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KNIME for Data Science and Data Cleaning
Rating: 4.5 out of 5(47 ratings)
380 students

KNIME for Data Science and Data Cleaning

Data Science with KNIME , How to do data cleaning with KNIME, AI Machine learning with KNIME, ETL with KNIME, data prep
Created byDan We
Last updated 3/2025
English
English [Auto],

What you'll learn

  • How to use Tensorflow in KNIME
  • How to do DataScience in KNIME WITH AND WITHOUT CODING
  • How to solve data cleaning and data preparation challenges
  • How to replace Excel and start KNIME for ETL and data cleaning issues
  • Examples of data science machine learning workflows with KNIME
  • Hopefully a higher efficiency in data preparation and data science related work
  • Enhance your basic KNIME skills already acquired ( for example in my KNIME crash course on udemy)

Course content

2 sections • 24 lectures • 2h 51m total length

  • 1 Welcome to Knime Lets get started0:55

    Discover how KNIME accelerates data science and data cleaning by building a no-code natural language processing workflow and exploring transfer learning with Python, enabling faster, more efficient analytics.

  • Before you start0:23
  • Download all resources0:03

    Attached you can download the resources

  • 4 Copying or Moving files with KNIME7:35
  • 5 Reading multiple excel files - potential errors and solutions8:22
  • 6 Reading multiple Excel files - benefits of loops10:26
  • 7 Excel files with different table structure in KNIME8:19

    Demonstrate reading Excel files with different table structures in KNIME by listing files, looping through them, and merging columns to align the schema for a single output data table.

  • 8 Useful Nodes - Column Aggregations13:07
  • 9 Countries - data cleaning challenge8:48
  • 10 Merge Table Challenge in KNIME7:06

    Merge Excel tables in Knime by mapping characters to series using a dynamic chunk loop. Extract table dimensions to set rows per chunk and append columns to build a dataset.

  • 11 A Json file challenge in KNIME13:36
  • 12 Create the Neural Network h5 model file to be used in Knime8:48
  • 13 Mismatching addresses - Introduction to similarity search in KNIME8:12

    Learn to match mismatched and misspelled addresses in KNIME using fuzzy matching and the similarity search, leveraging Levenshtein distance and nearest neighbor results.

  • 14 Tensorflow Neural Network Regression Implementation in Knime10:15

    Load a pre-trained TensorFlow regression model in KNIME, read and scale Excel data, split features from the target, and produce a house-price prediction for comparison with the original value.

  • 15 Transferlearning in Knime using Python scripts11:42
  • 16 Introduction to NLP in KNIME8:59

    Learn how to perform sentiment analysis in KNIME using a Twitter dataset, convert sentiment to string, balance classes with stratified sampling, and convert tweets into documents for natural language processing.

  • 17 NLP in KNIME 2 Data preprocessing and cleaning9:09
  • 18 NLP in KNIME 3 bag of words and document vector8:56
  • 19 NLP in KNIME Choose ML Algorithm and score our model7:30

    Explore NLP in KNIME by building a decision tree workflow, with options like random forest or SVM, then filter the class column, train, and score for accuracy on training data.

  • Congratulations, thanks a lot and one favor to ask0:21
  • You want to learn more? Lets do it together0:55

Requirements

  • for beginners with KNIME my KNIME crash course on udemy is highly recommended to take first
  • No extra costs - KNIME can be downloaded for free
  • Basic knowledge of machine learning is certainly helpful for the later lectures in this course
  • The course complements my other Data science course with KNIME on udemy (I try to keep the overlapp as little as possible to give you the best kind of experience)

Description

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 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"


Please note: For some examples we use Python in combination with KNIME. So if you have no prior Python knowledge the section might be a bit harder for you. But if you follow along it is definitely worth it!


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? 

Who this course is for:

  • (Aspiring) data scientists
  • (Aspiring) data analysts
  • Everyone interested in the data analysis , data analytics and data science area
  • data scientists / analysts who want to work smarter faster and more efficient
  • Excel users who want to learn better ways to prepare data. Excel is nice but KNIME is AWESOME!

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