In data science, it's not enough to be able to analyze data, you must also be able to create compelling visualizations to showcase your insights.

In our exploratory data visualization course, you will learn about the different number of resources you can use to explore and showcase your data in an easy in a digestible way. We’ll cover how to use matplotlib, one of the many popular data visualization libraries that are available for you to use in conjunction with Python.

By the end of this course, you will be able to create visualizations such as line charts, bar plots, scatter plots, histograms, and box plots to better understand your data and help others understand your data as well.

You will also learn how to add and work with multiple plots in your code to show different visualizations together in a single dashboard. We’ll cover how pandas and matplotlib can be used together. And at the end of the course, you’ll be able to combine all the new skills you’ve learned to create a portfolio project that visualizes real world post-graduate earnings data by various college majors.

By the end of this course, you'll be able to:

  • Use data visualization to explore data.
  • Determine how and when to use the most common plots.

Learn to use Python for Data Visualization

Line Charts

Learn the basics of data visualization.

Multiple Plots

Learn how matplotlib represents plots to work with multiple plots.

Bar Plots and Scatter Plots

Learn how to visualize un-ordered data bar plots and scatter plots.

Histograms and Box Plots

Learn about hash tables, a versatile data structure with fast lookup and insertion times.

Visualize Earnings Based on College Majors

Learn how to use pandas to quickly create visualizations.