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Data science and Data preparation with KNIME

Name: Data science and Data preparation with KNIME
Rating: 4.6 (367 reviews)

KNIME - a powerful tool for data science and machine learning Data science with higher efficiency. KNIME data cleaning

Highest Rated

Created byDan We

Last updated 7/2025

English

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
You increase your productivity

Course content

7 sections • 70 lectures • 11h 2m total length

Course introduction1:03
Important Please read before you get started Courseupdate Info0:41

Switch between old and new KNIME interface1:46
Transition in KNIME0:22
Download the course files here0:02
4 Reading multiple CSV files in bulk into KNIME12:35
5 Reading multiple CSV files in bulk into KNIME with Loops8:33
6 Reading multiple Excel files efficiently into KNIME19:50
7 A great helper node for timeseries analysis in KNIME7:31
Learn to use KNIME's lag node for time series analysis, importing stock price history from csv, and creating rolling lags with copy and multiple lag options.
8 Examples of Loops in KNIME8:30
9 Exploring more Loops in KNIME Analytics Platform7:00
10 Loops to split data into multiple outputs12:33
11 Advanced Loops in KNIME Recursion example15:52
12 How to do webscraping in KNIME14:23
Url for the next lecture0:01
13 Webscraping financial data from an API with KNIME12:30
14 Scripting How to use Python in KNIME11:47
15 Scripting in KNIME python as part of our workflow8:41
16 Hyperparameter Tuning in KNIME Data preparation14:14
Learn hyperparameter tuning in KNIME by building a Titanic data preparation workflow, applying one-hot encoding, column filtering, and stratified data splitting while handling missing values and preventing data leakage.
17 Hyperparameteroptimization in KNIME Training and Testing20:02
18 Feature Selection in KNIME How to get started15:24

1 One message at the beginning0:56
Download all resources here0:02
Reading multiple csv files in bulk into KNIME update22:05
Bulk read 12 CSV files into KNIME using the new CSV reader files and folders mode, or loop with list files and folders to concatenate into a single table.
Reading multiple Excel files in bulk into KNIME update14:48
Learn to read multiple Excel files with multiple sheets into KNIME using loops, list files and folders, and sheet name columns.
3 A great helper Node for time series analysis in KNIME6:30
Important for the next chapter with loops0:18
4 Examples on how to use loops in KNIME5:51
5 More on loops in KNIME - several ways to get the same result5:43
6 Loops How to split data into multiple output files update15:40
Important KNIME Update (depending on which version you use for the next video)1:09
7 Loops Recursion in KNIME13:42
Master recursive loops in KNIME to replace abbreviations with real names using a lookup table, column and row splitters, and string manipulation within data preparation workflows.
8 Webscraping with KNIME14:41
Webscraping with KNIME update - financial data28:32
Scripting - How to use Python in KNIME (R and Java as well)12:53
Learn to script in KNIME with python, r, and java nodes, using python source and snippet nodes, configure environments in preferences, and run code within a no-code workflow.
Python in KNIME - further examples - update9:35
12 Hyperparameter optimization in KNIME - data preparation10:13
13 Hyperparameter optimization for Machine Learning Models using loops in KNIME16:22
Optimize a KNIME random forest by tuning levels and trees with a parameter optimization loop, apply the best parameters to test data, and evaluate accuracy with the scorer.
14 Feature Selection in KNIME15:33
15 Let's rehearse the machine learning prediction process9:48
Move from feature selection to evaluation by exporting a labeled dataset, normalizing data, training a logistic regression model, and evaluating accuracy and ROC on train and test sets.

Important note0:29
Attached you can find the course files to follow along.0:04
Missing Project days data17:38
Splitting long text challenge13:59
Approximate Match mapping challenge11:03
Customer Weekday Weekend challenge16:13
Bonus How to check for missing flow variables9:08
Check available Info challenge12:41
Approximate Date Lookup challenge12:01
Textprocessing string removal challenge11:25
keyword tagging challenge12:22
misspelling_challenge12:25
How to deal with date conversion issues12:08
Bonus Molecule challenge21:54

Important Note0:29
Attached you can find and download the resources to follow along0:04
Copy or Moving files with KNIME update7:35
Discover how to move and copy files in knime using the new transfer files node (knime 9, 4.3+), filtering to copy only sales excel files into a new folder.
Countries - data cleaning challenge8:48
Clean a country dataset by unpivoting, using the cell splitter to remove parentheses, and re-pivoting to restore country, population, GDP, and inflation columns with KNIME nodes.
Merge Table Challenge in KNIME7:06
A Json file challenge in KNIME13:36
Mismatching addresses - Introduction to similarity search in KNIME8:12
Introduction to NLP in KNIME8:59
Learn sentiment analysis in KNIME using a Twitter dataset. Convert sentiment to string for stratified sampling and transform tweets into documents for NLP.
NLP in KNIME 2 Data preprocessing and cleaning9:09
NLP in Knime 3 bag of words and document vector8:56
NLP in KNIME Choose ML Algorithm and score our model7:30

Requirements

No extra costs - KNIME can be downloaded for free
You should have worked with KNIME before
The program itself and the basics are covered in "KNIME - a crash course for beginners" which is also available on udemy
Basic knowledge of machine learning is certainly helpful.
Coding is not required but we learn how you can use python and use your python code in KNIME

Description

Master Data Science, Cleaning, and Preparation with KNIME

Welcome to the world of efficient data preparation, where tedious tasks become a breeze. In the realm of data science and analysis, one thing is certain: data cleaning, preprocessing, or whatever you choose to call it, can be a time-consuming ordeal.

Efficiency at Your Fingertips

How can we expedite this process and work smarter, not harder? The answer lies in tools that not only accelerate the process but also reduce the need for excessive coding.

Meet KNIME - Your Data Ally

KNIME is the hero of the day. It offers a user-friendly, drag-and-drop interface that simplifies data preparation and cleaning. No coding experience required, though you have the option to unleash the power of R, Python, or Java if you wish. KNIME's flexibility knows no bounds. You can even venture into Data Science, including machine learning and AI, with or without coding.

Did We Mention It's FREE?

You read that correctly. KNIME Desktop won't cost you a dime. It's a robust tool that won't dent your budget.

Elevate Your KNIME Skills

This course follows our introductory KNIME class, "KNIME - A Crash Course for Beginners," also available on Udemy. In this second installment, we delve deeper into advanced topics.

What's Inside the Course

We skip the basics here (like the interface, basic data import, and filter nodes). If you're new to KNIME or need a refresher, consider exploring our first class, where we cover the fundamentals in a captivating case study.

In this class, we explore:

Efficient methods to import multiple files into KNIME
The power of loops
Web scraping techniques
Scripting using Python within KNIME
Hyperparameter optimization
Feature selection
Basic machine learning workflows and essential KNIME nodes

If efficiency and diving deep into data science and preparation sound appealing to you, then let's embark on this journey together!

Are you ready to elevate your data skills?

Who this course is for:

(Aspiring) data scientists
(Aspiring) data analysts
data scientists / analysts who want to work smarter faster and more efficient

Data science and Data preparation with KNIME

What you'll learn

Explore related topics

Course content

Hello and welcome to KNIME :)2 lectures • 2min