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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I hope this final chapter got you excited about all the directions you can take to further expand your data analytics toolbox. We took our first steps in Tableau and realized how similar it is, in its fundamental features, to Power BI. We have also gone through a friendly introduction to Python, the ubiquitous programming language in data science. As we integrated Python in KNIME, we have seen how to take the best from both the visual and coding programming worlds. As we did so, we took the opportunity to learn how to expand KNIME further by using its vast extensions base and leveraging the public KNIME Hub environment. Lastly, we got a quick tour through the attractive land of AutoML, being exposed to its promising ability to simplify the process of building high-performing machine learning models considerably.

In this chapter, we extended our toolbox by exploring new tools and approaches to run better data analytics in our everyday work. My advice is to make this a habit...