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Data Analytics Made Easy

Data Analytics Made Easy

By : De Mauro
4.7 (12)
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Data Analytics Made Easy

Data Analytics Made Easy

4.7 (12)
By: 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)
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10
And now?
12
Other Books You May Enjoy
13
Index

Chapter 5

  • To learn how to run time-series analytics using KNIME components, check out the following: Tonini, D. Weisinger, C., Widmann, M. "Time Series Analysis with Components", Low Code for Advanced Data Science (2021), http://tiny.cc/KNIMEtimeseries.
  • To get a step-by-step demo of how the k-means algorithm works, check out this simulator by Naftali Harris: https://www.naftaliharris.com/blog/visualizing-k-means-clustering.
  • Read the following to learn about more ways to detect outliers using KNIME: Widmann, M., Heine, M., Four Techniques for Outlier Detection, Low Code for Advanced Data Science (2021), http://tiny.cc/outlierdetection.
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Data Analytics Made Easy
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