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

Three types of data analytics

The term data analytics normally denotes those processes and techniques used to extract some sort of value from data. Sometimes, the same term indicates the actual tools used to make this transformation happen. In any case, data analytics represents how we can transform crude data into something more actionable and valuable. We can recognize three different types of data analytics, each one carrying its own set of peculiarities and possible applications: descriptive, predictive, and prescriptive analytics.

Descriptive analytics

Descriptive analytics is the unmissable "bread and butter" of any analytical effort. These methodologies focus on describing past data to make it digestible and useable as required by the business need. They answer the generic question "what happened?" by leveraging summary statistics (like average, median, and variance) and simple transformations and aggregations (like indices, counts, and sums), ultimately...