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).
Meet KNIME
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?
