Description

Introduction

KNIME (Konstanz Information Miner) is a powerful open-source platform designed for data analytics, machine learning, and data science workflows. It enables seamless collaboration and productionizing of data science models, helping organizations streamline their analytics pipelines.

This training focuses on enhancing proficiency in KNIME for collaborative workflows and enterprise-level deployment. Participants will learn how to manage large-scale data processing, automate workflows, integrate with external tools, and deploy models for production. By the end of this course, learners will have the skills needed to use KNIME for advanced data analytics in a business environment.

Prerequisites

• Basic understanding of data science concepts and analytics.
• Familiarity with KNIME basics (recommended but not mandatory).
• Knowledge of machine learning and scripting (Python, R) is beneficial.

Table of Contents

1. Introduction to KNIME for Collaboration and Productionizing

1.1 Overview of KNIME Software and its capabilities
1.2 Understanding collaborative analytics in KNIME
1.3 Setting up and managing KNIME workflows
1.4 KNIME Hub and sharing workflows with teams

2. Advanced Data Preparation and Processing

2.1 Data import/export from multiple sources
2.2 Data transformation, filtering, and cleansing
2.3 Handling missing values and outliers
2.4 Feature engineering for machine learning models

3. Automating and Scaling Workflows in KNIME

3.1 Looping and workflow automation techniques
3.2 Parallel execution and optimizing performance
3.3 Managing large datasets efficiently
3.4 Scheduling and monitoring automated workflows

4. KNIME Integration with External Tools

4.1 Connecting KNIME with Python and R for advanced analytics
4.2 Database integration and SQL scripting
4.3 API integration and web-based data sources
4.4 Connecting KNIME with cloud platforms

5. Machine Learning and Model Deployment

5.1 Building and evaluating machine learning models in KNIME
5.2 Hyperparameter tuning and optimization
5.3 Deploying models using KNIME Server(Ref: L4-DA: Data Analytics and Visualization in KNIME Analytics Platform)
5.4 Monitoring and updating production models

6. Collaboration and Workflow Management

6.1 Version control and documentation in KNIME
6.2 Best practices for team collaboration
6.3 Managing permissions and user roles
6.4 Integrating KNIME with enterprise data science platforms

7. Advanced Reporting and Visualization

7.1 Creating interactive dashboards in KNIME
7.2 Visualizing machine learning results
7.3 Exporting reports to external tools (Excel, Power BI)
7.4 Automating report generation and distribution

8. Productionizing Data Science with KNIME Server

8.1 Overview of KNIME Server and its features
8.2 Deploying workflows in an enterprise environment
8.3 Managing workflow execution and scheduling
8.4 Ensuring security and compliance in production workflows

9. Case Studies and Hands-on Projects

9.1 Real-world applications of KNIME in enterprises
9.2 End-to-end data science pipeline implementation
9.3 Workflow optimization for efficiency
9.4 Final project presentation and assessment

Conclusion

This training equips participants with the skills to leverage KNIME for collaboration and productionizing data science models in an enterprise setting. By mastering workflow automation, integration, and deployment techniques, learners can drive efficiency and scalability in data-driven decision-making. The course provides hands-on experience, enabling professionals to confidently apply KNIME in real-world analytics projects.

Reference