Course Outline:
Introduction to KNIME:
Overview of KNIME and its applications
Installing and setting up KNIME Analytics Platform
Navigating the KNIME interface (Node Repository, Workflow Editor, Console)
Understanding KNIME concepts (nodes, workflows, data tables)
Getting Started with KNIME:
Creating and managing workflows
Adding, configuring, and connecting nodes
Running and executing workflows
Saving and exporting workflows
Data Input and Output:
Importing data from various sources (Excel, CSV, databases, web services)
Data import nodes and configuration
Exporting results and data to different formats
Using file readers and writers in KNIME
Data Preparation and Cleaning:
Data preprocessing techniques (filtering, sorting, grouping)
Handling missing values and outliers
Data transformation (merging, splitting, pivoting)
Creating and manipulating columns and data types
Exploratory Data Analysis:
Descriptive statistics and summary analysis
Data visualization (histograms, scatter plots, bar charts)
Analyzing data distributions and patterns
Using KNIME’s visualization nodes
Machine Learning and Modeling:
Introduction to machine learning concepts
Building and training models (regression, classification, clustering)
Using KNIME’s built-in machine learning algorithms
Evaluating model performance (accuracy, precision, recall, ROC)
Advanced Analytics:
Feature engineering and selection
Hyperparameter tuning and optimization
Ensemble methods and advanced modeling techniques
Time series analysis and forecasting
Text Mining and Natural Language Processing (NLP):
Introduction to text mining in KNIME
Text preprocessing and feature extraction
Performing sentiment analysis and text classification
Using KNIME’s text processing nodes
Automation and Workflow Optimization:
Automating workflows with loops and meta-nodes
Using flow variables for dynamic workflow execution
Scheduling and running workflows on KNIME Server
Debugging and optimizing workflows
Integration with Other Tools:
Connecting KNIME with external databases and APIs
Integrating with other data science tools (Tableau, Power BI, R, Python)
Using KNIME extensions and plugins for additional functionality
Exporting results and integrating with BI systems
KNIME Server and Deployment:
Introduction to KNIME Server and its features
Managing and scheduling workflows on KNIME Server
Collaborating and sharing workflows and models
Monitoring and maintaining KNIME Server
Best Practices and Case Studies:
Real-world examples and case studies
Practical exercises and projects
Best practices for data analysis and workflow management in KNIME
Skills Gained:
Proficiency in using KNIME for data preparation, analysis, and modeling
Ability to create and manage complex data workflows
Skills in machine learning and advanced analytics
Knowledge of text mining and NLP techniques
Competence in automating workflows and integrating with other tools
Target Audience:
Data scientists
Data analysts
Business analysts
IT professionals
Students and professionals interested in data science and analytics
Course Logistics:
Location: Training centers, educational institutions, or online platforms
Duration: Typically ranges from a few days to several weeks, depending on the course depth and schedule
Format: Classroom-based, online, or hybrid