Project Jupyter: From Computational Notebooks to Large Scale Data Science with Sensitive Data


#1

Title: Project Jupyter: From Computational Notebooks to Large Scale Data Science with Sensitive Data
Date: Friday, September 7, 2018
Time: 12:00 PM Eastern Daylight Time
Duration: 1 hour

SPEAKER: Brian Granger, Associate Professor of Physics and Data Science at Cal Poly State University

Resources:
Webinar Registration Link (free)
Jupyter Cookbook (Free Book for ACM Members)
Jupyter for Data Science (Free Book for ACM Members)
Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server (Free Book for ACM Members)
Introduction to Python for Engineers and Scientists: Open Source Solutions for Numerical Computation (Free Book for ACM Members)
Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons (Free Video for ACM Members)
Beginning Data Analysis with Python And Jupyter (Free Book for ACM Members)
Learning Path: Jupyter: Learn Jupyter Skills from Scratch (Free Learning Path for ACM Members)
Python Fundamentals: Introduction to Jupyter/IPython (Free Short Video for ACM Members)


#2

Hi Brian, I took part in the webinar yesterday.
In my humble opinion, we need to rethink
all user interfaces, for that matter, user experiences, once again.
For example you had included geospatial data as one of the examples.
Geospatial data could in general consist of raster, vector layers, graphics,
multimedia, tables and text. With the advances in digital display systems,
augmented reality, multimedia, etc., one should reimagine how
information gets displayed so that the reader understands it in a jiffy.
All users of Jupyter need not posses coding skills. They may be interested
in only visualizing the output.
After hearing you yesterday, I could imagine the very broad potential of this
open source project Jupyter. My best wishes and regards.
R Nandakumar