Jupyter and Data Driven Decision Making

Excerpts from JupyterCon, in O'Reilly.

Jupyter is where humans and data science intersect
Discover how data-driven organizations are using Jupyter to analyze data, share insights, and foster practices for dynamic, reproducible data science.     By Paco Nathan

I'm grateful to join Fernando Pérez and Brian Granger as a program co-chair for JupyterCon 2018. Project Jupyter, NumFOCUS, and O'Reilly Media will present the second annual JupyterCon in New York City August 21–25, 2018.

Timing for this event couldn't be better. The human side of data science, machine learning/AI, and scientific computing is more important than ever. This is seen in the broad adoption of data-driven decision-making in human organizations of all kinds, the increasing importance of human centered design in tools for working with data, the urgency for better data insights in the face of complex socioeconomic conditions worldwide, as well as dialogue about the social issues these technologies bring to the fore: collaboration, security, ethics, data privacy, transparency, propaganda, etc.

JupyterCon 2018, New York City, Aug. 21-24, 2018

Jupyter is where humans and data science intersect.

And Fernando Perez:   The better the technology, the more important that human judgement becomes.

Consequently, we'll explore three main themes at JupyterCon 2018:

Interactive computing with data at scale: the technical best practices and organizational challenges of supporting interactive computing in companies, universities, research collaborations, etc., (JupyterHub)
Extensible user interfaces for data science, machine learning/AI, and scientific computing (JupyterLab)
Computational communication: taking the artifacts of interactive computing and communicating them to different audiences  .... "