Course Description
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.
- 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
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
- Line plots without dates on the x-axis
- Bar plots
- Interpreting bar plots
- Interpreting stacked bar plots
- Dot plots
- Interpreting dot plots
- Sorting dot plots
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
- Interpreting parallel coordinates plots
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