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


Data analytics is a sexy topic these days. Skills such as machine learning, data visualization, and storytelling are becoming essential in virtually any professional field. The necessity to acquire data fluency is not a thing for data scientists and analysts only: it is becoming a development objective for pretty much everyone, irrespectively of their education, experience, business function, and seniority level. It is a universal need.

For the majority of people interested in using analytics, learning how to code in a programming language is an intimidating barrier to break and—sadly—the first reason for abandoning their intent. For this reason, this book leverages low-code analytical tools. This way, we decouple the objective of learning how to leverage data analytics effectively in our jobs (which is the primary focus of this book) from the requirement of learning how to program (which is, instead, an ancillary nice-to-have for scaling and expanding the role of analytics even further).

This guide offers an accessible journey through the most valuable techniques of data analytics, enabling you to move quickly from theory to practice using low-code environments. Although a large part of the content is application-agnostic and can be leveraged on any software you or your company decide to use, the book's tutorials are based on KNIME and Power BI.

KNIME and Power BI were selected because they make the best travel buddies for your journey through data analytics. KNIME is the "Swiss Army knife" among the analytical platforms according to Gartner. Its graphical interface enables everyone—managers, analysts, and students—to automate data pipelines and employ machine learning algorithms without writing any line of code. One positive aspect to keep in mind is that visual analytics tools like KNIME are not simplistic and "depowered" versions of the "real thing." With KNIME, you can do full-on data science, including sophisticated data crunching and serious AI applications, such as creating deep neural networks. Microsoft Power BI requires no introduction: it is among the most popular data visualization and dashboarding tools. It provides a comprehensive environment to build self-service business intelligence interfaces that are omnipresent in supporting decision-making. Both Power BI and KNIME offer full access to their main functionalities without any capital investment. You can download them for free, enabling you to put into practice what you learn immediately. The combination of KNIME as back-end and Power BI (or Tableau, which is also introduced) as front-end is versatile and robust, empowering organizations to cover the full range of data capabilities, from descriptive to predictive and prescriptive analytics. This book's step-by-step tutorials—based on real business cases and data—will provide you with confidence through practice and make you an independent user of such a powerful software combo. The hands-on journey that waits for you in the following pages has a high ambition: making data analytics a trusted companion for your everyday work.