Tableau Desktop Certified Professional Training , study and exam guide
1 Introduction to Tableau
1.1 Overview of Tableau Desktop
1.2 Understanding the Tableau Interface
1.3 Connecting to Data Sources
1.4 Data Types and Hierarchies
2 Data Preparation
2.1 Data Blending
2.2 Data Joining
2.3 Data Aggregation
2.4 Data Cleaning Techniques
2.5 Data Pivoting
3 Building Visualizations
3.1 Creating Basic Charts
3.2 Customizing Charts
3.3 Using Marks and Filters
3.4 Creating Dashboards
3.5 Storytelling with Stories
4 Advanced Visualizations
4.1 Creating Maps
4.2 Using Parameters
4.3 Creating Calculated Fields
4.4 Using Level of Detail Expressions
4.5 Creating Sets and Groups
5 Data Analysis
5.1 Understanding Table Calculations
5.2 Using Quick Filters and Context Filters
5.3 Creating Hierarchies
5.4 Analyzing Data with Trend Lines
5.5 Using Forecasting
6 Performance Optimization
6.1 Understanding Tableau Data Engine
6.2 Using Extracts
6.3 Optimizing Data Connections
6.4 Managing Metadata
6.5 Using Tableau Prep for Data Preparation
7 Collaboration and Sharing
7.1 Publishing Workbooks to Tableau Server
7.2 Using Tableau Online
7.3 Sharing Views and Dashboards
7.4 Managing Permissions and Access
7.5 Using Tableau Mobile
8 Certification Exam Preparation
8.1 Understanding Exam Format
8.2 Practice Questions and Simulations
8.3 Time Management Strategies
8.4 Reviewing Key Concepts
8.5 Taking the Exam
Advanced Visualizations in Tableau

Advanced Visualizations in Tableau

Advanced visualizations in Tableau allow you to delve deeper into your data and uncover complex insights. This section will guide you through four essential advanced visualizations: Heat Maps, Scatter Plots, Box Plots, and Bullet Charts.

Key Concepts

1. Heat Maps

A Heat Map is a graphical representation of data where individual values are represented as colors. This visualization is particularly useful for showing patterns and trends in large datasets. Heat Maps are ideal for visualizing data such as sales performance across different regions or customer satisfaction scores over time.

Example:

        1. Drag the "Region" field to the Columns shelf.
        2. Drag the "Product" field to the Rows shelf.
        3. Drag the "Sales" field to the Color shelf.
        4. Tableau automatically creates a Heat Map showing sales by region and product.
    

2. Scatter Plots

A Scatter Plot is used to display the relationship between two numerical variables. Each point on the plot represents an observation, and the position of the point corresponds to the values of the two variables. Scatter Plots are ideal for identifying correlations and outliers in your data.

Example:

        1. Drag the "Profit" field to the Columns shelf.
        2. Drag the "Sales" field to the Rows shelf.
        3. Drag the "Category" field to the Color shelf.
        4. Tableau automatically creates a Scatter Plot showing the relationship between profit and sales by category.
    

3. Box Plots

A Box Plot, also known as a box-and-whisker plot, is used to display the distribution of data based on a five-number summary: minimum, first quartile, median, third quartile, and maximum. Box Plots are ideal for comparing the spread and central tendency of different groups of data.

Example:

        1. Drag the "Category" field to the Columns shelf.
        2. Drag the "Sales" field to the Rows shelf.
        3. Change the Mark Type to "Box Plot" in the Marks card.
        4. Tableau automatically creates a Box Plot showing the distribution of sales by category.
    

4. Bullet Charts

A Bullet Chart is a variation of a bar chart designed to compare a single measure against a target value. Bullet Charts are ideal for showing progress towards a goal and are commonly used in dashboards for performance tracking.

Example:

        1. Drag the "Region" field to the Columns shelf.
        2. Drag the "Sales" field to the Rows shelf.
        3. Create a calculated field for the target sales value.
        4. Drag the target sales field to the Rows shelf.
        5. Change the Mark Type to "Bar" and "Line" for the actual and target sales, respectively.
        6. Tableau automatically creates a Bullet Chart showing actual sales compared to the target sales by region.
    

Detailed Explanation

Heat Maps

In a Heat Map, the color intensity represents the magnitude of the data. Darker colors indicate higher values, while lighter colors indicate lower values. This makes it easy to identify hotspots and trends in your data. For example, a Heat Map can show which regions have the highest sales for specific products.

Scatter Plots

In a Scatter Plot, the position of each point on the plot represents the values of two variables. This allows you to see the relationship between the variables, such as whether they are positively or negatively correlated. For example, a Scatter Plot can show whether higher sales are associated with higher profits.

Box Plots

In a Box Plot, the box represents the interquartile range (IQR), which contains the middle 50% of the data. The line inside the box represents the median, and the whiskers extend to the minimum and maximum values. This makes it easy to compare the distribution of data across different categories. For example, a Box Plot can show whether the sales distribution is similar across different product categories.

Bullet Charts

In a Bullet Chart, the bar represents the actual value, and the line represents the target value. This allows you to quickly compare the actual performance against the target. For example, a Bullet Chart can show whether sales in a region are meeting the target.

Examples and Analogies

Heat Maps

Think of a Heat Map as a weather forecast. Darker colors represent areas with higher temperatures, while lighter colors represent areas with lower temperatures. Similarly, in a Heat Map, darker colors represent higher values in your data.

Scatter Plots

Think of a Scatter Plot as a constellation in the sky. Each star represents an observation, and the pattern of the stars shows the relationship between two variables. Similarly, in a Scatter Plot, each point represents an observation, and the pattern of the points shows the relationship between two variables.

Box Plots

Think of a Box Plot as a summary of a marathon race. The box represents the middle 50% of the runners, the line inside the box represents the median runner, and the whiskers represent the fastest and slowest runners. Similarly, in a Box Plot, the box represents the middle 50% of the data, the line inside the box represents the median, and the whiskers represent the minimum and maximum values.

Bullet Charts

Think of a Bullet Chart as a progress bar. The bar represents the current progress, and the line represents the target progress. Similarly, in a Bullet Chart, the bar represents the actual value, and the line represents the target value.

By mastering these advanced visualizations, you can effectively communicate complex insights from your data and make informed decisions. Each visualization type has its own strengths and is suited to different types of data and analysis.