Thursday, August 20, 2015
01:00 PM - 04:15 PM
|Level: ||Technical - Intermediate|
Communicating the patterns found in data is hard enough without the data coming from diverse sources and in unprecedented volumes. A fundamentally human way of interpreting patterns and insights in data is through visualization. We will present easy to implement case studies of diverse visualization strategies from summary statistics to visualizing statistical inference from machine learning algorithms on big data.
Python is a powerful development, computational, and programming environment and one of the areas where Python excels is visualization and analysis of big data, due to several high-quality modules for both simple and advanced visual analytics. This tutorial will cover the following big-data visualization capabilities in Python: -interactive plotting with IPython, matplotlib, and databases, -building web visualizations with Bokeh, -and Python integration with VTK and ParaView. -Additional information will also be provided on mapreduce and NoSQL capabilities in our case studies.
Attendees will leave with an understanding of a breadth of visualization capabilities and with a general impression of the ease to which these techniques can be applied to diverse domains and data types, across varied data disciplines.
Minnesota boy at home in the mountains, when Charlie is not teaching his 5 yr old how to ski powder, he is using mathematics to tell computers how to discover patterns in data. He believes anyone can do machine learning and that by sharing information on computer science, we are all better off. He thinks that if you only give him the chance, he can teach you any statistical concept, and that you'll walk away actually thinking positively about math.