In this chapter you’ll learn the value of visualizations, using real-world data on British monarchs, Australian salaries, Panamanian animals, and US cigarette consumption, to graphically represent the spread of a variable using histograms and box plots.

A plot tells a thousand words

Motivating visualization

Continuous vs. categorical variables

Histograms

Interpreting histograms

Adjusting bin width

Box plots

Interpreting box plots

Ordering box plots

Visualizing distributions

You’ll learn how to interpret data plots and understand core data visualization concepts such as correlation, linear relationships, and log scales. Through interactive exercises, you’ll also learn how to explore the relationship between two continuous variables using scatter plots and line plots. You'll explore data on life expectancies, technology adoption, COVID-19 coronavirus cases, and Swiss juvenile offenders. Next you’ll be introduced to two other popular visualizations—bar plots and dot plots—often used to examine the relationship between categorical variables and continuous variables. Here, you'll explore famous athletes, health survey data, and the price of a Big Mac around the world.

Scatter plots

Interpreting scatter plots

Trends with scatter plots

Line plots

Interpreting line plots

Logarithmic scales for line plots

Bar plots

Interpreting bar plots

Interpreting stacked bar plots

Dot plots

Interpreting dot plots

Sorting dot plots

Visualizing two variables

It’s time to make your insights even more impactful. Discover how you can add color and shape to make your data visualizations clearer and easier to understand, especially when you find yourself working with more than two variables at the same time. You'll explore Los Angeles home prices, technology stock prices, math anxiety, the greatest hiphop songs, scotch whisky preferences, and fatty acids in olive oil.

Higher dimensions

Another dimension for scatter plots

Another dimension for line plots

Using color

Eye-catching colors

Qualitative, sequential, diverging

Highlighting data

Plotting many variables at once

Interpreting pair plots

Interpreting correlation heatmaps

The color and the shape

In this final chapter, you’ll learn how to identify and avoid the most common plot problems. For example, how can you avoid creating misleading or hard to interpret plots, and will your audience understand what it is you’re trying to tell them? All will be revealed! You'll explore wind directions, asthma incidence, and seats in the German Federal Council.

Polar coordinates

Pie plots

Rose plots

Axes of evil

Bar plot axes

Dual axes

Sensory overload

Chartjunk

Multiple plots

Congratulations

99 problems but a plot ain't one of them

Visualizing data using charts, graphs, and maps is one of the most impactful ways to communicate complex data. In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You'll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you'll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN's 100 most famous athletes, and the greatest hip-hop songs of all time.

The videos contain live transcripts, which can be accessed by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Learn how to visualize your data using charts, graphs and maps as well as how to interpret common plot time, best practices for colors, shapes, and more.

Chartjunk

Understanding Data Visualization

Praktische interactieve oefening