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

What is Data Analytics?

Before we start our journey across the vast and exciting land of data analytics, it is wise to get equipped with an up-to-date map that can show us the way. In this chapter, you will cover all those fundamental concepts you need to visualize, with clarity, the role of data analytics in companies. This will let you spot opportunities for leveraging data and decide how to distill business value out of it. You also want to feel confident about the naming conventions adopted in this domain to avoid any confusion and speak decisively with those around you. Given the hectic development of data analytics these days, it is a wise choice to build a robust foundation of the key concepts before getting our hands dirty with tables and algorithms.

Specifically, within this chapter, you will find answers to the following questions:

  • What types of analytics can we find in companies?
  • Who should be designing, maintaining, and using them?
  • What technology is...