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

Three types of learning algorithms

The scenarios we have seen in the previous section were not selected at random. They match the standard categorization of ML algorithms that provides for three fundamental types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. When we want to apply the ML way, we need to select one of these three routes: our choice will depend on the nature of the problem we need to solve. Let's now go through each group to understand what they are made of and what types of tasks they fulfill.

Supervised learning

In supervised learning, your objective is to predict something "unknown" by learning from some "known" pieces of information. The easiest way to make sense of the supervised learning approach is to think about how it differs from traditional programming. In Figure 4.2, you will find on the left a very familiar setup. In plain computer programming, we need some input data, a program, and a computer to...