Marketing Manager at Continuum Analytics...
4
What is Anaconda?
© 2017 Continuum Analytics - Confidential & Proprietary 5
The Most Popular Python Data Science Platform with Over 4.5M Use...
© 2017 Continuum Analytics - Confidential & Proprietary 6
Anaconda Distribution, the Most Trusted Distribution for Data Sc...
7
Data Science IDEs
© 2017 Continuum Analytics - Confidential & Proprietary 8
Data Science IDEs vs Developer IDEs
Data Science IDE Developer I...
© 2017 Continuum Analytics - Confidential & Proprietary 9
Data Science IDEs in Anaconda
© 2017 Continuum Analytics - Confidential & Proprietary 10
Spyder
 Editor with code
completion and linting
 Multiple int...
© 2017 Continuum Analytics - Confidential & Proprietary 11
RStudio
 Environment - variables
 History
 Interactive Conso...
© 2017 Continuum Analytics - Confidential & Proprietary 12
Jupyter Notebook
 Storytelling
 Markdown & code cells
 Inter...
13
The Jupyter Story
© 2017 Continuum Analytics - Confidential & Proprietary 14
2009 2010 2011 2012 2013 2014 2015 2016 2017
From IPython to Ju...
15
JupyterLab
© 2017 Continuum Analytics - Confidential & Proprietary 16
 Flexibility
 Extensibility
 Speed
 Scalability
JupyterLab
17
Anaconda Enterprise v5
© 2017 Continuum Analytics - Confidential & Proprietary 18
JupyterLab at the Core of Anaconda Enterprise v5
19
JupyterLab Demo
© 2017 Continuum Analytics - Confidential & Proprietary 20
JupyterLab demos
• Demo 1: JupyterLab tour
• Demo 2: Data explo...
© 2017 Continuum Analytics - Confidential & Proprietary 21
Next Steps
DOWNLOAD Anaconda and JupyterLab
continuum.io/downlo...
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Discover the Next Generation Data Science IDE: From Notebooks to JupyterLab—The Evolution of Data Science Environments

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Travis Oliphant, President, Chief Data Scientist and Co-Founder, creator of NumPy and author of Guide to NumPy: 2nd Edition, will discuss the impact that the notebook experience has had on data science, and how JupyterLab—the next generation data science IDE—has evolved from classic notebooks.

As notebooks grew in popularity, the data science community began asking for features that would enable notebooks to play a more central role in data science projects. With this in mind, members of the community joined forces to create JupyterLab, an open source project that takes classic notebooks to the next level. The new platform adapts easily to multiple workflows, enabling data scientists to seamlessly move between notebooks, scripts, data, and applications. And this is just the beginning.

In this webinar, Travis and Product Marketing Manager Gus Cavanaugh will cover:
- The history of notebook environments
- How JupyterLab differs from the classic notebooks
- Why JupyterLab will be the default experience for Anaconda Enterprise
- A tour of key functionalities of JupyterLab
- Examples of data science projects developed using JupyterLab

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Discover the Next Generation Data Science IDE: From Notebooks to JupyterLab—The Evolution of Data Science Environments

  1. 1. © 2017 Continuum Analytics - Confidential & Proprietary© 2017 Continuum Analytics - Confidential & Proprietary Discover the Next Generation Data Science IDE From Notebooks to JupyterLab—The Evolution of Data Science Environments Travis Oliphant, Co-founder, President & Chief Data Scientist Gus Cavanaugh, Product Marketing Manager August 22, 2017
  2. 2. © 2017 Continuum Analytics - Confidential & Proprietary 2 Travis Oliphant • President, Chief Data Scientist & Co-Founder of Continuum Analytics • Co-Founder of NumFOCUS • PhD from the Mayo Clinic, BS & MS degrees in mathematics and electrical engineering from Brigham Young University • Primary developer of NumPy package • Founding contributor of SciPy package • Author of Guide to NumPy • Creator of Numba
  3. 3. © 2017 Continuum Analytics - Confidential & Proprietary 3 Gus Cavanaugh • Product Marketing Manager at Continuum Analytics • Previously Consultant at IBM and Booz Allen Hamilton • Bachelor's Degree in Business Administration & English • Master’s Degree in Systems Engineering
  4. 4. 4 What is Anaconda?
  5. 5. © 2017 Continuum Analytics - Confidential & Proprietary 5 The Most Popular Python Data Science Platform with Over 4.5M Users
  6. 6. © 2017 Continuum Analytics - Confidential & Proprietary 6 Anaconda Distribution, the Most Trusted Distribution for Data Science
  7. 7. 7 Data Science IDEs
  8. 8. © 2017 Continuum Analytics - Confidential & Proprietary 8 Data Science IDEs vs Developer IDEs Data Science IDE Developer IDE Data-centric Code-centric Interactivity, visualizations, variable explorer Classes, debugging, profiling Less code complexity, scripts More complex code, programs Integration with data sources Integration with git, build tools, compilers Models and narratives storytelling Tools and libraries functionality RStudio, Spyder, Jupyter, JupyterLab PyCharm, Visual Studio, Wing IDE, Sublime text
  9. 9. © 2017 Continuum Analytics - Confidential & Proprietary 9 Data Science IDEs in Anaconda
  10. 10. © 2017 Continuum Analytics - Confidential & Proprietary 10 Spyder  Editor with code completion and linting  Multiple interactive consoles and evaluation models  Variable explorer to view Pandas data frames and Numpy arrays  Interactive documentation
  11. 11. © 2017 Continuum Analytics - Confidential & Proprietary 11 RStudio  Environment - variables  History  Interactive Console  R Markdown  Viewer, plots  Scripts / Markdown
  12. 12. © 2017 Continuum Analytics - Confidential & Proprietary 12 Jupyter Notebook  Storytelling  Markdown & code cells  Interactivity – Widgets  Visualizations  Multilanguage - Kernels
  13. 13. 13 The Jupyter Story
  14. 14. © 2017 Continuum Analytics - Confidential & Proprietary 14 2009 2010 2011 2012 2013 2014 2015 2016 2017 From IPython to JupyterLab 2001 … Initial release of IPython: - ipython by Fernando Pérez - IPP by Janko Hauser - LazyPython by Nathan Gray Jupyter Notebooks added to IPython 0.12 First JupyterCON Jupyter User Survey JupyterLab Started
  15. 15. 15 JupyterLab
  16. 16. © 2017 Continuum Analytics - Confidential & Proprietary 16  Flexibility  Extensibility  Speed  Scalability JupyterLab
  17. 17. 17 Anaconda Enterprise v5
  18. 18. © 2017 Continuum Analytics - Confidential & Proprietary 18 JupyterLab at the Core of Anaconda Enterprise v5
  19. 19. 19 JupyterLab Demo
  20. 20. © 2017 Continuum Analytics - Confidential & Proprietary 20 JupyterLab demos • Demo 1: JupyterLab tour • Demo 2: Data exploration in JupyterLab • Demo 3: JupyterLab in Anaconda Enterprise v5
  21. 21. © 2017 Continuum Analytics - Confidential & Proprietary 21 Next Steps DOWNLOAD Anaconda and JupyterLab continuum.io/downloads conda install -c conda-forge jupyterlab SIGN UP for the Anaconda Enterprise Innovator Program go.continuum.io/anaconda-enterprise-innovator/ READ more about data science in our developer blog continuum.io/blog/developer-blog/

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