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Introduction to KNIME

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

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3 hours9 videos25 exercises

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Course Description

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.

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  1. 1

    First steps into KNIME Analytics Platform

    Free

    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.

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    KNIME Analytics Platform
    50 xp
    Quiz Quest
    50 xp
    The user interface of KNIME Analytics Platform
    50 xp
    Read the data from HR
    100 xp
    Clean sweep
    100 xp
    Plot it out
    100 xp
  2. 2

    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.

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  3. 3

    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!

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  4. 4

    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.

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datasets

Exercises and Datasets

collaborators

Collaborator's avatar
Iason Prassides
Emilio Silvestri HeadshotEmilio Silvestri

Data Scientist, KNIME's Evangelism Team

Emilio is dedicated to advancing the field of data science through education and outreach. He studied Computer Science in Italy and Germany and worked with visual programming software for a while. At KNIME, Emilio plays a key role in designing, building, and delivering comprehensive courses that cater to a wide range of learners. He leads online and in-person workshops, focusing on making data science accessible to all.
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