Lesson 1. KNIME Concepts and Installation Methods

1. What is KNIME 010?
2. Installing the KNIME Analytics Platform (Windows) 011
2.1 Initial Launch and Environment Setup 020
3. Installing the KNIME Extension 022
4. Installing the KNIME Extension (Offline) 025

Lesson 2. KNIME Analytics Platform

1. KNIME Analytics Platform Home Screen Configuration 030
1.1 Creating a Workflow 031
1.2 Checking the Existing Workflow (Local Space) 033
2. KNIME Analytics Platform Screen Configuration 034
2.1 Go to the home screen 036
2.2 Top right menu (Menu/Help) 037
2.3 Workflow Toolbar 039
2.4 Information 039
2.5 Nodes 041
2.6 Space Explorer 044
2.7 AI Assistant 045
2.8 Monitor 045
2.9 Workflow Editor 046
2.10 Table 046
3.
Node 048
3.1 Node Port 048
3.2 Node Status 049
3.3 Running the node and checking the results 050
4. KNIME AI 052
4.1 Installing K-AI Assistant 052
4.2 KNIME AI Assistant [Q&A] 056
4.3 KNIME AI Assistant [Build] · 060
4.4 Q&A vs. Build 063

Lesson 3. KNIME Data Preprocessing Practice

1.
Data Import 070
1.1 Importing CSV Files 070
1.2 Importing Excel Files 071
2.
Data Merge (JOIN) 073
3.
Column Manipulation 076
3.1 Column Split 076
3.2 Column Filtering 078
3.3 Change Column Name 079
3.4 Change Column Order 082
4.
Missing Value Handling 084
5.
Data Conversion 087
5.1 Data Encoding 087
5.2 Data Manipulation 090
5.3 Math Formula 092
6.
Data Aggregation and Visualization 095
6.1 Data Aggregation 095
6.2 Data Decimal Processing 098
6.3 Data Sorting 100
6.4 Bar Chart Visualization 101

Semifinals.
Data Analysis Practice

1.
Importing Data 109
1.1 Importing CSV Files 109
1.2 Importing Excel files 110
2.
Data Merge (JOIN) 112
2.1 Adding (Joining) Data 112
3.
Data Preprocessing 119
3.1 Handling Missing Values ​​119
3.2 Renaming Columns 122
3.3 Changing the Column Order 124
4.
Creating Features 127
4.1 Math Formula 127
4.2 Data Decimal Handling 129
4.3 One-Hot Encoding 131
4.4 Label Encoding 133
4.5 Numeric Outliers 135
4.6 Data Analysis 138
4.7 Partitioning 140
4.8 Linear Regression Learner 142
4.9 Regression Predictor 144
4.10 Numeric Scorer 145

Lesson 5. KNIME Business Hub & Afterburner

1. Introducing KNIME Business Hub 151
1.1 Collaboration and Project Management 152
1.2 Running and Testing the Workflow 152
1.3 Data Apps, Schedules, and API Services 152
1.4 Version Control and Change Tracking 152
1.5 Integrated Management of Execution Infrastructure 153
2. KNIME Afterburner Portal 153
3.
Business Hub Server Specifications and Features 156
3.1 Installation Conditions 156
3.2 Installation Requirements 157
3.3 Function 159
4. KNIME Business Hub Permission System 160
5.
Data Source Support 161

In conclusion 164

Intuitive Visual Workflow

Easy access and visual workflow design with drag-and-drop functionality

Data Preprocessing & EDA Visualization

Exploratory data analysis (EDA) using various charts and graphs

Integrated support for data science, machine learning, and MLOps

Support for collection, analysis, modeling, machine learning, and deep learning through over 4,000 nodes.
Implementing an end-to-end MLOps pipeline encompassing data preprocessing, training, evaluation, and deployment.

KNIME AI features

Provides natural language-based node search, workflow generation, and code suggestion functions through K-AI.

Highly scalable script support

Utilizes various languages ​​such as R, Python, SQL, and JavaScript

Distribution, Sharing & Scheduling (Business Hub)

Easily deploy and share models and workflows, and automate repetitive analysis and retraining.

This book is designed to be helpful for everyone, from those just starting out with data analysis to those already using KNIME in the field.
From preprocessing to analysis, machine learning, and collaboration environments... I hope this book will serve as a reliable guide for readers learning how to utilize KNIME in their data science journey.