Book Image

Tableau Certified Data Analyst Certification Guide

By : Mr. Harry Cooney, Mr. Daisy Jones
Book Image

Tableau Certified Data Analyst Certification Guide

By: Mr. Harry Cooney, Mr. Daisy Jones

Overview of this book

The Tableau Certified Data Analyst certification validates the essential skills needed to explore, analyze, and present data, propelling your career in data analytics. Whether you're a seasoned Tableau user or just starting out, this comprehensive resource is your roadmap to mastering Tableau and achieving certification success. The book begins by exploring the fundamentals of data analysis, from connecting to various data sources to transforming and cleaning data for meaningful insights. With practical exercises and realistic mock exams, you'll gain hands-on experience that reinforces your understanding of Tableau concepts and prepares you for the challenges of the certification exam. As you progress, expert guidance and clear explanations make it easy to navigate complex topics as each chapter builds upon the last, providing a seamless learning experience—from creating impactful visualizations to managing content on Tableau Cloud. Written by a team of experts, this Tableau book not only helps you pass the certification exam but also equips you with the skills and confidence needed to excel in your career. It is an indispensable resource for unlocking the full potential of Tableau.
Table of Contents (11 chapters)

Choosing an Appropriate Data Source Type

Before any user or developer can start building a visualization, an appropriate data source must be defined. Without data, there will be no visuals to be built and no stories to be told. Multiple factors should inform decision-making when it comes to choosing a data source. In summary, these include the following.

Content and Quality

The following points are key for any data preparation for a developer to be able to analyze data properly. It is important for you to familiarize yourselves with these practices as you will be questioned about them in the exam.

Level of detail: Dimensions and Measures

Tableau should be approached not merely as a tool for data visualization. Charts should be created and used thoughtfully as a means of answering business questions. Therefore, data should be selected with a goal in mind: does it contain the fields (columns) and records (rows) required to answer the questions at hand?

When it comes to fields, data should contain the appropriate dimensions (to divide the view) and measures (for assessable metrics). It is impossible to review the relative performance of each salesperson, for example, without their name or other unique identifiers alongside a Profit field. And if those fields are incomplete – lacking all salespeople, or profits for certain months of the year – then accurate conclusions cannot be drawn.

Data Quality

The previous point touched on completeness as an important facet. This can be expanded further: any data source used should have an appropriate level of completeness, accuracy, and consistency for the resulting insights to be valuable. You need to make sure that the data used is complete, all field names are named appropriately, and the spellings are kept consistent. Please see Chapter 2, Transforming Data, for further details.