Skip to main content

Improving Your Data Visualizations in Python

Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.

Start Course for Free
4 Hours15 Videos54 Exercises10,686 Learners4650 XPData Visualization Track

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

Great data visualization is the cornerstone of impactful data science. Visualization helps you to both find insight in your data and share those insights with your audience. Everyone learns how to make a basic scatter plot or bar chart on their journey to becoming a data scientist, but the true potential of data visualization is realized when you take a step back and think about what, why, and how you are visualizing your data. In this course you will learn how to construct compelling and attractive visualizations that help you communicate the results of your analyses efficiently and effectively. We will cover comparing data, the ins and outs of color, showing uncertainty, and how to build the right visualization for your given audience through the investigation of a datasets on air pollution around the US and farmer's markets. We will finish the course by examining open-access farmers market data to build a polished and impactful visual report.

  1. 1

    Highlighting your data


    How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.

    Play Chapter Now
    Highlighting data
    50 xp
    Hardcoding a highlight
    100 xp
    Programmatically creating a highlight
    100 xp
    Comparing groups
    50 xp
    Comparing with two KDEs
    100 xp
    Improving your KDEs
    100 xp
    100 xp
    50 xp
    A basic text annotation
    100 xp
    Arrow annotations
    100 xp
    Combining annotations and color
    100 xp
  2. 3

    Showing uncertainty

    Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly.

    Play Chapter Now
  3. 4

    Visualization in the data science workflow

    Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow.

    Play Chapter Now

In the following tracks

Data Visualization


beccarobinsBecca Robinshillary-green-lermanHillary Green-Lerman
Nicholas Strayer Headshot

Nicholas Strayer

Biostatistician at Vanderbilt

I am currently biostatistician and data scientist at Vanderbilt University. My research focuses on the fusion of machine learning and data visualization to explore and explain electronic health records data. I have worked as a data journalist in the graphics department at the New York Times, a data scientist at Johns Hopkins University Data Science Lab, and as a 'Data Artist in Residence' at data visualization startup Conduce. I am active on Twitter @nicholasstrayer and blog about data science and visualization with fellow DataCamp instructor Lucy D'Agostino McGowan at Live Free or Dichotomoize.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA