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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Who is involved in data analytics?

The short answer to the heading of this section is also the most obvious: everyone has a role to play in data analytics – nobody is excluded! In fact, all knowledge workers will undoubtedly have to deal with data as part of their job: they will interact with analytics in one way or another, either solely as a passive user or all the way to the other end of engagement, as the main creator and owner of data capabilities. We can recognize four families of roles with regard to data analytics in companies: business users, business analysts, data scientists, and data engineers. Let's deep dive to understand what each role means and what competencies it requires:


Vertical Business Knowledge

Data Analysis and Storytelling

Machine Learning Algorithms