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
Data Preparation in Tableau

Data Preparation in Tableau

Data preparation is a crucial step in the data analysis process. It involves cleaning, transforming, and organizing raw data into a format that is suitable for analysis. In Tableau, data preparation tools help you ensure that your data is accurate, consistent, and ready for visualization.

Key Concepts

1. Data Cleaning

Data cleaning involves identifying and correcting or removing inaccuracies, inconsistencies, and redundancies in your dataset. This step ensures that your data is reliable and can be used for meaningful analysis.

Example: Suppose you have a dataset with missing values in the 'Sales' column. You can use Tableau's data preparation tools to either fill in these missing values with an average or remove the rows entirely, depending on the context.

2. Data Transformation

Data transformation involves converting data from one format or structure to another. This can include tasks like normalizing data, aggregating values, or splitting columns. The goal is to make the data more suitable for analysis and visualization.

Example: If your dataset has a 'Date' column in a format like 'YYYY-MM-DD', you might want to split this into separate 'Year', 'Month', and 'Day' columns for easier analysis. Tableau allows you to perform such transformations directly within the tool.

Detailed Explanation

Data Cleaning

Data cleaning is essential to ensure the accuracy and reliability of your analysis. Common tasks include:

Data Transformation

Data transformation helps in making the data more manageable and suitable for analysis. Common tasks include:

Examples

Data Cleaning Example

Suppose you have a dataset with the following structure:

    | Product | Sales |
    |---------|-------|
    | A       | 100   |
    | B       |       |
    | C       | 150   |
    

You can clean this data by filling in the missing 'Sales' value for Product B with the average sales:

    | Product | Sales |
    |---------|-------|
    | A       | 100   |
    | B       | 125   |
    | C       | 150   |
    

Data Transformation Example

Consider a dataset with a 'Date' column in the format 'YYYY-MM-DD':

    | Date       | Sales |
    |------------|-------|
    | 2023-01-01 | 100   |
    | 2023-02-01 | 150   |
    | 2023-03-01 | 200   |
    

You can transform this data by splitting the 'Date' column into 'Year', 'Month', and 'Day':

    | Year | Month | Day | Sales |
    |------|-------|-----|-------|
    | 2023 | 01    | 01  | 100   |
    | 2023 | 02    | 01  | 150   |
    | 2023 | 03    | 01  | 200   |
    

By mastering data preparation in Tableau, you can ensure that your data is clean, consistent, and ready for insightful analysis and visualization.