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)
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Evaluating performance

Measuring how good an algorithm is at doing its job is not always an easy task. Take the case of unsupervised learning: we expect a good unsupervised algorithm to unveil the most interesting and useful structures from data. The assessment on what makes them interesting or useful, however, will depend on your specific end goal and often requires some human judgment as well. In reinforcement learning, a good algorithm will be able to come back with a sizeable total reward, unlocking the opportunity to keep maximizing the return of our continuous interaction with the environment. Also in this case, the concept of reward will depend on a specific definition of value, determined by the case we are solving.

If we stay, instead, in the area of supervised learning, the performance evaluation is more straightforward: since our objective is to predict something (numbers or categories), we can assess the performance by measuring the differences between predicted and...