Tableau Data Visualization Guide: Chart Types

Humans have biologically evolved to be visual creatures. In fact, our sight is implicated in some of the most important activities we rely on to make sense of the world around us, such as reading, measuring distance, or interpreting information on screen. And yet, no matter how advanced our eyes are, people can rarely make sense of raw data just by looking at it. Think of large spreadsheets or Python notebooks that house overwhelming amounts of information. How do you even begin extracting valuable insights? Thankfully, there are powerful data visualization tools, like Tableau, that you can quickly get the hang of.

In this article, we’ll discuss the importance of visual analytics for decision-making, and why Tableau is the leading data viz platform. Before going on to explore some of the chart types you can create using the software.

Why Is Data Visualization Important?
Why Tableau for Data Visualization?
What Are the Types of Charts for Data Visualization in Tableau?
Data Visualization in Tableau: Next Steps

Why Is Data Visualization Important?

Needless to say, data is a leading force in today’s economy. And being able to gather and preprocess it allows us to implement predictive modeling, machine learning, and other amazing data science tools to drive business growth. However, there is nothing more important than being able to interpret that data – especially for someone outside of the data science realm.

If you’re working at a company as part of its data team, it is highly likely that your audience would not be super technical. On the contrary, they will most likely be people whose job is to make strategic decisions based on your insights. More often than not, they won’t go out of their way to explore the underlying data unless you explain it to them in an easy-to-digest way. Remember, our brains take in and process visual information the fastest.

Data visualization helps people understand the numbers. As the person in charge of these visuals, you are the one to whom management turns when creating a focused business strategy.

In a way, we can say that data visualization is the “face of data science”. There may be hundreds of human hours behind gathering, preprocessing and analyzing the data, however, the key decision-makers in a business are likely to only trust the information they can visually make sense of.

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Why Tableau for Data Visualization?

There are many tools you can choose from, like Excel, and Power BI (or even Python’s data visualization packages), to create charts and dashboards for your data. But Tableau stands out from the crowd as the preferred software that data analysts and BI analysts use in their day-to-day workflow.

So, why choose Tableau for data visualization? Here are just some of the reasons:

Tableau Public is open-source and perfect for beginners in data visualization
It has a user-friendly interface that is easy to navigate
You can quickly integrate it with other software or programming languages like Python and SQL for more advanced analyses

There are many other benefits, including the fact that it is an extremely intuitive platform able to recognize data types and make suggestions based on the inputs. For example, it can offer appropriate chart types automatically. You then have the option to customize these further, and thus minimize workload while maximizing efficiency.

What Are the Types of Charts for Data Visualization in Tableau?

No data is the same, meaning each project will have its own unique requirements. To get the most out of your work, you need to understand what type of visualization will work best for every individual dataset. In this section, we’ll outline some of the more common chart types you’ll use when visualizing data in Tableau.

Bar Chart

Bar charts are one of the most popular ways to visualize your data, not just in Tableau. You’ll be pressed not to find one during business meetings, science seminars, or even news broadcasts. The reason is that bar charts are among the clearest and most straightforward visual representations of information. As such, they are also easily interpreted by an audience, regardless of their technical qualifications or background.

It is highly likely that you will use this particular form of visualization regularly on the job. Therefore, mastering the bar chart is one of the most fundamental skills you can gain. Luckily, Tableau is quite intuitive (as we previously said) and it will guide you through the process.

Pie Chart

Pie charts are another popular visualization type that represents your variables as parts of a circle. Keep in mind, though, that they are best suited for displaying data that sums up to 100%.

While they’re very common, we don’t recommend using pie charts. The reason for this is the human eye is just not great at determining the size of non-rectangular-shaped objects – it is often very difficult for the audience to visually compare the different slices and order them from largest to smallest. If you can, simply use a bar chart instead.

Now, although pie charts are usually not the best way to visualize data, being able to create one is a must if you’re working as a data analyst, Tableau developer, or in another data-visualization-related profession.

Line Chart

A line chart is exactly what it says on the tin – lines running across the graph that represent the data at hand. More precisely, they show the evolution of one or several quantities and their behavior over time. If your task is to follow a value’s timeline or identify trends in a dataset, then this is the chart for you.

In data science terms, we can safely say that a line chart is most often used to represent time series data. A great example is the visualization of financial information – you’d be able to trace how stock market returns changed over a specific timeframe.

Histogram

First and foremost, you shouldn’t confuse this with a bar chart – the 2 types serve different purposes. In essence, a histogram shows the distribution of a numeric variable. The range of values is split into intervals, represented by different bars, more commonly known as bins.

Meanwhile, the height of these bins shows the number of observations within an interval. Depending on the data, the histogram might skew to the left, the right, or peak at the center.

It may sound complicated, but this is one of the most fundamental and useful tools for understanding numerical data.

Scatter Plot

A scatter plot shows the relationship between 2 numerical features. The observations are displayed as points on the graph where the “X coordinate” is one of the variables, while the “Y coordinate” is the other.

This chart is extremely useful and has one big advantage: scatter plots, unlike other visualizations, are able to display a large number of points. Whereas some graphs are limited to clearly showing just a few elements, in a scatter you can easily display a few hundred. Most notably, each observation is a distinct data point on that scatter.

Q&A

What Is Tableau Data Visualization?

Tableau is a popular software for data analysis and data visualization, used by both professional and aspiring data scientists. The main advantages of this platform stem from its ease of use, intuitive interface, and powerful visual analytic abilities. Tableau also allows you to integrate different file formats – Excel, text files in the CSV form, JSON files, PDFs, and many others – that will help you create presentations with better insights (the software has some analysis features, but they are fairly limited compared to other programming tools). There are 2 versions of this software: a Public version that is perfect if you’re just starting out with data visualization, and a paid subscription that allows you to do more complex analyses and save your work directly on your PC rather than Tableau’s server (like in the free version).

Is Tableau Good for Data Visualization?

Yes, Tableau is great for data visualization. It is perhaps the leading data viz platform amongst data professionals and beginners alike. With a large selection of chart types, you can create the most appropriate visuals for your dataset that suit the needs of your stakeholders. Moreover, you can combine charts, like bars and lines or regression plots, for more complex dashboards that incorporate your data in a cohesive way. Tableau’s intuitive nature is an added benefit as it will automatically suggest the type of chart for your data, which you can then further tailor to your needs. Compared to other data viz tools, it allows for a lot more customization and complex graphs. In fact, Tableau can handle double the data than Excel, for example – a great advantage when you’re trying to calculate and present a large amount of information to your company’s management.

How Do You Visualize in Tableau?

Visualizing data in Tableau is done entirely through its interface’s workspace – otherwise known as a Workbook. In turn, a workbook is divided into separate sheets (which you might recognize as the concept is similar to Excel’s structure). A workbook can contain 3 different types of sheets. The first type is a worksheet - a single view, containing ‘shelves’, ‘cards’, ‘legends’, as well as the ‘Data’ and ‘Analytics’ panels. The second type is a dashboard – a collection of views from multiple worksheets. And lastly, a sheet can be a story, which is a sequence of worksheets or dashboards, working together to convey information. After importing your dataset, you’ll mainly be working with the data pane on the left side in order to categorize your data. Meanwhile, the visualization options are located on the upper right – a button called ‘Show me’, marked with a signature color histogram. There, you’ll find the different chart types that are at your disposal.

Data Visualization in Tableau: Next Steps

Visual analytics add value and help businesses convert insights into revenue. You will become an indispensable part of any data team by showing you know how to extract meaning out of a company’s reserves. Thus, Tableau is an extremely useful skill for someone looking to create compelling visualizations that help others “see” the connections within their data.

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