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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Getting Started with KNIME

It's time to get our hands finally dirty with data as we unveil KNIME, the first instrument we find in our data analytics toolkit. This chapter will introduce you to the foundational features of any low-code analytics platform and will allow you to get started with the universal need you face at the beginning of every analytics project: loading and cleaning data.

Let's have a look at the questions this chapter aims to answer:

  • What is KNIME and where can I get it?
  • What are nodes and how do they work?
  • What does a data workflow look like?
  • How can I load some data in KNIME and clean it up?

This is going to be a rather hands-on initiation to the everyday practice of data analytics. Since we will spend some time with KNIME, it's worth first getting some basic background on it.

KNIME (/na�m/) is pronounced like the word knife but with an m at the end instead of an f.