Introduction to Data Visualization with Matplotlib

Learn how to create, customize, and share data visualizations using Matplotlib.
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Clock4 HoursPlay14 VideosCode44 ExercisesGroup39,818 Learners
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Course Description

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

  1. 1

    Introduction to Matplotlib

    Free
    This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.
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  2. 2

    Plotting time-series

    Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.
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  3. 3

    Quantitative comparisons and statistical visualizations

    Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.
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  4. 4

    Sharing visualizations with others

    This chapter shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.
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In the following tracks
Data Science for EveryoneData Analyst Data Scientist Data Visualization
Collaborators
Chester IsmayAmy Peterson
Ariel Rokem Headshot

Ariel Rokem

Senior Data Scientist, University of Washington
Ariel Rokem is a Data Scientist at the University of Washington eScience Institute. He received a PhD in neuroscience from UC Berkeley, and postdoctoral training in computational neuroimaging at Stanford. In his work, he develops data science algorithms and tools, and applies them to analysis of neural data. He is also a contributor to multiple open-source software projects in the scientific Python ecosystem.
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