In Part 4 we will learn how to read data from an external file and apply to them some advanced transformation and analysis capabilities provided by KNIME. This tutorial picks up where Part 3 left. You may also want to read Part 1 and Part 2.
Part 3 of KNIME for beginners picks up directly from where Part 2 ended. If you did not cover Part 2 yet, it is recommended that you go back and do it now.
In this Part 2 you are going to create your first KNIME workflow from scratch. If you haven’t read Part 1 yet, you may consider stopping here and going through it first.
KNIME is great for all data manipulation and analysis tasks, but so far it hasn’t shined in the visualization compartment. The native chart nodes produce an output which, despite being interactive and supporting highlighting, is everything but visually appealing. In full honesty it has a bit of 1990’s look and feel.
This series of tutorials – KNIME for beginners – introduces KNIME version 3 from the ground up through some real-life examples and applications. It is recommended that you follow along and try out each step by yourself.
KNIME, pronounced “nime” like in “lime”, is an open source end-to-end data analytics tool which can be used to solve a wide range of problems in science and in business.
Thanks to its intuitive visual way of working, KNIME can be quickly mastered also by non-technical users. KNIME has a broad user community which provides support and develops additional components to extend its capabilities and range of applications.
When more flexibility is required, KNIME natively integrates with a wide range of programming languages such as Java, R, Python and many more.