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Mastering Tableau

Mastering Tableau

By : Jen Stirrup, Baldwin
4.4 (10)
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Mastering Tableau

Mastering Tableau

4.4 (10)
By: Jen Stirrup, Baldwin

Overview of this book

Tableau has emerged as one of the most popular Business Intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. This book will empower you to become a master in Tableau by exploiting the many new features introduced in Tableau 10.0. You will embark on this exciting journey by getting to know the valuable methods of utilizing advanced calculations to solve complex problems. These techniques include creative use of different types of calculations such as row-level, aggregate-level, and more. You will discover how almost any data visualization challenge can be met in Tableau by getting a proper understanding of the tool’s inner workings and creatively exploring possibilities. You’ll be armed with an arsenal of advanced chart types and techniques to enable you to efficiently and engagingly present information to a variety of audiences through the use of clear, efficient, and engaging dashboards. Explanations and examples of efficient and inefficient visualization techniques, well-designed and poorly designed dashboards, and compromise options when Tableau consumers will not embrace data visualization will build on your understanding of Tableau and how to use it efficiently. By the end of the book, you will be equipped with all the information you need to create effective dashboards and data visualization solutions using Tableau.
Table of Contents (14 chapters)
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Data-mining and knowledge-discovery process models

Data modeling, data preparation, database design, data architecture; how do these and other, similar terms fit together? This is no easy question to answer! Terms may be used interchangeably in some contexts and be quite distinct in others. Also, understanding the interconnectivity of any technical jargon can be challenging. In the data world, data mining and knowledge-discovery process models attempt to consistently define terms and contextually position and define the various data sub-disciplines. Since the early 1990s, various models have been proposed. The following list is adapted from A Survey of Knowledge Discovery and Data Mining Process Models by Lukasz A. Kurgan and Petr Musilek, published in The Knowledge Engineering Review Volume 21 Issue 1, March 2006.

Survey of the process models

Fayyad et al.

KDD

CRISP-DM

Cios et al

SEMMA

Developing and Understanding of the Application Domain

Selection

Business Understanding...

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