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QlikView: Advanced Data Visualization

You're reading from   QlikView: Advanced Data Visualization Discover deeper insights with Qlikview by building your own rich analytical applications from scratch

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789955996
Length 786 pages
Edition 1st Edition
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Authors (4):
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Barry Harmsen Barry Harmsen
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Barry Harmsen
Miguel  Angel Garcia Miguel Angel Garcia
Author Profile Icon Miguel Angel Garcia
Miguel Angel Garcia
Stephen Redmond Stephen Redmond
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Stephen Redmond
Karl Pover Karl Pover
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Karl Pover
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Table of Contents (25) Chapters Close

QlikView: Advanced Data Visualization
Contributors
Preface
1. Performance Tuning and Scalability 2. QlikView Data Modeling FREE CHAPTER 3. Best Practices for Loading Data 4. Advanced Expressions 5. Advanced Scripting 6. What's New in QlikView 12? 7. Styling Up 8. Building Dashboards 9. Advanced Data Transformation 10. Security 11. Data Visualization Strategy 12. Sales Perspective 13. Financial Perspective 14. Marketing Perspective 15. Working Capital Perspective 16. Operations Perspective 17. Human Resources 18. Fact Sheets 19. Balanced Scorecard 20. Troubleshooting Analysis 21. Mastering Qlik Sense Data Visualization Index

Data exploration, visualization, and discovery


Data visualization is not something that is done at the end of a long, costly Business Intelligence (BI) project. It is not the cute dashboard that we create to justify the investment in a new data warehouse and several Online Analytical Processing (OLAP) cubes. Data visualization is an integral part of a data exploration process that begins on the first day that we start extracting raw data.

The importance and effectiveness of using data visualization when we are exploring data is highlighted using Anscombe's quartet. Each of the following scatterplots analyzes the correlation between two variables. Correlation can also be explained numerically by means of R-squared. If we were to summarize the correlations of each of the following scatterplots using R-squared, we would discover that the number is be the same for each scatterplot, .816. It is only by visualizing the data in a two-dimensional space do we notice how different each correlation...

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