In this chapter, you will learn how to split columns based on cell values, allowing you to separate combined data into more manageable parts. Next, you will explore the use of conditional statements for data transformation, enabling you to apply specific rules to your dataset. Additionally, you will learn how to use logical operators to formulate logical conditions. Finally, you will discover how to rename one or multiple columns, ensuring your dataset is well-organized and easy to understand.

Conditional statements and logical operators

Assign inflation value

Enhance student data

Split into city and quantity

Rename columns

Assign discount percentage

Delve into Expression- Based Data Transformation, a crucial skill for data manipulation. This lesson covers understanding and utilizing string expressions for various string manipulations. Additionally, you will master mathematical expressions using formulas and operators, enabling them to create custom columns tailored to their needs. The chapter culminates with the ability to build mixed expressions that seamlessly combine mathematical and string expressions, providing a comprehensive toolkit for advanced data transformation.

String and math expressions

Calculate the bill value of Items

Calculate student scores

Convert sales column to numeric

Calculate discount value

Calculate profit

String and Math Expressions

Learn how to append and merge columns from multiple tables, enabling you to consolidate data from various sources. Discover how to add constant value columns and reorder columns to customize your dataset layout. Finally, delve into sorting tables by specifying a list of criteria, ensuring your data is organized and easily accessible for analysis. These skills will enhance your data management proficiency, making complex datasets more manageable and insightful.

Data integration

Find the most expensive food item

Integrate and analyze students data

Append and merge columns

Add  date and reorder columns

Sort the table

Data Integration

This final chapter focuses on enhancing KNIME workflows by adding detailed descriptions and tags to represent their themes. Further, learn how to abstract workflow segments into reusable KNIME components, bundle functionality, and add visual elements for better usability. 

Lastly, learn to share your workflows and components on the KNIME Community Hub, ensuring they are valuable and accessible to the community.

Workflows delen en samenwerken

Kenmerken van componenten

Workflowsdetails toevoegen

Component maken

Proces voor het delen van een component

De component delen

Cursusafsluiting

Share workflows & collaborate

Exercises and Datasets

DataCamp vs. Local Experience

Datatransformatie in KNIME is ontworpen om je essentiële en gemiddelde technieken te leren voor het manipuleren en transformeren van data met KNIME. Je begint met basisvaardigheden voor datatransformatie, zoals kolommen splitsen en werken met voorwaardelijke uitspraken. De cursus bouwt verder op naar complexere taken, zoals expressiegebaseerde transformaties en het consolideren van data. Tot slot leer je KNIME-workflows te verbeteren door herbruikbare componenten te maken en de herbruikbaarheid te optimaliseren. Deze cursus is ideaal voor iedereen die zijn of haar databeheer­vaardigheden met KNIME wil verdiepen.

Introduction to KNIME

Verbeter je KNIME-vaardigheden met onze cursus over datatransformatie en workflowoptimalisatie.

Datatransformatie in KNIME

KNIME-basisprincipes

Kenmerken van componenten

Datatransformatie in KNIME

Praktische interactieve oefening