Tableau TDS-C01 - Tableau Desktop Specialist

To visually group marks in Tableau, you typically start by selecting the marks directly in the view. Once the desired marks are selected, you can then create a group from them. This will visually group the selected marks, as indicated by the blue color in the example provided.

Explanation

Yes, it is possible to create worksheet-specific filters in Tableau.

When you add a filter to a worksheet, by default it applies to the current worksheet. Sometimes, however, you might want to apply the filter to other worksheets in the workbook.

Then, you can select specific worksheets to apply the filter to or apply it globally to all worksheets that use the same data source or related data sources.

Explanation

SUM is an aggregate function, not a table calculation!

A table calculation is a transformation you apply to the values in a visualization. Table calculations are a special type of calculated field that computes on the local data in Tableau. They are calculated based on what is currently in the visualization and do not consider any measures or dimensions that are filtered out of the visualization.

The most common Table calculations are:

Running Total

Percent Difference

Difference

Percent of Total

Rank

Percentile

These can be calculated using : Table(across), Cell, or Specific dimensions!

Important question! If you selected Dimension and Measure, don't worry! It is a very common mistake. But we're here to learn aren't we?

When you connect to a new data source, Tableau assigns each field in the data source as dimension or measure in the Data pane, depending on the type of data the field contains. You use these fields to build views of your data.

By default, all filters that you set in Tableau are computed independently. That is, each filter accesses all rows in your data source without regard to other filters. However, you can set one or more categorical filters as context filters for the view. You can think of a context filter as being an independent filter (Option stating - To create a dependent filter eliminated here). Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter.

You may create a context filter to:

1) Improve performance – If you set a lot of filters or have a large data source, the queries can be slow. You can set one or more context filters to improve performance.

2) Create a dependent numerical or top N filter – You can set a context filter to include only the data of interest, and then set a numerical or a top N filter.

For example, suppose you’re in charge of breakfast products for a large grocery chain. Your task is to find the top 10 breakfast products by profitability for all stores. If the data source is very large, you can set a context filter to include only breakfast products. Then you can create a top 10 filter by profit as a dependent filter, which would process only the data that passes through the context filter.

Explanation

You can union your data to combine two or more tables by appending values (rows) from one table to another. To union your data in Tableau data source, the tables must come from the same connection.

For example, suppose you have the following customer purchase information stored in three tables, separated by month. The table names are "May2016," "June2016," and "July2016."

A union of these tables creates the following single table that contains all rows from all tables.

Explanation

Here's how to add image files to your repository:

1) Find image file on the internet. I try to find consistent image formats if I plan to use a set of shapes such as logos or flags.

2) Download the image to your computer.

3) Drag images into your My Documents -> My Tableau repository -> Shapes folder.

4) Open Tableau and your new shapes will automatically be included in your "edit shapes" menu.

The Tableau workspace consists of menus, a toolbar, the Data pane, cards and shelves, and one or more sheets. Sheets can be worksheets, dashboards, or stories. For details on dashboard or story workspaces, see Create a Dashboard or The Story Workspace.

Graphical user interface, application Description automatically generated

The main thing you do in the workspace is to create visualisations to analyze your data.

Renaming data fields, cleaning the data, previewing the data can all be done in the Data source window.

To create a simple scatter plot in Tableau, you need to have a measure on Columns and a measure on Rows. A scatter plot is a type of visualization that shows the relationship between two numerical variables. In Tableau, you can create a scatter plot by placing at least one measure on the Columns shelf and at least one measure on the Rows shelf. The measures can be continuous or discrete, but they must be aggregated. The marks in the scatter plot represent the intersection of the values for each measure1 The other options are not valid ways to create a simple scatter plot in Tableau. A dimension on Detail and a measure on Columns will create a bar chart, not a scatter plot. A dimension on Columns and a measure on Rows will create a line chart or an area chart, depending on the mark type. A dimension on Columns and a dimension on Rows will create a text table or a heat map, depending on the mark type1

Explanation

Tableau Data Extract (TDE) is a snapshot of the data that Tableau stores locally. Good for very large datasets of which we only need few fields. Performance is optimised because it queries its own database engine instead of the local data source.

When you create an extract of your data, you can reduce the total amount of data by using filters and configuring other limits. After you create an extract, you can refresh it with data from the original data. When refreshing the data, you have the option to either do a full refresh, which replaces all of the contents in the extract, or you can do an incremental refresh, which only adds rows that are new since the previous refresh.

Extracts are advantageous for several reasons:

1) Supports large data sets: You can create extracts that contain billions of rows of data.

2) Fast to create: If you're working with large data sets, creating and working with extracts can be faster than working with the original data.

3) Help improve performance: When you interact with views that use extract data sources, you generally experience better performance than when interacting with views based on connections to the original data.

4) Support additional functionality: Extracts allow you to take advantage of Tableau functionality that's not available or supported by the original data, such as the ability to compute Count Distinct.

5) Provide offline access to your data: Extracts allow you to save and work with the data locally when the original data is not available. For example, when you are traveling.