Description

Introduction

KNIME Analytics Platform is a powerful open-source data analytics, reporting, and integration tool used widely for data science, machine learning, and AI applications. This beginner-level course introduces participants to the KNIME platform, covering its user-friendly interface, key functionalities, and foundational concepts in data manipulation, visualization, and workflow automation. Through hands-on exercises, learners will build, analyze, and optimize workflows, preparing them for more advanced KNIME applications.

Prerequisites

1. Basic Knowledge of Data Analysis – Understanding of fundamental data analysis concepts is beneficial.
2. No Programming Experience Required – KNIME is a low-code platform, making it accessible to beginners.
3. Interest in Data Science and Workflow Automation – Ideal for professionals looking to automate data processing.

Table of Contents

1. Introduction to KNIME Analytics Platform

• 1.1 Overview of KNIME and Its Applications
• 1.2 Installing and Setting Up KNIME
• 1.3 Understanding the KNIME Workbench
• 1.4 Key Features and Extensions
• 1.5 First Steps: Creating a Basic Workflow

2. Data Importing and Preprocessing

• 2.1 Loading Data from Various Sources (CSV, Excel, Databases)
• 2.2 Data Cleaning and Transformation
• 2.3 Handling Missing Values and Duplicates
• 2.4 Data Type Conversion and Filtering
• 2.5 Automating Preprocessing Steps

3. Data Manipulation and Workflow Optimization

• 3.1 Using Nodes for Data Manipulation
• 3.2 Joining, Merging, and Aggregating Data
• 3.3 Row and Column Filtering Techniques
• 3.4 Conditional and Rule-Based Processing
• 3.5 Optimizing Workflow Performance

4. Data Visualization in KNIME

• 4.1 Introduction to Data Visualization Concepts
• 4.2 Creating Charts, Graphs, and Interactive Reports
• 4.3 Using the KNIME Report Designer
• 4.4 Exporting and Sharing Visualizations
• 4.5 Best Practices for Data Storytelling

5. Introduction to Machine Learning in KNIME

• 5.1 Basics of Machine Learning and AI in KNIME
• 5.2 Implementing Simple Classification Models
• 5.3 Regression Analysis with KNIME(Ref: KNIME for Healthcare Analytics: Enhancing Data-Driven Patient Care)
• 5.4 Model Evaluation and Performance Metrics
• 5.5 Introduction to Advanced KNIME AI Extensions

Conclusion

This KNIME Analytics Platform of L1 course provides a solid foundation in KNIME Analytics Platform, equipping learners with essential skills in data preprocessing, workflow automation, and visualization. By the end of the training, participants will be able to efficiently build and optimize KNIME workflows for real-world data analysis. This KNIME Analytics Platform of L1 training serves as a stepping stone for advanced KNIME analytics, machine learning, and automation techniques.

Reference