What is Data Visualization?

1. Bar Chart

At some point or another, you've either seen, interacted with, or built a bar chart before. Bar charts are such a popular graph visualization because of how easy you can scan them for quick information. Bar charts organize data into rectangular bars that make it a breeze to compare related data sets.

When do I use a bar chart visualization?

Use a bar chart for the following reasons:

• You want to compare two or more values in the same category
• You want to compare parts of a whole
• You don’t have too many groups (less than 10 works best)
• You want to understand how multiple similar data sets relate to each other

Don’t use a bar chart for the following reasons:

• The category you’re visualizing only has one value associated with it
• You want to visualize continuous data

Best practices for a bar chart visualization

If you use a bar chart, here are the key design best practices:

• Use consistent colours and labeling throughout so that you can identify relationships more easily
• Simplify the length of the y-axis labels and don’t forget to start from 0 so you can keep your data in order

3. Scatterplot

Scatterplots are the right data visualizations to use when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or for understanding the distribution of your data.

If the data forms a band extending from lower left to upper right, there most likely a positive correlation between the two variables. If the band runs from upper left to lower right, a negative correlation is probable. If it is hard to see a pattern, there is probably no correlation.

When do I use a scatter plot visualization?

Use a scatterplot for the following reasons:

• You want to show the relationship between two variables
• You want a compact data visualization

Don’t use a scatterplot for the following reasons:

• You want to rapidly scan information
• You want clear and precise data points

Best practices for a scatter plot visualization

If you use a scatterplot, here are the key design best practices:

• Although trend lines are a great way to analyze the data on a scatterplot, ensure you stick to 1 or 2 trend lines to avoid confusion
• Don’t forget to start at 0 for the y-axis

5. Pie Chart

Pie charts are an interesting graph visualization. At a high-level, they're easy to read and understand because the parts-of-a-whole relationship is made very obvious. But top data visual experts agree that one of their disadvantages is that the percentage of each section isn’t obvious without adding numerical values to each slice of the pie.

So, what’s the point? As long as you stick to best practices, pie charts can be a quick way to scan information.

When do I use a pie chart visualization?

Use a pie chart for the following reasons:

• You want to compare relative values
• You want to compare parts of a whole
• You want to rapidly scan metrics

Don’t use a pie chart for the following reason:

• You want to precisely compare data

Best practices for a pie chart visualization

If you use a pie chart, here are the key design best practices:

• Make sure that the pie slices add up to 100%. To make this easier, add the numerical values and percentages to your pie chart
• Order the pieces of your pie according to size
• Use a pie chart if you have only up to 5 categories to compare. If you have too many categories, you won’t be able to differentiate between the slices

7. Waterfall Chart

A waterfall chart is an information visualization that should be used to show how an initial value is affected by intermediate values and resulted in a final value. The values can be either negative or positive.

When do I use a waterfall chart visualization?

Use a waterfall chart for the following reason:

• To reveal the composition or makeup of a number

Don’t use a waterfall chart for the following reason:

• You want to focus on more than one number or metric

Best practices for a waterfall chart visualization

If you use a waterfall chart, here are the key design best practices:

• Use contrasting colors to highlight differences in data sets
• Choose warm colors to indicate increases and cool colors to indicate decreases

9. Heat Map

A heat map or choropleth map is a data visualization that shows the relationship between two measures and provides rating information. The rating information is displayed using varying colors or saturation and can exhibit ratings such as high to low or bad to awesome, and needs improvement to working well.

It can also be a thematic map in which the area inside recognized boundaries is shaded in proportion to the data being represented.

When do I use a heat map visualization?

Use a heat map for the following reasons:

• To show a relationship between two measures
• To illustrate an important detail
• To use a rating system

Don’t use a heat map for the following reason:

• To visualize individual, unconnected metrics

Best practices for a heat map visualization

If you use a heat map, here are the key design best practices:

• Use a simple map outline to avoid distracting from the data
• Use a single color in varying shades to show changes in data
• Avoid using multiple patterns

11. Box Plot

(Source: Python Graph Gallery)

A box plot, or box and whisker diagram, is a visual representation of displaying a distribution of data, usually across groups, based on a five number summary: the minimum, first quartile, the median (second quartile), third quartile, and the maximum.

The simplest of box plots display the full range of variation from minimum to maximum, the likely range of variation, and a typical value. A box plot will also show the outliers.

When do I use a box plot visualization?

Use a box plot for the following reasons:

• To display or compare a distribution of data
• To identify the minimum, maximum and median of data

Don’t use a box plot for the following reason:

• To visualize individual, unconnected data sets

Best practices for a box plot visualization

If you use a box plot, here are the key design best practices:

• Ensure font sizes for labels and legends are big enough and line widths are thick enough to understand the findings easily
• If plotting multiple datasets, use different symbols, line styles or colour to differentiate each
• Always remove unnecessary clutter from the plots