Loved by learners at thousands of companies
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.
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.
Visualizing two variables
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 plots50 xpInterpreting scatter plots100 xpTrends with scatter plots50 xpLine plots50 xpInterpreting line plots100 xpLogarithmic scales for line plots50 xpLine plots without dates on the x-axis50 xpBar plots50 xpInterpreting bar plots50 xpInterpreting stacked bar plots50 xpDot plots50 xpInterpreting dot plots50 xpSorting dot plots50 xp
The color and the shape
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 dimensions50 xpAnother dimension for scatter plots50 xpAnother dimension for line plots50 xpUsing color50 xpEye-catching colors50 xpQualitative, sequential, diverging50 xpHighlighting data50 xpPlotting many variables at once50 xpInterpreting pair plots100 xpInterpreting correlation heatmaps100 xpInterpreting parallel coordinates plots100 xp
99 problems but a plot ain't one of them
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.
Data Evangelist at DataCamp
Richie is a Data Evangelist at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.