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Course Outline

  1. Introduction to data processing and analysis
  2. Basic information about the KNIME platform
    • installation and configuration
    • interface overview
  3. Platform overview in the context of tool integration
  4. Introduction to workflow creation
  5. Methodology for creating business models and data processing processes
    • documentation of workflows
    • methods for importing and exporting processes
  6. Overview of basic nodes
  7. Overview of ETL processes
  8. Data mining methodologies
  9. Data import methodologies
    • importing data from files
    • importing data from relational databases using SQL
    • creating SQL queries
  10. Overview of advanced nodes
  11. Data analysis
    • data preparation for analysis
    • data quality and validation
    • statistical data analysis
    • data modelling
  12. Introduction to using variables and loops
  13. Building advanced, automated processes
  14. Visualization of results
  15. Publicly available and free data sources
  16. Fundamentals of Data Mining
    • Overview of selected types of Data Mining tasks and processes
  17. Knowledge discovery from data
    • Web Mining
    • SNA – Social Network Analysis
    • Text Mining – document analysis
    • data visualization on maps
  18. Integration of other tools with KNIME
    • R
    • Java
    • Python
    • Gephi
    • Neo4j
  19. Report building
  20. Training summary

Requirements

Knowledge of fundamental mathematical analysis.

Knowledge of basic statistics.

 35 Hours

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