Interactive Course

Introduction to Data Visualization with Python

• 4 hours
• 14 Videos
• 58 Exercises
• 80,940 Participants
• 5,000 XP

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

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.

2. 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.

3. 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.

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.

1. 1

Customizing 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.

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.

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Team Anaconda

Data Science Training

This course was created in collaboration with Anaconda. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python data science and machine learning. It's the industry standard for developing, testing, and training on a single machine.

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Collaborators
• Yashas Roy

• Hugo Bowne-Anderson