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This course is part of these tracks:

Dhavide Aruliah
Dhavide Aruliah

Data Scientist and Applied Mathematician

Dhavide Aruliah is an applied mathematician & data scientist. He was Director of Training at Anaconda after leaving his position as Associate Professor at the University of Ontario Institute of Technology (UOIT). His research interests include computational inverse problems, numerical linear algebra, & high-performance computing.

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Bryan Van de Ven
Bryan Van de Ven

Software Engineer at Anaconda and Developer of Bokeh

Bryan is a developer of Bokeh and is a software engineer at Anaconda. 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.

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  • Yashas Roy

    Yashas Roy

  • Hugo Bowne-Anderson

    Hugo Bowne-Anderson

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.


  1. 1

    Customizing plots


    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.

  2. Plotting 2D arrays

    This chapter showcases various techniques for visualizing two-dimensional arrays. This includes the use, presentation, and orientation of grids for representing two-variable functions followed by discussions of pseudocolor plots, contour plots, color maps, two-dimensional histograms, and images.

  3. Statistical plots with Seaborn

    This is a high-level tour of the Seaborn plotting library for producing statistical graphics in Python. The tour covers Seaborn tools for computing and visualizing linear regressions as well as tools for visualizing univariate distributions (e.g., strip, swarm, and violin plots) and multivariate distributions (e.g., joint plots, pair plots, and heatmaps). This also includes a discussion of grouping categories in plots.

  4. Analyzing time series and images

    This chapter ties together the skills gained so far through examining time series data and images. This involves customizing plots of stock data, generating histograms of image pixel intensities, and enhancing image contrast through histogram equalization.