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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Data is nothing without its chance to be transformed into value. However sophisticated and accurate our model is, whatever profound insights we manage to unveil, data will make a difference only when it finally drives some action that positively impacts the business. The bad news is that driving actions might be quite complex and often more difficult than building a powerful data capability. The good news is that we have not just one but multiple ways to impact the business with analytics: we call them data-to-value paths. It's important to have them clear in our mind before we start working on data, so that we can maximize the business relevance of what we build right from the start. The three most frequent data-to-value paths you can encounter in business are:

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