Data science and Data preparation with KNIME

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Data science and Data preparation with KNIME

KNIME - A powerful tool for data science and machine learning

updated on icon Updated on Sep, 2023

language icon Language - English

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architecture icon Development,Data Science,KNIME


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Course Description

Data preparation, data cleaning, data preprocessing (whatever you want to call it) is quite often the most tedious and time-consuming work in the data science/data analysis area. Especially if we are short of time and want to deliver crucial data analysis insights to our audience.

KNIME makes the data prep process efficient and easy. With KNIME, you can use the easy-to-use drag-and-drop interface, if you are not an experienced coder. But if you know how to work with languages such as R, Python, or Java, you can use them as well. This makes KNIME a truly flexible and versatile tool.

In this course, we will learn the efficient ways to import multiple files into KNIME, loops, web scraping, scripting (using Python code in KNIME), hyperparameter optimization, and feature selection. Also, learn basic machine learning workflows and helpful nodes for this in KNIME.

By the end of this course, you will be able to use KNIME for data cleaning and data preparation without any code.

All the resources and support files for this course are available at https://github.com/PacktPublishing/Data-science-and-Data-preparation-with-KNIME

Audience :

This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently. This course is also for anyone who wants to learn how to effectively clean data or encounter various data issues (for example, format) in the past and is looking for a solid solution, and who is familiar with KNIME as no basics are covered in this course.


What will you learn in this course:

  • Enhance your basic KNIME skills already acquired
  • Increase your productivity and save time in your data preparation tasks
  • Discover what kind of loops are available and how to use them
  • Learn how to use Python in KNIME
  • Learn how to do data science in KNIME with and without coding
  • Learn basic machine learning workflows and helpful nodes


What are the prerequisites for this course?

  • Basic knowledge of machine learning is certainly helpful for the later lectures in this course. 
  • Note: Tableau Desktop and Microsoft Power BI Desktop are optional.
Data science and Data preparation with KNIME


Check out the detailed breakdown of what’s inside the course

Data Science and Data Preparation with KNIME
17 Lectures
  • play icon Course Introduction 01:04 01:04
  • play icon Reading Multiple CSV Files in Bulk into KNIME Update 22:07 22:07
  • play icon Reading Multiple Excel Files in Bulk into KNIME Update 14:49 14:49
  • play icon A Great Helper Node for Time Series Analysis in KNIME 06:31 06:31
  • play icon Examples of How to Use Loops in KNIME 05:53 05:53
  • play icon More on Loops in KNIME - Several Ways to Get the Same Result 05:42 05:42
  • play icon Loops - How to Split Data into Multiple Output Files 12:42 12:42
  • play icon Loops Recursion in KNIME 13:41 13:41
  • play icon Webscraping with KNIME 14:41 14:41
  • play icon Webscraping with KNIME - Financial Data 16:25 16:25
  • play icon Scripting - How to Use Python in KNIME 11:08 11:08
  • play icon Python in KNIME - Further Examples 09:36 09:36
  • play icon Hyperparameter Optimization in KNIME - Data Preparation 10:12 10:12
  • play icon Hyperparameter Optimization for Machine Learning Models Using Loops in KNIME 16:23 16:23
  • play icon Feature Selection in KNIME 15:34 15:34
  • play icon Machine Learning Prediction Process 09:50 09:50
  • play icon KNIME Logout 00:24 00:24
Older Videos KNIME Version Before 4.3
4 Lectures

Instructor Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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