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

Applying Machine Learning at Work

You've heard a lot about creating business value with intelligent algorithms: it's finally time to roll up our sleeves and make it happen. In this chapter, we are going to experience what it means to apply machine learning to tangible cases by going through a few step-by-step tutorials. Our companion KNIME is back on stage: we will learn how to build workflows for implementing machine learning models using real-world data. We are going to meet a few specific algorithms and learn the intuitive mechanisms behind how they operate. We'll glimpse into their underlying mathematical models, focusing on the basics to comprehend their results and leverage them in our work.

This practical chapter will answer several questions, including:

  • How do I make predictions using supervised machine learning algorithms in KNIME?
  • How can I check whether a model is performing well?
  • How do we avoid the risk of overfitting?
  • What techniques...