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?
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Chapter 4

  • For a general introduction on topic modeling, see In particular, LDA is implemented in KNIME within the Topic Extractor (Parallel LDA) node, which is part of the KNIME Textprocessing extension.
  • The following paper reveals the challenges related to the implementation of reinforcement learning in real-world problems: Dulac-Arnold, G., Mankowitz, D., & Hester, T. "Challenges of real-world reinforcement learning." arXiv preprint arXiv:1904.12901 (2019),
  • One of the most promising types of machine learning algorithms is neural networks. They make a machine learning subdiscipline by itself, called Deep Learning. To get started with deep learning in KNIME, I recommend reading Melcher, K., Silipo, R., Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform. Packt Publishing Ltd, 2020...