There are many data types in statistics, each of which has its way of representing it in an image. Let’s find out what those data types are and how we can visualize them in this article.

## Why is Data Visualization Important?

Before discussing anything else, we first need to discuss the importance of data visualization. By graphing information, you essentially make it easier to analyze for humans. While machines can take in enormous amounts of raw data at once, humans can’t do so. Therefore, including raw data but not graphs in your report will result in readers losing attention the situation and eventually giving up reading your report, which is not what you want.

Graphs provide a much more comfortable way of knowing the properties of the data as it’s essentially a summary of the distributions, trends, and correlations of the information, which readers can learn about at first glance, improving their chances of being retained in the report.

#### Discrete vs Continuous Data

Before we continue with the actual methods, let’s just explain one essential concept used for visualizing data: Discrete and continuous data. Continuous data can take on any arbitrary value. For example, measured information, such as lengths, areas, mass, or density, is continuous as the numerical values of the data points are often irrational (i.e., numbers that have infinitely many decimal places that are not repeating). On the other hand, data such as categories, sentiments, or anything countable is discrete data, such as the number of planets in a planetary system or the grades students receive.

#### The Types of Charts

Charts are often used to represent data visually. For humans, processing images are always faster than processing text, and when information is more organized and straightforward than its raw form, it is easier to convey the idea. Here are the types of charts that we can use to visualize data:

- Bar charts
- Pie charts
- Line charts
- Histograms
- Scatter plots
- Box plots
- Heatmaps

We’ll explain their properties and how to use them accordingly in the rest of this article.

#### Bar Charts

Bar charts are graphs that use bars to represent the quantities or numerical values of discrete data categories. For instance, bar charts are ideal if you are reviewing the sales of different products in your company.

Bar charts can be clustered to compare more than one item at a time for one data group. In that case, use different colors for the different bars representing different discrete data points within one data group. The bar chart is called a clustered bar chart if it satisfies this condition. On the other hand, bar charts can also be stacked upon each other. They often indicate sums of different related values in one data point.

#### Pie Charts

Pie charts are graphs that use a circle to represent the sum of the data points, and segments of the circle signal the percentages that one piece of data occupies in the sum of data points. However, pie charts cannot be used to visualize the exact data values, and if you would like to introduce this property to your chart, you’d better off making a bar chart instead. For instance, if you want to graph the categories of exoplanets known so far, you should use a pie chart because the percentages, instead of the numerical values, are often what matters the most.

#### Line Charts

Line charts are graphs that illustrate the flow of the values of data points over a sequence of parameters. Points are marked above the data category, and adjacent points are connected using straight lines. They are used for a variety of purposes to indicate trends. For example, if you want to show the number of clicks your website has been receiving over the past few months, or if you want to display how many planets have been discovered over the years, you should use a line chart.

#### Histograms

Histograms are similar to bar charts, but the bars have no gaps. These diagrams represent data categories as ranges of values. Using bar charts is not very good in these circumstances as they are often used to showcase non-numerical categories. On the other hand, a line chart might also be unsuitable for this because one point in a line chart usually shows single values instead of sums. For instance, if you want to graph the distribution of the sizes of exoplanets, histograms are perfect for this purpose.

#### Scatter Plots

Scatter plots involve points drawn on a 2D space, with the coordinates of the x- and y-axis being numerical values. They are often used in determining correlations as it involves the relationship between two variables, which can be plotted around the scatter plot. However, it is unsuitable for use in non-numerical discrete data because correlation cannot be derived from them.

#### Box Plots

Box plots are a way to visualize the distribution of a set of data. This includes the minimum value, the 1st quartile, the median, the 3rd quartile, and the maximum value of the set. This can become useful in determining how well a group of students scored in an exam, for example, or how many clicks each link receives on a website.

#### Heatmaps

Heatmaps assign each piece of data with a color on a spectrum based on its numerical value. The larger the value, the further down the scale the color that represents it. Therefore, it helps compare large amounts of data quantities at once. For instance, you can use heatmaps to look at the most popular and least popular links your visitors are clicking on at first glance.

#### How to Visualize Data?

After discussing the purposes of graphs, you should have an approximate idea of which chart you should choose. Now, you should make the graph. You should not use spreadsheet functions or simple graphic design tools like Canva to generate charts unless you’re dealing with tiny amounts of data. Instead, your best bet to visualizing larger amounts of data is to use Python libraries to make graphs. Matplotlib and Seaborn are libraries made for this task.

First, you might need to install those modules because they are not provided by default in Python. Run:

```
pip install matplotlib
pip install seaborn
```

After running these two commands, you will be ready with both libraries present on your computer. After that, you should load the dataset into place and graph it according to your own needs. If you want to learn how we can use these libraries, see theÂ MatplotlibÂ andÂ Seaborn tutorialsÂ on W3schools.

#### Conclusion

In this article, we’ve discussed how we should visualize data, including the graphs you can use, and how you should draw charts with readily available tools. Remember to visualize data whenever you need to present it to benefit your readers!