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

Data Integration Process

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.

Share workflows & collaborate

Component characteristics

Share & collaborate student's  workflow

Add workflow details

Create component

Sharing the component

Course wrap up

Exercises and Datasets

DataCamp vs. Local Experience

Data Transformation in KNIME is designed to equip you with essential and intermediate techniques for manipulating and transforming data using KNIME. You will start by learning basic data transformation skills, including splitting columns and using conditional statements. The course progresses to more complex tasks, such as expression-based transformations and data consolidation. Finally, you'll learn to enhance KNIME workflows by creating reusable components and optimizing re-usability. This course is perfect for anyone looking to deepen their data management skills using KNIME.

<h2>Basic Data Manipulation Techniques</h2>
<p>Kick off your learning journey by acquiring foundational data transformation skills in KNIME. This course segment covers splitting columns based on cell values, using conditional statements to enforce data integrity, and employing logical operators to create comprehensive data filters. Additionally, you'll learn techniques for renaming columns to maintain a well-organized dataset. These essential skills are crucial for anyone starting out with data transformation in KNIME.</p> <h2>Expression-Based Transformations</h2> <p>Build upon your foundational knowledge with intermediate data manipulation strategies. In this part of the course, you will practice the art of creating and using string and mathematical expressions to perform complex data transformations. Learn how to craft custom columns tailored to specific analytical needs and combine different types of data transformations seamlessly. This skill set will enable you to handle more sophisticated data scenarios effectively.</p> <h2>Enhancing Workflows and Data Consolidation</h2> <p>The final part of the course focuses on optimizing and enhancing KNIME workflows. Learn to append and merge columns from multiple data sources, add constant value columns, and effectively sort tables. Furthermore, this section teaches you how to abstract segments of your workflows into reusable components and enhance your visual elements for better usability. These advanced techniques will allow you to manage and analyze complex datasets with greater efficiency and insight.</p>

Introduction to KNIME

Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.

Data Transformation in KNIME

KNIME Fundamentals

Append columns from both table into a single table

Merge the Discount Price and Price columns into the Price column

Sort the table by the Category column in ascending order

Sort the table by the Price column in descending order

Observe the sorted table to find the most expensive food item

ID	Category
1	Sports
2	Food
3	Sports
4	Food

ID	Discount Price	Price
1	20	40
2	?	40
3	30	10
4	?	100

Find the most expensive food item

Data Transformation in KNIME

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