KNIME – A Guide for Absolute Beginners
Use KNIME for data cleaning with a case study and learn how to perform some advanced data preparation
Updated on Jun, 2025
Language - English
Development ,Data Science,Data Analysis
Lectures -13
Duration -2.5 hours
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Course Description
This course is designed for aspiring data scientists and data analysts who want to work smarter, faster, and more efficiently.
Data cleaning is always a big hassle, especially if we are short on 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 start from the basics and learn KNIME in a (hopefully) fun and easy way by going through a case study together and prepare the data based on the requirements in the case study.
After completing our data prep, we will learn how to visualize data using tools such as Power BI and Tableau.
Finally, we also briefly cover the predictive analytics capabilities of KNIME and see how easy machine learning in KNIME can be.
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 supportive files for this course are available at https://github.com/PacktPublishing/KNIME-A-Guide-for-Absolute-Beginners
Goals
- Learn KNIME using a case study
- Prepare data in advance to visualize it later in tools such as Tableau or Power BI
- Extend your data analytics knowledge
- Perform simple to advanced ETL (Extraction - Transformation - Load)
- Clean and shape your data the way you need it
- Learn how to use machine learning/AI predictive analytics capabilities
Prerequisites
- 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. Note: Tableau Desktop and Microsoft Power BI Desktop are optional.
Curriculum
Check out the detailed breakdown of what’s inside the course
Learn KNIME in a Fun and Easy Way - Through Solving a Case Study
13 Lectures
-
Welcome and Introduction
01:11
01:11
-
Let Us Get Started with KNIME - the Interface
09:30
09:30
-
Let Us Take a Look at Our Case Study Data
02:44
02:44
-
Reading Data into KNIME - the Basics.
16:41
16:41
-
Using Loops for Data Import - How to Be More Efficient in KNIME
12:17
12:17
-
Data Cleaning and Saving Files with KNIME
12:41
12:41
-
Webscraping in KNIME - Extracting Currency Exchange Rates from an API
15:10
15:10
-
Joining and Cleaning Our Data in KNIME
19:52
19:52
-
Final Data Cleaning and Preparation Steps for Visualizing Our Results
21:30
21:30
-
Bonus - Saving the Data as a Hyper Format for Tableau
06:19
06:19
-
Visualization - an Introduction on How to Visualize in Tableau or Power BI
09:22
09:22
-
Predictive Analytics in KNIME - Machine Learning AI to Predict Customer Churn
23:46
23:46
-
KNIME - Log Out and Final Words
00:32
00:32
Instructor Details
Packt Publishing
Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development.
Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge.
We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them.
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