KNIME for Data Science and Data Cleaning
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 completely free!
So, is it worth it to dive deeper into KNIME? ABSOLUTELY!
This course is the third KNIME class and expands the knowledge you have acquired in the other classes
“KNIME – a crash course for beginners”
“Data science and Data preparation with KNIME”
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 highly recommend to check at least the crash course first (which covers all the basics in a great and funny case study!)
In this class we extend our skills by
learning to use additional helpful KNIME nodes not covered in the other two classes
solve data cleaning challenges together
use pretrained models in tensorflow in KNIME (involves Python coding)
learn the fundametals for NLP tasks (Natural language processing) in KNIME using only KNIME nodes (without any additional coding)
If that does not sound like fun, then what? So, if that is interesting to you then let’s get started!
Are you ready?