  Interactive Course

# Introduction to Data Visualization in Python

Learn complex data visualization techniques using Matplotlib and seaborn.

• 4 hours
• 14 Videos
• 58 Exercises
• 120,891 Participants
• 5,000 XP

### Loved by learners at thousands of top companies:      ### Course Description

This course extends your existing Python skills to provide a stronger foundation in data visualization in Python. You’ll get 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 (like pseudocolor plots, contour plots, and images), statistical graphics (like visualizing distributions and 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. We’ll cover seaborn tools for computing and visualizing linear regressions, as well as tools for visualizing univariate distributions (like strip, swarm, and violin plots) and multivariate distributions (like joint plots, pair plots, and heatmaps). We’ll also discuss 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. You’ll customize plots of stock data, generate histograms of image pixel intensities, and enhance 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. We’ll cover seaborn tools for computing and visualizing linear regressions, as well as tools for visualizing univariate distributions (like strip, swarm, and violin plots) and multivariate distributions (like joint plots, pair plots, and heatmaps). We’ll also discuss 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. You’ll customize plots of stock data, generate histograms of image pixel intensities, and enhance image contrast through histogram equalization.

### What do other learners have to say? “I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group “DataCamp is the top resource I recommend for learning data science.”

Louis Maiden “DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA ##### 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.

See More