You will learn how to properly create insightful data visualization in QlikView that covers multiple analytical techniques. Once we have a possible solution, we present our case in a dashboard and use performance indicators to monitor future actions. In each type of analysis, we employ highlighting, heat maps, and other techniques on top of multiple chart types. We then go on to use rank, trend, multivariate, distribution, correlation, geographical, and what-if analysis as we try to resolve the problems of QDataViz, Inc, a fictitious company used as an example. We start our data discovery project by reviewing the data, people, and tools involved. Within the context of a real-world scenario and accompanying exercises, you will learn a set of analytical techniques and data visualization best practices that you can customize and apply to your own organization. Learning QlikView Data Visualization presents a simple way to organize your QlikView data discovery process. While QlikView's data engine complements our thought processes and gives us the ability to rapidly implement insightful data discovery, we must also learn to use proper analytical and data visualization techniques to enhance our ability to make data more presentable.
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