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 has equipped you with a set of practical tools and guidelines for designing professional-looking and effective visuals to use in your daily work. We started our journey by framing the overall objective of visualizing data: transferring data-based messages to others by leveraging human visual perception. We have seen how we need to impersonate the role of a designer, who must make choices about what and how to visualize. After selecting the specific message type to give (which can be focused on evolution over time, making sense of absolute and relative size, or relation across quantities), we learned how to pick the right chart type using the chart selection matrix. We discovered pros and cons and typical pitfalls behind the implementation of line charts, bar charts, treemaps, and scatterplots, which collectively account for the vast majority of charts we need. Lastly, we went through a set of guidelines to adopt when ensuring the quality of any data visual. We...