Data Science with KNIME Analytics Platform - Advanced

This course builds on the Data Literacy with KNIME Analytics Platform: Basics - L1-AP course and introduces advanced concepts for creating and automating workflows with the KNIME Analytics Platform Version 5.

This course covers topics related to controlling node settings and automating workflow execution. You will learn about concepts such as flow variables, loops, switches and error catching. You will also learn how to handle date and time data, create advanced dashboards and process data within a database.

In addition, this course introduces you to the basic concepts of data science. You will learn how to train, apply and evaluate a supervised machine learning (ML) model. The course also covers how to optimize and validate your ML model. The course concludes with unsupervised learning using the example of K-means clustering.

• Flow Variables
• Workflow Control and Invocation
• Date & Time, Databases, REST Services, Python & R Integration
• Introduction to Machine Learning
• Review of the Last Exercises and Q&A

Please note: This course consists of four 75-minute online sessions conducted by a KNIME data scientist. Each session includes an exercise for you to complete at home. Together we will go through the solution at the beginning of the following session. On day 5, the course ends with a 15-30 minute final session.

You should be an advanced KNIME user and ideally have already created a few workflows.

This course does not provide an introduction to the KNIME Analytics Platform - it focuses on more advanced data science concepts.

It is recommended to attend the course Data Literacy with KNIME Analytics Platform: Basics - L1-AP beforehand.

You should already have the latest version of the KNIME Analytics Platform installed on your laptop, which you can download here: knime.com/downloads