A dot plot chart is a type of scatter plot that shows the values of a numerical variable for different categories or groups using dots. It can be useful for comparing values across categories or groups, especially when there are many categories or groups.

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In this article, we will learn how to create a dot plot chart in Tableau, how businesses can benefit from using this chart, some interview questions on this topic, and why learning this can help in your career.

To create a dot plot chart in Tableau, we need to have a data set that contains at least one numerical variable (such as sales amount, profit ratio, customer satisfaction) and one categorical variable (such as product name, region name, segment name) that we want to compare.

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For example, let’s use the Sample – Superstore data set that comes with Tableau. This data set contains information about orders placed by customers for different products across different regions and segments.

We want to create a dot plot chart that shows the sales amount by product category and region.

Here are the steps to create a dot plot chart in Tableau:

1. Connect to the Sample – Superstore data source in Tableau.
2. Drag Category to Columns and Region to Rows.
3. Drag Sales to Text on Marks card.
4. On Marks card, change Automatic to Circle.
5. Right-click on Sales and choose Format. In the Numbers tab under Default section, choose Currency (Custom) for Number Format option and 0 for Decimal Places option.
6. Right-click on any axis and choose Add Reference Line. Choose Entire Table for Scope and Average for Type. Choose Value for Label option and Formatting options as desired.

Dot plot charts can help businesses gain insights into how their numerical values vary across different categories or groups. For example,

• A retail company can use dot plot charts to compare their sales amount by product category and region and identify which categories or regions are performing well or poorly.
• A marketing company can use dot plot charts to compare their customer satisfaction by product type and channel and determine which product types or channels are generating more or less satisfaction.
• A manufacturing company can use dot plot charts to compare their defect rate by product line and factory and find out which product lines or factories are having more or less quality issues.

By using dot plot charts, businesses can benchmark their performance or results against the average or other categories or groups and take appropriate actions or decisions based on them.

If you are applying for a data analyst role that requires Tableau skills,
you may encounter some interview questions on dot plot charts.

Here are some examples of interview questions on dot plot charts:

• What is a dot plot chart and when would you use it?
• How do you create a dot plot chart in Tableau?
• What are some advantages and disadvantages of using dot plot charts?
• How do you interpret a dot plot chart? What insights can you derive from it?
• How do you customize the appearance of a dot plot chart in Tableau?
• How do you handle missing values or outliers when creating
  a dot plot chart?

To answer these questions effectively, you should be able to explain what a dot plot chart is, how it works, and what it shows; demonstrate how
to create one using Tableau; discuss its pros and cons; analyze its results
and communicate its findings; and apply various formatting options and techniques to enhance its readability and functionality.

In addition to general interview questions, you may also face some technical round questions that test your ability to create and manipulate dot plot charts using real-world data sets.

Here are some examples of technical round questions on dot plot charts:

• Given this data set that contains information about employee salaries by department, create a dot plot chart that shows the salary by department and gender. Employee Salaries Data Set
• Given this data set that contains information about wine ratings by variety,
  create a dot plot chart that shows the rating by variety and country. Wine Ratings Data Set

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