Introduction to KNIME

Overview

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Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.

Introduction to KNIME Analytics Platform

This course introduces you to the KNIME Analytics Platform, a no-code/low-code tool that streamlines data operations. With KNIME’s drag-and-drop interface, you’ll create workflows to automate data blending, transformation, and analysis, making it accessible to users of all skill levels. By the end, you can perform data analysis without writing code.

Building and Managing Workflows

Learn how to build workflows from scratch using KNIME’s visual programming tools. This section covers everything from data access—importing files and querying databases—to data cleaning, where you’ll handle missing values, remove duplicates, and prepare data for analysis. With these skills, you’ll create efficient end-to-end workflows that handle data preparation and manipulation.

Data Analysis and Aggregation

Apply the knowledge you’ve gained to analyze and summarize data. You’ll merge cleaned datasets and use aggregation techniques to answer key questions. By the end, you'll be confident in using KNIME to perform data analysis and extract valuable insights from your data.

Syllabus

First steps into KNIME Analytics Platform

In this chapter, you will get a first touch of KNIME Analytics Platform, a no-code/low-code tool that lets you handle various data tasks with visual programming. You will create your first KNIME workflow and produce a simple data analysis.

Data access

Now that you have created your first end-to-end workflow, let's do a step back and dig deeper into the very first step of any data analysis: data access. In this chapter, you will learn the different ways to read a file stored on your computer or somewhere else and how to build a database query without writing it.

Data Cleaning

After accessing the data, in this chapter you will do the dirty job of cleaning the data for the HR department. You will remove unnecessary or duplicated data, handle missing values, remove characters from strings and convert data types. After all this job, the data will be ready for analysis!

Data analysis

Time to put things together! In this chapter you will merge the data that you have accessed and cleaned and will aggregate it to answer some questions for the HR department.