# Data Visualization 101

Updated: Jan 23

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Imagine – you have been in your classroom for three hours, slaving away over the heat of a freshly minted spreadsheet. You have performed your calculations, examined the differences between groups, and have made some really interesting and actionable discoveries. It’s time to share your findings with your team. How do you do make your data tell a story? Enter: The Data Visualization.

Data visualizations, also called graphs or charts, help users understand your data by turning numbers into a graphic representation. They are super helpful for your readers because, pardon the proverb, a picture tells a thousand words. In this post, I want to give you a run down of the five basic types of data visualizations and give you some tips on when to use them.

**The Scatter Plot**

A scatter plot is a type of plot in which each point represents a piece of data. It is used to show the relationship between two variables, which are laid out along the x and y axis. Consider the model scatter plot below. In this plot, each dot represents the intersection of two variables. We can clearly see that there is a strong, positive relationship between these two variables. This graph is showing us a correlation, it tells us only that the two variables are related, but cannot determine if one variable caused another.

**The Line Graph**

A line graph is a type of visualization that uses a horizontal line to show change over time. The outcome variable is along the y axis (or the vertical axis) and the time series is along the x axis (or horizontal axis). Each data point is connected by a dot. Line graphs are great when you need to quickly and accurately show trends over time. The line graph below shows the data points for a variable over a twenty year period of time. You can clearly see an upward trend of the data, demonstrating progress or growth on the variable.

**The Bar Chart**

A bar chart is a type of plot that uses vertical bars to show the number of items in a category. They are great when you need to show the breakdown of a population. The bar chart below shows the count of cats, dogs, fish, and turtles in the home of some fictitious students. With this visualization, you can quickly see that dogs are the most popular pet, while very few students have turtles.

**The Histogram**

A histogram is a type of visualization that uses vertical, contiguous bars to show a distribution of data. Each bar represents the number of times a score showed up within a given range, called a bin. Think of it like a bucket of scores. The histogram helps you to see how your scores are distributed by showing you how many scores are in that bucket. It differs from the bar plot above in that all of the bars represent observations of a single variable, whereas the bar plot represents the number of observations over multiple variables. In the histogram below, you can easily see that the third bucket of scores has the largest number of occurrences, at 10 and that there are fewer occurrences on the outskirts of the distribution.

**The Boxplot**

A boxplot, sometimes called a box and whisker plot, is a type of visualization that combines multiple descriptive statistics into one picture to help you understand your scores. The boxplot is made up of a box, that shows you the boundaries of the second and third quartiles. The horizontal line across the middle shows you the median, while the x shows you the mean. The whiskers represent the outer boundaries of the first and fourth quartile. The dot at the top represents an outlier. Boxplots are great when you want to show how a distribution has changed from one period to the next, such as a pre-test to a post-test. The boxplots below show how you can easily depict multiple visualizations together to clearly show how they changed.

I hope this brief run down of the most common types of data visualizations has given you some food for thought the next time you have to present data to a group. If you want to build visualizations faster, consider uploading your data into one of my free data analysis tools, located in __The Repository__. All you have to do is upload your data and the tools will automatically generate appropriate plots that you can copy and paste into your reports. Good luck on your journey friends, and let me know how I can help.