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Certificate
Course Outline
Day 1:
Module 1: Overview of KNIME Analytics Platform
- Installation process.
- Launching and customizing the KNIME Analytics Platform.
- Understanding nodes, data, and workflows.
- The data science lifecycle.
Module 2: Data Access
- Reading data from files.
- Accessing REST services.
Module 3: ETL and Data Manipulation
- Filtering rows and columns.
- Using aggregators.
- Joining and concatenating data.
- Transformation techniques: Conversion, replacement, standardization, and feature generation.
- Preparing data for time series analysis.
Day 2:
Module 4: Exporting Data
- Writing data to files.
- Generating reports.
Module 5: Data Visualization
- Interactive univariate visual exploration.
- Interactive multivariate visual exploration.
- Advanced visualization features.
Module 6: Predictive Analytics with KNIME
- Fundamental concepts of data mining.
- Regression techniques.
- The decision tree family.
- Model evaluation methods.
Day 3:
Module 7: Controlling Workflow Flow
- Workflow parameterization using flow variables.
- Re-executing parts of a workflow using loops.
- Cleaning up and organizing your workflow.
Module 8: Practical Case Study using KNIME Analytics Platform
Requirements
Recommended Background
- A fundamental understanding of interpreting data.
- Experience with basic data processing techniques.
Target Audience
- Data analysts.
- Data scientists.
- Business analysts.
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
