BASIC DATA VISUALIZATION HOW TO

Want to learn how to create data visualizations? Follow this free introductory data visualization tutorial. A common example of a line graph in action is the measure of stock market behavior or resource costs over time, e.g. This is useful for spotting correlations or deviation. While the simple line graph shown represents a single dataset, more complex line graphs may overlay several lines to represent different data. This allows for quick observation of features like acceleration (when the line goes up), deceleration (when the line goes down), and volatility (when the line moves up and down erratically). They are visually similar to scatterplots but represent data points separated by time intervals with segments joined by a line. Line graphs, or line charts, are a simple but effective staple for representing time-series data. Displaying different-sized circles (rather than single points), bubble charts represent three dimensions of data, rather than the usual two.

Nevertheless, they are an excellent tool for hypothesis creation.Ī common variant of the scatterplot is the bubble chart. people’s weights are also related to their diet) scatterplots are best for inferring relationships between variables rather than drawing firm conclusions. However, because other factors affect the data (e.g. This would help identify any correlation between the two measures. For instance, a simple scatterplot might measure people’s weight against height. Scatterplots are best used for large datasets where time is not a significant factor. if there’s a z-axis) 3D scatterplots are also possible. When there are three interrelated data points (i.e. Each item of data is shown as a single point, creating the chart’s visual ‘scatter’ effect. ScatterplotsĪ scatterplot (also known as a scattergraph, scattergram, or scatter chart) displays the relationship between two variables on an x- and y-axis. getting to understand a dataset’s key features before drawing any broad conclusions about it. This makes them useful for exploratory and explanatory data analysis, i.e. the median of the lower and upper halves of the data).īoxplots are what is known as ‘non-parametric.’ This means they display variation in a data sample without making any assumptions about the data’s distribution. We can use them to represent minimum and maximum values, the median value, and the lower and upper quartiles (i.e. Like pivot tables, boxplots are useful for visualizing a dataset’s key statistics. BoxplotsĪnother useful (if not particularly flashy) type of descriptive visualization is the boxplot (also known as a box-and-whisker plot). You can learn how to create a pivot table here. They are also commonly used to underpin more complex forms of data visualization, hence making it on to our list.

Sometimes, this is all you need, making pivot tables a basic but effective form of data viz. For instance, highlight tables, as shown in the image, use different shades or colors to easily flag the highest and lowest values in a dataset. While pivot tables aren’t always the most visually inspiring form of data viz, they are useful in the right context. The type of information collected in pivot tables might include sums, means, or other numerical summaries. You might not think of tables as a form of data visualization, but they are! When dealing with vast repositories of information-ones that are too large to easily comprehend-pivot tables help us summarize key statistics in a single view. In this post, we’ll cover 13 of the most common ones, starting with… 1. But before we can unlock all these benefits, we first need to understand the basics, including the different types of data visualization and how they’re used. Master the art of data viz and you’ll soon be spotting trends and correlations, all while flexing your creative muscle. And while the priority should always be the integrity of your data, if done well, data visualization can also be a lot of fun. Mapping raw data using graphical elements is great for aiding pattern-spotting and it’s useful for sharing findings in an easily digestible, eye-catching way. But what are the most common types of data visualization? Let’s find out.ĭata visualization (or ‘data viz’) is one of the most important aspects of data analytics. In data analytics, data visualization is a vital technique for pattern-spotting and presenting findings.

Basic data visualization