Book Image

Tableau Desktop Certified Associate: Exam Guide

By : Dmitry Anoshin, JC Gillet, Fabian Peri, Radhika Biyani, Gleb Makarenko
Book Image

Tableau Desktop Certified Associate: Exam Guide

By: Dmitry Anoshin, JC Gillet, Fabian Peri, Radhika Biyani, Gleb Makarenko

Overview of this book

The Tableau Desktop Certified Associate exam measures your knowledge of Tableau Desktop and your ability to work with data and data visualization techniques. This book will help you to become well-versed in Tableau software and use its business intelligence (BI) features to solve BI and analytics challenges. With the help of this book, you'll explore the authors' success stories and their experience with Tableau. You'll start by understanding the importance of Tableau certification and the different certification exams, along with covering the exam format, Tableau basics, and best practices for preparing data for analysis and visualization. The book builds on your knowledge of advanced Tableau topics such as table calculations for solving problems. You'll learn to effectively visualize geographic data using vector maps. Later, you'll discover the analytics capabilities of Tableau by learning how to use features such as forecasting. Finally, you'll understand how to build and customize dashboards, while ensuring they convey information effectively. Every chapter has examples and tests to reinforce your learning, along with mock tests in the last section. By the end of this book, you'll be able to efficiently prepare for the certification exam with the help of mock tests, detailed explanations, and expert advice from the authors.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Getting Started with Tableau
3
Section 2: Answering Questions with Data
8
Section 3: Advanced Tableau
13
Mock Test A + B (Assessment)

Summary

In this chapter, we covered the three types of LOD expressions available in Tableau (FIXED, INCLUDE, and EXCLUDE) and studied how we can use them to aggregate data at a level that is either more granular (in the case of INCLUDE) or less granular than the dimensions already in the view. We also looked at the order of operations to explain the differences between these calculations and the ones seen previously (such as table calculations). For instance, we now hold the tools to calculate contributions to a total, create cohorts based on first order date, and aggregate customer-level information starting from item-level data.

Here is a quick reminder:

FIXED INCLUDE EXCLUDE
Order of operations Before dimension filters After dimension filters After dimension filters
Purpose Calculate across the dataset along selected dimensions Calculate results using a dimension that...