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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
- overview of the interface
- Overview of the platform in the context of tool integration
- Introduction to practical work: creating workflows
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Methodologies for building business models and data processing processes
- documentation of work
- methods for importing and exporting processes
- Overview of basic nodes
- Overview of ETL processes
- Data exploration 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 examination of data
- data modelling
- Introduction to the use of variables and loops
- Building advanced, automated processes
- Visualisation of results
- Publicly available and free data sources
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Basics of Data Mining
- Overview of selected types of Data Mining tasks and processes
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Knowledge discovery from data
- Web Mining
- SNA – Social Network Analysis
- Text Mining – document analysis
- data visualisation on maps
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Integration of other tools with KNIME
- R
- Java
- Python
- Gephi
- Neo4j
- Building reports
- Training summary
Requirements
Basic knowledge of mathematical analysis.
Basic knowledge of statistics.
35 Hours
Testimonials (3)
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.
Ian - Archeoworks Inc.
Course - ArcGIS Fundamentals
Radomir was very patient and helpful when I got stuck. He was very knowledgeable about the subject matter, and his explanations on different concepts were detailed and thorough.
Rachel Turcan - Animal and Plant Health Agency
Course - ArcGIS for Spatial Analysis
It's a hands-on session.
