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Introduction to Data Visualization with Python

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  • 14 Videos
  • 58 Exercises
  • 4 hours 
  • 3,075 Participants
  • 5000 XP

Instructor(s):

Bryan Van de Ven
Bryan Van de Ven

Bryan is a developer of Bokeh and is a software engineer at Continuum Analytics. He received undergraduate degrees in Computer Science and Mathematics from UT Austin, and a Master's degree in physics from UCLA. He has worked at the Applied Research Labs, developing software for sonar feature detection and classification systems on US Naval submarine platforms. He also spent time at Enthought, where he worked on problems in financial risk modeling and fluid mixing simulation, and also contributed to the Chaco visualization library. He has also worked on an assortment of iOS projects as an independent consultant.

Collaborator(s):

Hugo Bowne-Anderson Hugo Bowne-Anderson

Yashas Roy Yashas Roy

Course Description

This course extends Intermediate Python for Data Science to provide a stronger foundation in data visualization in Python. The course provides a broader coverage of the Matplotlib library and an overview of Seaborn (a package for statistical graphics). Topics covered include customizing graphics, plotting two-dimensional arrays (e.g., pseudocolor plots, contour plots, images, etc.), statistical graphics (e.g., visualizing distributions & regressions), and working with time series and image data.

Prerequisites:

1Customizing plots Free

Following a review of basic plotting with Matplotlib, this chapter delves into customizing plots using Matplotlib. This includes overlaying plots, making subplots, controlling axes, adding legends and annotations, and using different plot styles.