Book Image

Data Analytics Made Easy

By : Andrea De Mauro
4 (1)
Book Image

Data Analytics Made Easy

4 (1)
By: Andrea De Mauro

Overview of this book

Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own. Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Table of Contents (14 chapters)
And now?
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Combining tables

Data models show us how data points within separate tables are logically connected with each other. In the practice of data analytics, we often need to combine data together by leveraging the logic relationships which the data model describes. The most common operation for combining two tables into a third one is called Join. By combining two tables together, we cross-enrich them as we merge all the information we have on a specific event or entity. The join operation will take the two tables and match the rows that have the same values in the columns we specify (Matching Columns). Let's imagine we have the following two tables, which refer to sales transactions and to the product master data:





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