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)
10
And now?
12
Other Books You May Enjoy
13
Index

The data analytics toolbox

Out of all the technologies related to data analytics, this book is going to focus on the application layer. This is where the "magic" happens: analytics applications can transform data into actual business value and in the next chapters, you will learn how to do this.

There are many data analytics applications out there available for use. Each of them has its strengths and peculiarities. Although some can be very versatile, no single application will satisfy the full range of analytical needs we could encounter on our way. Hence, we should pick a selection of tools that will jointly cover an acceptable range of needs: they form our data analytics toolbox. By learning how to use and how to effectively combine the few tools we have put in the toolbox, we can become autonomous data analytics practitioners. Like a plumber would have his or her preferences on the instruments to use, you will also have your own predilections and can customize your...