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?
Other Books You May Enjoy

Tutorial: Sales Dashboard

This tutorial will take us back to a familiar business, which we have by now grown fond of: the UK-based online retailer selling all-occasions gifts. Automating the weekly Excel reports using KNIME (something we did back in Chapter 3, Transforming Data) has granted us quite a reputation that is now pulling us back into the spotlight. As the business keeps growing, the hunger to make descriptive analytics available to a greater number of employees expands. Updating some Excel reports that answer a static list of given questions is not sufficient anymore. We would like to offer the opportunity to deep-dive into quarter-by-quarter and country- and product-level details so that our colleagues can "explore" data by themselves and find interesting insights.

With the help of the financial analyst (who is still full of gratitude for us) and after interviewing a few managers about their most recurrent needs, we are ready to synthesize the requirements...