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Mastering Tableau 2021

Mastering Tableau 2021 - Third Edition

By : Marleen Meier, David Baldwin
4.2 (29)
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Mastering Tableau 2021

Mastering Tableau 2021

4.2 (29)
By: Marleen Meier, David Baldwin

Overview of this book

Tableau is one of the leading business intelligence (BI) tools that can help you solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you’ll be able to perform data preparation and handling easily. You’ll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Next, you’ll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You’ll also learn about improving dashboard performance, connecting to Tableau Server and understanding data visualization with examples. Finally, you'll cover advanced use cases such as self-service analysis, time series analysis, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within it. By the end of this Tableau book, you’ll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain.
Table of Contents (18 chapters)
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Index

Data mining and knowledge discovery process models

Data modeling, data preparation, database design, data architecture—the question that arises is, 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.

Survey of the process models

In the following table, we can see a comparison of blueprints for conducting a data mining project with three data processing models, all of which are used to discover patterns and relationships in data in order to help make better business decisions.

The following list is adapted from A Survey of Knowledge Discovery and Data Mining Process Models by Lukasz A. Kurgan and Petr Musilek, and published in The Knowledge Engineering Review, Volume 21, Issue 1, March 2006.

Later on, we will see how Tableau comes into play and makes this process easier and faster for us.

KDD

CRISP-DM

SEMMA

Phase I

Selection

Business understanding

Sample

Phase II

Pre-processing

Data understanding

Explore

Phase III

Transformation

Data preparation

Modify

Phase IV

Data mining

Modeling

Model

Phase V

Interpretation/ evaluation

Evaluation

Assess

Phase VI

Consolidate knowledge

Deployment

-

Since CRISP-DM is used by four to five times the number of people as the closest competing model (SEMMA), it is the model we will consider in this chapter. For more information, see http://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html.

The important takeaway is that each of these models grapples with the same problems, particularly concerning the understanding, preparing, modeling, and interpreting of data.

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