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Course Outline
- Introduction to data processing and analysis
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Basic information about the KNIME platform
- installation and configuration
- interface overview
- Platform overview from the perspective of tool integration
- Getting started. Creating workflows
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Methodology for creating business models and data processing processes
- documentation of work
- methods for importing and exporting processes
- Overview of basic nodes
- Overview of ETL processes
- Data mining methodologies
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Data import methodologies
- importing data from files
- importing data from relational databases using SQL
- creating SQL queries
- Overview of advanced nodes
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Data analysis
- preparing data for analysis
- data quality and validation
- statistical data exploration
- data modeling
- Introduction to the use of variables and loops
- Building advanced, automated processes
- Visualization of results
- Public and free data sources
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Data Mining basics
- Overview of selected data mining tasks and processes
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Knowledge discovery from data
- Web Mining
- SNA – Social Network Analysis
- Text Mining – document analysis
- data visualization on maps
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Integration of other tools with KNIME
- R
- Java
- Python
- Gephi
- Neo4j
- Report building
- Training summary
Requirements
Knowledge of basic mathematical analysis.
Knowledge of basic statistics.
35 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.
