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Data science and Data preparation with KNIME

Data science and Data preparation with KNIME

Discover the four basic steps in data preparation for machine learning algorithms and their significance with our in-depth guide from KNIME.

Category : Development, Data Science, KNIME

Instructor : Dan We

Redeem On Udemy

What you'll learn

  • New job opportunities might open up for you
  • You might be able to increase your productivity and save time in your data preparation tasks
  • Hopefully a higher efficiency in data preparation and data science related work
  • What kind of loops are available and how to use them in KNIME
  • Examples of data science machine learning workflows with KNIME
  • Enhance your basic KNIME skills already acquired ( for example in my KNIME crash course on udemy)
  • How to use Python in KNIME (Java and R could also be used but will not be the focus here)
  • How to do DataScience in KNIME WITH AND WITHOUT CODING


Data science and Data cleaning and Data preparation with KNIME

Hello everyone hope you are doing fine.

Let’s face it. Data preparation ,data cleaning, data preprocessing (whatever you want to call it) is most often the most tedious and time consuming work in the data science / data analysis area.

So many people ask: How can we speed up the process and be more efficient?

Well one option could be to use tools which allow us to speed up the process (and sometimes reduce the amount of code we need to write).


A great tool which comes to our rescue. KNIME allows us to do data preparation / data cleaning in a very appealing drag and drop interface. (No coding experience is required yet it still allows us if we want to use languages like R, Python or Java. So, we can code if we want but don’t have to!). The flexibility of KNIME makes that happen. WITH KNIME we can also do Data Science, so machine learning and AI with or without coding.

And the best: The Desktop version is free!

So, is it worth it to dive deeper into KNIME? ABSOLUTELY!

This course is the second KNIME class and expands the knowledge you have acquired in the first class "KNIME - a crash course for beginners" which is  also available on udemy.

We do not cover the basics (e.g. the interface, basic data import and filter nodes,...) here. If you need  to refresh your knowlege or you have not had the chance to learn the basics I would recommend to check the prior class first (which covers all the basics in a great case study!)

In this class we dive into

  • efficient ways to import multiple files into KNIME
  • loops
  • webscraping
  • scripting (using Python code in KNIME)
  • hyperparameter optimization
  • feature selection
  • basic machine learning workflows and helpful nodes for this in KNIME

If that does not sound like fun, then what? So, if that is interesting to you then let’s get started!

Are you ready? 

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