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.

Bagikan workflow & berkolaborasi

Karakteristik komponen

Bagikan & kolaborasikan workflow mahasiswa

Tambahkan detail alur kerja

Buat komponen

Proses Membagikan Komponen

Membagikan komponen

Penutup kursus

Share workflows & collaborate

Exercises and Datasets

DataCamp vs. Local Experience

Transformasi Data di KNIME dirancang untuk membekali Anda dengan teknik-teknik esensial dan menengah dalam memanipulasi serta mentransformasi data menggunakan KNIME. Anda akan mulai dengan mempelajari keterampilan dasar transformasi data, termasuk membagi kolom dan menggunakan pernyataan kondisional. Kursus ini kemudian berlanjut ke tugas yang lebih kompleks, seperti transformasi berbasis ekspresi dan konsolidasi data. Terakhir, Anda akan mempelajari cara meningkatkan alur kerja KNIME dengan membuat komponen yang dapat digunakan kembali dan mengoptimalkan kemampuan penggunaannya. Kursus ini ideal bagi siapa pun yang ingin memperdalam keterampilan pengelolaan data menggunakan KNIME.

Introduction to KNIME

Tingkatkan keterampilan KNIME Anda dengan kursus transformasi data, operasi kolom, dan optimasi alur kerja.

Transformasi Data di KNIME

KNIME Fundamentals

Klik panel Space Explorer di KNIME Analytics Platform dan pilih KNIME Community Hub.

Pada tab masuk KNIME Community Hub, masukkan kredensial Anda atau buat akun lalu masuk.

Klik komponen yang ingin Anda bagikan dan pilih opsi berbagi.

Pilih ruang di KNIME Community Hub tempat Anda ingin membagikan komponen tersebut lalu klik bagikan.

Membagikan komponen

Transformasi Data di KNIME

Latihan interaktif praktis