KNIME Essentials Practice Exam

KNIME Essentials Practice Exam

About KNIME Essentials Exam

Data cleaning is an essential step in any data analysis and machine learning workflow. Without clean data, insights can be inaccurate, leading to poor decision-making. KNIME is a powerful tool that simplifies data preparation through a drag-and-drop interface, making it easy for non-programmers to handle complex ETL (Extract, Transform, Load) tasks.

Skilled Evaluated

This course will teach you how to use KNIME for data cleaning, preparation, and Natural Language Processing (NLP) without requiring any coding knowledge. You will also learn to work with pre-trained TensorFlow models in KNIME (which involves Python coding for advanced users). By the end of the course, you will be able to handle data inconsistencies, process NLP tasks, and optimize data workflows using only KNIME nodes.

Skills Required

To make the most of this course, you should:

• Have basic knowledge of KNIME, as fundamental concepts are not covered.
• Be familiar with data science concepts, such as data transformation and data cleaning.
• Have some understanding of machine learning workflows (optional but useful).
• Be comfortable working with Excel files and structured data formats.

Coding is not required, but knowledge of Python, R, or Java can be useful for advanced KNIME workflows.

Knowledge Area

This course provides practical hands-on training in:

• Data cleaning and data transformation using KNIME.
• Handling Excel files with multiple structures and loops in KNIME.
• Using similarity search for matching inconsistent addresses.
• Building neural networks and working with pre-trained models in KNIME.
• Implementing Natural Language Processing (NLP) using KNIME nodes.
• Replacing Excel with KNIME for ETL and automation workflows.
• Solving real-world data issues using KNIME workflows.

Who should take This Course?

This course is ideal for:

• Data analysts and data scientists who want to streamline data cleaning and transformation.
• Business professionals and non-programmers looking for an easy-to-use data preparation tool.
• Machine learning practitioners who want to integrate KNIME with TensorFlow models.
• Excel users who want to transition to KNIME for ETL and automation.
• Researchers and students who need NLP and data processing tools without coding.

Prerequisites:

• Basic understanding of KNIME (not an introductory course).
• Familiarity with data cleaning and transformation concepts.
• Basic knowledge of machine learning is helpful for later sections.

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.