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Certificate
Duration 21 hours
Course Outline
Day 1:
Module 1: KNIME Analytics Platform: Introduction
- Software installation procedures
- Initializing and customizing the KNIME Analytics Platform environment
- Understanding nodes, data structures, and workflow architecture
- Overview of the data science lifecycle
Module 2: Data Access
- Importing data from local files
- Interfacing with REST-based services
Module 3: ETL and Data Manipulation
- Filtering by row and column criteria
- Utilizing aggregation techniques
- Executing joins and data concatenation
- Data transformation: type conversion, value replacement, standardization, and feature engineering
- Preparing datasets for time series analysis
Day 2:
Module 4: Data Export
- Writing output to file systems
- Generating analytical reports
Module 5: Data Visualization
- Interactive univariate visual analysis
- Interactive multivariate visual analysis
-
Advanced visualization capabilities
Module 6: Predictive Analytics with KNIME
- Foundational data mining principles
- Regression analysis methods
- Decision tree algorithms
- Model performance evaluation
Day 3:
Module 7: Workflow Control
- Workflow parameterization using flow variables
- Iterative execution of workflow components via loops
- Optimizing and maintaining workflow clarity
Module 8: Practical Case Study on KNIME Analytics Platform
Requirements
Recommended
- A basic understanding of data interpretation.
- Experience with fundamental data processing techniques.
Audience
- Data analysts
- Data scientists
- Business analysts
Testimonials (3)
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Equipped with examples