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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
- Overview of the platform in terms of tool integration
- Introduction to working. 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
- quality and validation of data
- statistical data examination
- data modeling
- Introduction to using variables and loops
- Building advanced, automated processes
- Visualization 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 networks
- Text Mining – document analysis
- visualizing data on maps
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Integrating other tools with KNIME
- R
- Java
- Python
- Gephi
- Neo4j
- Building reports
- Course summary
Requirements
Knowledge of the basics of mathematical analysis.
Knowledge of the basics 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.
