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

Summary

This chapter gave you all the background you needed to begin your journey through the practice of data analytics. We saw the differences between the three types of analytics (descriptive, predictive, and prescriptive) and recognized the underlying potential value of each. We realized how virtually everyone in a company can benefit from using data analytics and familiarized ourselves with the different roles and skills required across business users, analysts, data scientists, and engineers. We glimpsed at the complexity of the full technology stack required for analytics to work: specifically, we went through the tools that data analytics practitioners should keep handy in their toolbox, spotting the value of the KNIME/Power BI couple. At last, we distinguished between the several paths that data can take to be transformed into actual business value.

I hope that the last few pages convinced you even further of the massive business potential hidden in data. It's now...