Applied Data Science in KNIME Software

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Course Data analysis in KNIME Software enables students to cover every aspect of data science process from accessing the data, data preparation, visual analytics and machine learning.


KNIME Software is the leading open solution for data-driven innovation, designed for discovering the potential hidden in data, mining for fresh insights, or predicting new futures. It is Leader in Gartner Magic Quadrant and used by big companies like Procter & Gamble, Verizon or Unilever for data science processes. In this two day course student will learn how to use KNIME Analytics Platform to unleash potential of data using KNIME’s graphical interface and workflows through numerious number of nodes.


After finishing this course students will be able to:

  • Create data science workflow in KNIME Analytics Platform
  • Access and preprocess data in fast and efficient way
  • Use statistics to explain correlations between data
  • Automatize preparation and analytics tasks
  • Structure data from unstructured sources like JSON or XML
  • Visualize data in various formats in Javascript
  • Integrate R and Python in workflow
  • Use machine learning algorithms to predict the future or explain unknown associations
  • Export data to your favourite business analytics tool


This course is inteded for all students who use data in everyday work, but want to raise their knowledge to higher level. This course is perfect for data analysts, data scientist, business analyst and data engineers roles.


Day 1. – Data Preparation 

  • Introduction to KNIME Software arhitecuture
  • Importing data from various sources (CSV, Excel, databases, JSON, REST API)
  • Joining data from multiple data sources
  • Filtering and transforming data
  • Data aggregations
  • Pivoting and upivoting
  • Writing data to multiple data sources (Database, Excel, CSV)
  • Automatization of everyday analytics tasks

Day 2. Data Analytics

  • Visualizing data in multiple charts
  • Integration with R language
  • Integration with Python
  • Statistics overview
  • Introduction to machine learning
  • Using classification algorithms (decision trees, ensambles, Bayes)
  • Using clustering algorithms (k-means)
  • Association algorithms (market basket analysis)

NOTE: This is not an official KNIME course and it is based on the experience of Poslovna inteligencija’s consultants.

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