KNIME Course Information

KNIME offers the following courses. Currently, due to the Covid-19 situation, all courses are being run online. For an overview of all current courses and other KNIME events, please visit our events overview page.

All KNIME Courses:

[L1-DS] KNIME Analytics Platform for Data Scientists: Basics
[L1-DW] KNIME Analytics Platform for Data Wranglers: Basics
[L1-LS] KNIME Analytics Platform for Data Scientists (Life Science): Basics
[L2-DS] KNIME Analytics Platform for Data Scientists: Advanced
[L2-DW] KNIME Analytics Platform for Data Wranglers: Advanced
[L2-LS] KNIME Analytics Platform for Data Scientists (Life Science): Advanced
[L3-PC] KNIME Server Course: Productionizing and Collaboration
[L4-BD] Introduction to Big Data with KNIME Analytics Platform
[L4-CH] Introduction to Working with Chemical Data
[L4-DV] Codeless Data Exploration and Visualization
[L4-ML] Introduction to Machine Learning Algorithms
[L4-TP] Introduction to Text Processing
[L4-TS] Introduction to Time Series Analysis

 

[L1-DS] KNIME Analytics Platform for Data Scientists: Basics

This course is designed for those who are just getting started on their data science journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

The course then introduces you to KNIME Analytics Platform covering the whole data science cycle from data import, manipulation, aggregation, visualization, model training, and deployment.

NOTE: This course is followed by the [L2-DS] KNIME Analytics Platform for Data Scientists: Advanced.

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[L1-DW] KNIME Analytics Platform for Data Wranglers: Basics

This course is designed for those who are just getting started on their data wrangler journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

The course focuses on accessing, merging, transforming, fixing, standardizing, and inspecting data from different sources. It dives into data cleaning and aggregation, using methods such as advanced filtering, concatenating, joining, pivoting, and grouping. With all of this, you’ll learn how to get your data into the right shape to generate insights quickly.

We’ll take you through everything you need to get started with KNIME Analytics Platform, so you can start creating well-documented, standardized, reusable workflows for your (often) repeated tasks.

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[L1-LS] KNIME Analytics Platform for Data Scientists (Life Science): Basics

This course is designed for Life Scientists who are just getting started on their data science journey with KNIME Analytics Platform. It starts with a detailed introduction of KNIME Analytics Platform - from downloading it through to navigating the workbench.

The course then introduces you to KNIME Analytics Platform covering the whole data science cycle from data import, manipulation, aggregation, visualization, model training, and deployment with a focus on Life Science data.

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Download course material here.

 

[L2-DS] KNIME Analytics Platform for Data Scientists: Advanced

This course builds on the KNIME Analytics Platform for Data Scientist: Basics by introducing advanced data science concepts.

Learn all about flow variables, different workflow controls such as loops, switches, and error handling. Find out how to automatically find the best parameter settings for your machine learning model, see how Date&Time integrations work, and get a taste for ensemble models, parameter optimization, and cross validation.

During the course there’ll be hands-on sessions based on real-world use cases.

NOTE: This course builds on the [L1-DS] KNIME Analytics Platform for Data Scientists: Basics course.

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Download course material here.

 

[L2-DW] KNIME Analytics Platform for Data Wranglers: Advanced

This course builds on the [L1-DW] KNIME Analytics Platform Course for Data Wranglers: Basics by introducing advanced concepts for building and automating workflows.

Learn all about flow variables, different workflow controls such as loops, switches, and how to catch errors. And lastly learn how to visualize your data, export your results, format your Excel tables, and look beyond data wrangling towards data science, training your first classification model.

During the course there’ll be hands-on sessions based on real-world use cases.

Download sample agenda.

Download course material here.

 

[L2-LS] KNIME Analytics Platform for Data Scientists (Life Science): Advanced

This course builds on the [L1-LS] KNIME Analytics Platform for Data Scientists (Life Science): Basics by introducing advanced data science concepts using Life Science examples.

Learn all about flow variables, different workflow controls such as loops, switches, and error handling. Find out how to automatically find the best parameter settings for your machine learning model, get a taste for ensemble models, parameter optimization, and cross validation and see how Date/Time integrations work.

Download sample agenda.

Download course material here.

 

[L3-PC] KNIME Server Course: Productionizing and Collaboration

This course dives into the details of KNIME Server and KNIME WebPortal. Learn how to use KNIME Server to collaborate with colleagues, automate repetitive tasks, and deploy KNIME workflows as analytical applications and services.

Specifically, learn how to share workflows, data, and components with colleagues and among different functions within the company. Learn how to set access rights on your workflows, data, and components, execute workflows remotely on KNIME Server and from the KNIME WebPortal, and schedule report and workflow executions.

You’ll also learn how to build and deploy an analytical application using KNIME Software and how to automate the deployment task using the KNIME Integrated Deployment Extension.

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[L4-BD] Introduction to Big Data with KNIME Analytics Platform

This course focuses on how to use KNIME Analytics Platform for in-database processing and writing/loading data into a database. Get an introduction to the Apache Hadoop ecosystem and learn how to write/load data into your big data cluster running on premise or in the cloud on Amazon EMR, Azure HDInsight, Databricks Runtime or Google Dataproc. Learn about the KNIME Spark Executor, preprocessing with Spark, machine learning with Spark, and how to export data back into KNIME/your big data cluster.

This course lets you put everything you’ve learnt into practice in a hands-on session based on the use case: Eliminating missing values by predicting their values based on other attributes.

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[L4-CH] Introduction to Working with Chemical Data

During this online course you’ll learn to build interactive cheminformatics workflows using KNIME Analytics Platform and its Cheminformatics Extensions. The hands-on training will contain several units where we'll cover a diverse set of topics such as data manipulation and interactive filtering, fingerprints and R-group decomposition, similarity searches and clustering, and data visualization and exploration. After completing this course you'll have a set of fully functional workflows and will have learned how to build your own.

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[L4-DV] Codeless Data Exploration and Visualization

Data visualization is one of the most important parts of data analysis and an integral piece of the whole data science process. It not only enables the communication of results, it also serves to explore and understand data better. For this reason, data visualization is a necessary part of the toolkit for anyone working in data science.

This course focuses on data visualisation goals, primary assumptions, and common techniques. (Please note that this is an introductory data visualization course.) We will explain a variety of approaches to compare data, find relationships, investigate development, and visualize multidimensional data. We will conclude with the creation of interactive dashboards and how to make them accessible via a web browser.

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[L4-ML] Introduction to Machine Learning Algorithms

This course introduces you to the most commonly used Machine Learning algorithms used in Data Science applications.

At the course we will explore different supervised algorithms for classification and numerical problems such as decision trees, logistic regression, and ensemble models. We will also look at recommendation engines and neural networks and investigate the latest advances in deep learning. In addition, we will examine unsupervised learning techniques, such as clustering with k-means, hierarchical clustering, and DBSCAN.

We will also discuss various evaluation metrics for trained models and a number of classic data preparation techniques, such as normalization or dimensionality reduction.

This course is designed for current and aspiring data scientists who would like to learn more about machine learning algorithms used commonly in data science projects.

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[L4-TP] Introduction to Text Processing

This course is about text mining, its theory, concepts, and applications. Specifically, the course focuses on the acquisition, processing and mining of textual data with KNIME Analytics Platform. You will learn how to use the Text Processing Extension to read textual data into KNIME, enrich it semantically, preprocess it, transform it into numerical data, and extract information and knowledge from it through descriptive analytics (data visualization, clustering) and predictive analytics (regression, classification) methods. Course also covers popular text mining applications including social media analytics, topic detection and sentiment analysis.

Put what you’ve learnt into practice with the hands-on exercises.

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[L4-TS] Introduction to Time Series Analysis

This course introduces the main concepts behind Time Series Analysis, with an emphasis on forecasting applications: data cleaning, missing value imputation, time-based aggregation techniques, creation of a vector/tensor of past values, descriptive analysis, model training (from simple basic models to more complex statistics and machine learning based models), hyperparameter optimization, and model evaluation.

Learn how to implement all these steps using real-world time series datasets. Put what you’ve learnt into practice with the hands-on exercises.

Download sample agenda.

Download course material here