Dear reader, welcome to an intuitive way of data analysis. Using a visual programming language based on dataflows, you can create an easy-to-understand analysis process, while it internally checks signals about some of the common problems. Obviously, any environment that does not help with proper documentation would be destined to fail, but KNIME's success is based not just on its high quality—cross-platform—code, but also on the good description about what it does and how you can use the building blocks.

This book covers the most common tasks that are required during the data preparation and visualization phase of data analysis using KNIME. Because of the size constraints—and to bring the best price/value for those who are already familiar with or not interested in modeling—we have not covered the modeling and machine learning algorithms available for KNIME. If you are already familiar with these algorithms, you will easily get familiar with the options in KNIME, and these are quite obvious to use, so you lose almost nothing. If you have not found time yet to get acquainted with these concepts, we encourage you to first learn for what these procedures are good and when you should use them. There are some good books, courses, and training available—these are the ideal options for learning—but the Wikipedia articles can also give you a basic introduction specific to the algorithm you want to use.