Description
Introduction:
This course focuses on applying KNIME for data science, specifically in building and deploying predictive models. It is designed for data scientists and analysts who want to leverage KNIME’s capabilities to develop, validate, and deploy predictive models. Participants will gain practical experience in using KNIME for various stages of the data science workflow, including data preprocessing, feature engineering, model building, evaluation, and deployment. The course emphasizes hands-on exercises and real-world case studies to help learners effectively apply KNIME in their data science projects.
Prerequisites:
- Proficiency with KNIME fundamentals (building workflows, basic data manipulation)
- Basic understanding of data science concepts and techniques
- Familiarity with machine learning principles is beneficial
- Knowledge of statistical analysis (optional but useful)
Table of Content:
1. Introduction
1.1 Overview of data science and KNIME’s role
1.2 Key concepts and components of predictive modeling
1.3 Setting up KNIME for data science projects
2. Data Preparation and Preprocessing
2.1 Data cleaning and preprocessing techniques
2.2 Handling missing values and outliers
2.3 Feature engineering and selection
3. Exploratory Data Analysis (EDA)
3.1 Performing EDA using KNIME
3.2 Visualizing data distributions and relationships
3.3 Identifying patterns and insights
4. Building Predictive Models
4.1 Introduction to KNIME’s machine learning nodes
4.2 Building and training classification models (e.g., Decision Trees, Random Forest)
4.3 Building and training regression models (e.g., Linear Regression, Support Vector Machines)
5. Model Evaluation and Validation
5.1 Evaluating model performance using metrics (e.g., accuracy, precision, recall, F1-score)
5.2 Cross-validation and hyperparameter tuning
5.3 Handling overfitting and underfitting
6. Advanced Modeling Techniques
6.1 Ensemble methods and model stacking
6.2 Using neural networks and deep learning in KNIME
6.3 Incorporating external libraries and extensions (e.g., TensorFlow, Keras)
7. Model Deployment and Integration
7.1 Preparing models for deployment
7.2 Deploying models using KNIME Server and web services
7.3 Integrating models into existing workflows and systems
8. Automating and Scaling Predictive Workflows
8.1 Automating model training and evaluation processes
8.2 Scaling workflows for large datasets
8.3 Scheduling and monitoring predictive models
9. Case Studies and Practical Applications
9.1 Real-world case studies demonstrating predictive modeling with KNIME
9.2 Hands-on projects to build and deploy models
9.3 Analyzing and interpreting results
10. Best Practices and Advanced Tips
10.1 Best practices for data science workflows in KNIME
10.2 Advanced tips for optimizing model performance
10.3 Staying updated with new KNIME features and tools
11. Conclusion and Future Learning Opportunities
11.1 Recap of key concepts and techniques
11.2 Resources for further learning and advanced topics
11.3 Exploring additional KNIME extensions and applications in data science
To conclude; KNIME offers a comprehensive platform for data science projects, enabling seamless data preparation, model building, evaluation, and deployment. With advanced analytics capabilities and workflow automation, it empowers users to extract meaningful insights and scale predictive models effectively. By following best practices and staying updated with new features, professionals can maximize the potential of KNIME in real-world data science applications.
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