Data Analytics with KNIME

KNIME Analytics Platform enables you to gain valuable insights by analyzing data and improving efficiency by automating repetitive data tasks, even without programming or special IT skills.

The training has a modular structure and focuses on statistical basics, the use of the KNIME tool, and the topic of data preparation and data visualization in KNIME. KNIME is an open-source application for data analysis that can be used to automate data extract, transform, and load tasks, as well as sophisticated data analyses such as text mining, image processing, and machine learning intuitively and without programming knowledge. We will also show you how to link KNIME with other tools like Power BI. After the basic training, you will be supported in implementing practical applications in the form of learning transfers.

Aim of the further education

• Basics of low-code data analysis, data preparation and data visualization
• Intuitive and productive handling of data
• Introduction and basics of KNIME Analytics Platform
• Statistical & mathematical basics
• Practical implementation of your use cases using so-called learning transfer sessions
• Ability to make data-driven decisions & use data strategically

Basic training (presence or live-online)
Mi., 14.05. - Do., 15.05.2025

Introduction to data analytics

• Motivation and characteristics of data-driven companies
• Terms relating to data science, data mining, data analytics and business intelligence
• Data-driven mindset: development and significance in production
• From data to insights
• Terminology and basics concepts of data analysis

Introduction to KNIME

• Basics of the KNIME Analytics platform
• Start, settings and options
• Overview of the graphical user interface
• Important functions
• Basic principle of nodes and visual workflows
• K-AI: KNIME Artifical Intelligence, Chatbot

Statistical basics

• Application of descriptive statistics
• Dependent/independent variables
• Scale levels
• Measures of position: mean, median and mode
• Measures of dispersion: variance and standard deviation, range and normal distribution
• Pitfalls in data analysis (e.g. causality vs. correlation)

Preparation of data

• Requirements for data quality
• Common data problems
• Data preparation
• Storytelling with data

Extract, Transform, Load (ETL) with KNIME

• Data import of machine data and other data
• Data check, detection of common data problems
• Merging data
• Data cleansing, measures for data preparation, text functions
• Data formats
• Table & workflow organization
• Data export and integration of other tools, e.g. Power BI
• Time and date formats
• Text functions

Reports and visualisations

• Visualization elements and best practices in dashboard design
• Use of different chart types
• Data visualisation with Java Script Nodes in KNIME

Outlook and Q&A session

• Data mining and process mining
• Automation with loop function
• Plug & play utilisation of existing workflows
• Expert tips
• Clarification of open questions

Learning transfer
Mi., 21.05. and Do., 29.05.2025 (2 times 0.5 days 09:00-12:30)

• Specific problems and questions from users related to the use of KNIME and the implementation of use cases are discussed and solved directly. 
• Participants receive intensive support and guidance during the practical exercises. They will also receive tips for implementation, and individual questions will be addressed.
• Specific questions are collected in the appointment's introductory training session, which the trainer prepares for, and then dealt with in the learning transfer units.