KNIME Essentials Online Course

KNIME Essentials Online Course

This course is designed to help learners clean and prepare data efficiently using KNIME, without the need for programming knowledge. You will discover how KNIME simplifies data transformation and automation through its drag-and-drop interface, making it an ideal tool for data scientists, analysts, and business professionals.

Skilled Evaluated

The course covers essential data preparation tasks, including handling multiple Excel files, merging datasets, and dealing with inconsistent data. You will also learn how to apply Natural Language Processing (NLP) techniques in KNIME using only its built-in nodes, making text analysis and sentiment classification simple and efficient. For those interested in machine learning, the course introduces TensorFlow models within KNIME, including neural network regression and transfer learning. By the end of this course, you will have the skills to use KNIME for data science, data preparation, and NLP workflows—without any coding experience required.

Key Benefits

• No programming knowledge required – use KNIME’s drag-and-drop interface for data cleaning.
• Learn to automate data preparation tasks without relying on Excel.
• Master NLP workflows using only KNIME nodes—no external coding needed.
• Solve real-world data issues, including working with JSON files and mismatching addresses.
• Use TensorFlow models inside KNIME to perform machine learning tasks.
• Improve productivity by handling multiple datasets at once with loops.
• Develop strong ETL (Extract, Transform, Load) skills for data science.

Target Audience

This course is perfect for:

• Data analysts and scientists looking to simplify data preparation workflows.
• Business professionals who want to clean and merge datasets without coding.
• Machine learning enthusiasts interested in using TensorFlow inside KNIME.
• Researchers and students looking for an easy way to perform NLP tasks.
• Excel users who want a more powerful tool for data automation.

Prerequisites:

• Basic familiarity with KNIME (not for absolute beginners).
• Understanding of data preparation and ETL workflows.
• Basic knowledge of machine learning is helpful but not required.

Learning Objectives

• Learn how to clean, transform, and merge data efficiently using KNIME.
• Automate reading, processing, and merging multiple Excel files in KNIME.
• Handle inconsistent and mismatched data using KNIME’s data transformation nodes.
• Perform Natural Language Processing (NLP) tasks using only KNIME nodes.
• Work with pre-trained TensorFlow models inside KNIME for machine learning applications.
• Use loops and workflow automation to process large datasets efficiently.
• Master data aggregation, column transformations, and JSON file handling.
• Improve ETL and data preparation skills to replace manual Excel work.

Course Content Overview

The KNIME Essentials Exam covers the following topics - 

Domain 1. Introduction to KNIME

• Welcome to KNIME and its capabilities for data science.
• How to copy and move files within KNIME workflows.

Domain 2. Handling Excel Files in KNIME

• Reading multiple Excel files and dealing with potential errors.
• Using loops to efficiently process multiple Excel files.
• Handling Excel files with different table structures in KNIME.

Domain 3. Using Helpful KNIME Nodes for Data Cleaning

• Exploring column aggregation nodes for efficient data transformation.

Domain 4. Data Cleaning Challenges with Real-World Datasets

• Cleaning country-specific data for analysis.
• Merging multiple tables into a single dataset.
• Working with JSON files and structuring data for analysis.

Domain 5. Working with Address Data – Using Similarity Search

• Handling mismatching addresses by using similarity search techniques in KNIME.

Domain 6. Neural Networks in KNIME

• Creating an H5 model file to be used within KNIME workflows.
• Implementing TensorFlow-based regression models in KNIME.
• Performing transfer learning using Python scripts within KNIME.

Domain 7. Introduction to NLP (Natural Language Processing) in KNIME

• Understanding NLP workflows in KNIME (without coding).
• Preprocessing and cleaning text data for NLP tasks.
• Using Bag of Words and Document Vectors for text representation.
• Selecting machine learning models and evaluating NLP performance.

Domain 8. Completion and Next Steps

• Final thoughts and next steps after completing the course.

Domain 9. (Bonus) Older KNIME Version (Before 4.3)

• Understanding how to copy and move files in KNIME workflows.
• Using loops for reading multiple Excel files efficiently.
• Handling Excel files with different table structures in KNIME.