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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This chapter introduced us to KNIME, the new addition to our data analytics toolbox. We learned what KNIME is in a nutshell and got started with its user interface, which enables us to combine simple computation units (nodes) into more complex analytical routines (workflows) with speed and agility, without having to write extensive code. We got started with the ever-present preliminary steps of any data work: loading and cleaning up data to make it usable for doing analytics. We got acquainted with twelve basic nodes in KNIME that empowered us to create repeatable routines, which include: opening files in different formats, sorting and filtering data following some logic, manipulating strings, and managing missing values and duplicate rows. Not bad for being just on the second chapter!

Having the basics clearly explained, we can now dare to go further with KNIME. In the next chapter, Chapter 3, Transforming Data, we will learn how to work on multiple data tables and to build more complex data workflows for analyzing real-world data feeds.