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Practical implementation of data analytics and data science with KNIME
Course
2

Booking no:

36405

Practical implementation of data analytics and data science with KNIME

Learn how to master the first steps in data analysis and implement complete data processes with the help of the KNIME tool. Among other things, you will learn how to use algorithms to evaluate data and visualize the results of your data analyses.

24 weeks
approx. 54 hours learning time
5 webinars & 4 self-study phases
German
Master Class with presence and Master Class Online

Date preview

Start date
Last module
Availability
Location
23.6.2025
15.12.2025
Few places available
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
1.9.2025
23.2.2026
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
10.11.2025
4.5.2026
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
23.2.2026
24.8.2026
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online

Module overview

The following module overview shows dates for the course start on
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Module
1

Kick-off and joint start to training

The first webinar starts with a detailed presentation of the structure and objectives of the course. Together, we will also take a look at the first learning units and discuss personal expectations.

Webinar
60 min.

Monday, 23.06.2025
11:00 a.m. - 12:00 p.m.

61402562
Module
1

Kick-off and joint start to training

The first webinar starts with a detailed presentation of the structure and objectives of the course. Together, we will also take a look at the first learning units and discuss personal expectations.

Webinar
60 min.

Monday, 01.09.2025
11:00 a.m. - 12:00 p.m.

61402563
Module
1

Kick-off and joint start to training

The first webinar starts with a detailed presentation of the structure and objectives of the course. Together, we will also take a look at the first learning units and discuss personal expectations.

Webinar
60 min.

Monday, 10.11.2025
09:00 am - 10:00 am

61417124
Module
1

Kick-off and joint start to training

The first webinar starts with a detailed presentation of the structure and objectives of the course. Together, we will also take a look at the first learning units and discuss personal expectations.

Webinar
60 min.

Monday, 23.02.2026
09:00 am - 10:00 am

61417176
Module
2

Business understanding and first steps in KNIME

Part 1: Business understanding and concepts of data mining

At the beginning of the first self-study unit, you will learn to identify use cases and describe how you can solve business problems by analyzing data and applying machine learning. You will then learn all about the basics and requirements for your own data projects and dive deep into the concepts of data structures and machine learning:

  • Knowledge check on data science and artificial intelligence
  • The skills and role of the data scientist
  • The composition of data teams
  • Vertical fields of application in companies
  • Horizontal fields of application in companies
  • Supervised learning: classification, regression, time series
  • Unsupervised learning: segments, anomalies, patterns
  • Reinforcement learning
  • Collect data and assess relevance
  • Big data, data types, data structures
  • Data integration, data sources, databases
  • Data warehouse, virtual databases and data lake

Part 2: Installation and introduction to working with KNIME

After you have learned all about the basics and concepts of data science, the next step is to take your first steps in practical application. You will familiarize yourself with the use of the KNIME tool, a powerful development platform for data mining, with which you can carry out complex data analysis and data science projects:

  • Set up installation and workspace
  • The concept of nodes and workflows
  • Getting to know the user interface
  • Excel Reader and configuration menu
  • Outputs and other import nodes
  • Documentation and workflow organization
Self-study phase
6 weeks
61402562
Module
2

Business understanding and first steps in KNIME

Part 1: Business understanding and concepts of data mining

At the beginning of the first self-study unit, you will learn to identify use cases and describe how you can solve business problems by analyzing data and applying machine learning. You will then learn all about the basics and requirements for your own data projects and dive deep into the concepts of data structures and machine learning:

  • Knowledge check on data science and artificial intelligence
  • The skills and role of the data scientist
  • The composition of data teams
  • Vertical fields of application in companies
  • Horizontal fields of application in companies
  • Supervised learning: classification, regression, time series
  • Unsupervised learning: segments, anomalies, patterns
  • Reinforcement learning
  • Collect data and assess relevance
  • Big data, data types, data structures
  • Data integration, data sources, databases
  • Data warehouse, virtual databases and data lake

Part 2: Installation and introduction to working with KNIME

After you have learned all about the basics and concepts of data science, the next step is to take your first steps in practical application. You will familiarize yourself with the use of the KNIME tool, a powerful development platform for data mining, with which you can carry out complex data analysis and data science projects:

  • Set up installation and workspace
  • The concept of nodes and workflows
  • Getting to know the user interface
  • Excel Reader and configuration menu
  • Outputs and other import nodes
  • Documentation and workflow organization
Self-study phase
6 weeks
61402563
Module
2

Business understanding and first steps in KNIME

Part 1: Business understanding and concepts of data mining

At the beginning of the first self-study unit, you will learn to identify use cases and describe how you can solve business problems by analyzing data and applying machine learning. You will then learn all about the basics and requirements for your own data projects and dive deep into the concepts of data structures and machine learning:

  • Knowledge check on data science and artificial intelligence
  • The skills and role of the data scientist
  • The composition of data teams
  • Vertical fields of application in companies
  • Horizontal fields of application in companies
  • Supervised learning: classification, regression, time series
  • Unsupervised learning: segments, anomalies, patterns
  • Reinforcement learning
  • Collect data and assess relevance
  • Big data, data types, data structures
  • Data integration, data sources, databases
  • Data warehouse, virtual databases and data lake

Part 2: Installation and introduction to working with KNIME

After you have learned all about the basics and concepts of data science, the next step is to take your first steps in practical application. You will familiarize yourself with the use of the KNIME tool, a powerful development platform for data mining, with which you can carry out complex data analysis and data science projects:

  • Set up installation and workspace
  • The concept of nodes and workflows
  • Getting to know the user interface
  • Excel Reader and configuration menu
  • Outputs and other import nodes
  • Documentation and workflow organization
Self-study phase
6 weeks
61417124
Module
2

Business understanding and first steps in KNIME

Part 1: Business understanding and concepts of data mining

At the beginning of the first self-study unit, you will learn to identify use cases and describe how you can solve business problems by analyzing data and applying machine learning. You will then learn all about the basics and requirements for your own data projects and dive deep into the concepts of data structures and machine learning:

  • Knowledge check on data science and artificial intelligence
  • The skills and role of the data scientist
  • The composition of data teams
  • Vertical fields of application in companies
  • Horizontal fields of application in companies
  • Supervised learning: classification, regression, time series
  • Unsupervised learning: segments, anomalies, patterns
  • Reinforcement learning
  • Collect data and assess relevance
  • Big data, data types, data structures
  • Data integration, data sources, databases
  • Data warehouse, virtual databases and data lake

Part 2: Installation and introduction to working with KNIME

After you have learned all about the basics and concepts of data science, the next step is to take your first steps in practical application. You will familiarize yourself with the use of the KNIME tool, a powerful development platform for data mining, with which you can carry out complex data analysis and data science projects:

  • Set up installation and workspace
  • The concept of nodes and workflows
  • Getting to know the user interface
  • Excel Reader and configuration menu
  • Outputs and other import nodes
  • Documentation and workflow organization
Self-study phase
6 weeks
61417176
Module
3

Question time and preparation for work in KNIME

In the second webinar, you will review the content of the first self-study phase together with the trainers .

Webinar
60 min.

Monday, 04.08.2025
1:00 pm - 2:00 pm

61402562
Module
3

Question time and preparation for work in KNIME

In the second webinar, you will review the content of the first self-study phase together with the trainers .

Webinar
60 min.

Monday, 06.10.2025
11:00 a.m. - 12:00 p.m.

61402563
Module
3

Question time and preparation for work in KNIME

In the second webinar, you will review the content of the first self-study phase together with the trainers .

Webinar
60 min.

Monday, 15.12.2025
09:00 am - 10:00 am

61417124
Module
3

Question time and preparation for work in KNIME

In the second webinar, you will review the content of the first self-study phase together with the trainers .

Webinar
60 min.

Monday, 30.03.2026
09:00 am - 10:00 am

61417176
Module
4

Data Understanding and Data Preparation

 

In this module, you will learn all the steps and concepts for preparing the data for the modeling process. First, you will analyse the data by applying different visualization techniques to identify patterns, trends and outliers. You will then clean the data and prepare it for transformation:

  • Data preparation: Recognizing problems
  • Strategies for solving problems
  • Harmonization of time series
  • Data visualization and data analysis
  • Tables, diagrams, parameters, covariance
  • Data visualization in KNIME
  • Data cleansing in KNIME
  • Data transformation in KNIME
  • Exercise project for data preparation
Self-study phase
6 weeks
61402562
Module
4

Data Understanding and Data Preparation

 

In this module, you will learn all the steps and concepts for preparing the data for the modeling process. First, you will analyse the data by applying different visualization techniques to identify patterns, trends and outliers. You will then clean the data and prepare it for transformation:

  • Data preparation: Recognizing problems
  • Strategies for solving problems
  • Harmonization of time series
  • Data visualization and data analysis
  • Tables, diagrams, parameters, covariance
  • Data visualization in KNIME
  • Data cleansing in KNIME
  • Data transformation in KNIME
  • Exercise project for data preparation
Self-study phase
6 weeks
61402563
Module
4

Data Understanding and Data Preparation

 

In this module, you will learn all the steps and concepts for preparing the data for the modeling process. First, you will analyse the data by applying different visualization techniques to identify patterns, trends and outliers. You will then clean the data and prepare it for transformation:

  • Data preparation: Recognizing problems
  • Strategies for solving problems
  • Harmonization of time series
  • Data visualization and data analysis
  • Tables, diagrams, parameters, covariance
  • Data visualization in KNIME
  • Data cleansing in KNIME
  • Data transformation in KNIME
  • Exercise project for data preparation
Self-study phase
6 weeks
61417124
Module
4

Data Understanding and Data Preparation

 

In this module, you will learn all the steps and concepts for preparing the data for the modeling process. First, you will analyse the data by applying different visualization techniques to identify patterns, trends and outliers. You will then clean the data and prepare it for transformation:

  • Data preparation: Recognizing problems
  • Strategies for solving problems
  • Harmonization of time series
  • Data visualization and data analysis
  • Tables, diagrams, parameters, covariance
  • Data visualization in KNIME
  • Data cleansing in KNIME
  • Data transformation in KNIME
  • Exercise project for data preparation
Self-study phase
6 weeks
61417176
Module
5

Reflection on the exercise from module 4

In the third webinar, you will focus on the exercise project together with the speaker. A possible solution will be presented. Of course, there will also be ample opportunity to ask questions.

Webinar
60 min.

Monday, 15.09.2025
1:00 pm - 2:00 pm

61402562
Module
5

Reflection on the exercise from module 4

In the third webinar, you will focus on the exercise project together with the speaker. A possible solution will be presented. Of course, there will also be ample opportunity to ask questions.

Webinar
60 min.

Monday, 17.11.2025
11:00 a.m. - 12:00 p.m.

61402563
Module
5

Reflection on the exercise from module 4

In the third webinar, you will focus on the exercise project together with the speaker. A possible solution will be presented. Of course, there will also be ample opportunity to ask questions.

Webinar
60 min.

Monday, 26.01.2026
09:00 am - 10:00 am

61417124
Module
5

Reflection on the exercise from module 4

In the third webinar, you will focus on the exercise project together with the speaker. A possible solution will be presented. Of course, there will also be ample opportunity to ask questions.

Webinar
60 min.

Monday, 11.05.2026
09:00 am - 10:00 am

61417176
Module
6

The modeling phase

Now it's time for modeling based on the data. You will first learn how to find the right algorithm and methodology to achieve optimal results. You will then focus on how to adequately evaluate and interpret the results of the models. In practical exercises, you will carry out the processes yourself in KNIME and also implement more complex classification and clustering tasks:

  • Properties for modeling
  • Select algorithm and methodology: Classification and KNN
  • Evaluate result: Classification and KNN
  • Machine Learning in KNIME: Classification
  • Select algorithm and methodology: Regression and clustering
  • Evaluate the result: Regression and clustering
  • Machine learning in KNIME: complex classification and clustering
  • Exercise project for creating the data model
Self-study phase
6 weeks
61402562
Module
6

The modeling phase

Now it's time for modeling based on the data. You will first learn how to find the right algorithm and methodology to achieve optimal results. You will then focus on how to adequately evaluate and interpret the results of the models. In practical exercises, you will carry out the processes yourself in KNIME and also implement more complex classification and clustering tasks:

  • Properties for modeling
  • Select algorithm and methodology: Classification and KNN
  • Evaluate result: Classification and KNN
  • Machine Learning in KNIME: Classification
  • Select algorithm and methodology: Regression and clustering
  • Evaluate the result: Regression and clustering
  • Machine learning in KNIME: complex classification and clustering
  • Exercise project for creating the data model
Self-study phase
6 weeks
61402563
Module
6

The modeling phase

Now it's time for modeling based on the data. You will first learn how to find the right algorithm and methodology to achieve optimal results. You will then focus on how to adequately evaluate and interpret the results of the models. In practical exercises, you will carry out the processes yourself in KNIME and also implement more complex classification and clustering tasks:

  • Properties for modeling
  • Select algorithm and methodology: Classification and KNN
  • Evaluate result: Classification and KNN
  • Machine Learning in KNIME: Classification
  • Select algorithm and methodology: Regression and clustering
  • Evaluate the result: Regression and clustering
  • Machine learning in KNIME: complex classification and clustering
  • Exercise project for creating the data model
Self-study phase
6 weeks
61417124
Module
6

The modeling phase

Now it's time for modeling based on the data. You will first learn how to find the right algorithm and methodology to achieve optimal results. You will then focus on how to adequately evaluate and interpret the results of the models. In practical exercises, you will carry out the processes yourself in KNIME and also implement more complex classification and clustering tasks:

  • Properties for modeling
  • Select algorithm and methodology: Classification and KNN
  • Evaluate result: Classification and KNN
  • Machine Learning in KNIME: Classification
  • Select algorithm and methodology: Regression and clustering
  • Evaluate the result: Regression and clustering
  • Machine learning in KNIME: complex classification and clustering
  • Exercise project for creating the data model
Self-study phase
6 weeks
61417176
Module
7

Q&A session and joint deep-dive into modeling

You will use the fourth webinar for another deep dive into modeling concepts. You will review the most important facts about algorithms and methodologies and focus in particular on evaluating the results of the models used.

Webinar
60 min.

Monday, 03.11.2025
11:00 a.m. - 12:00 p.m.

61402562
Module
7

Q&A session and joint deep-dive into modeling

You will use the fourth webinar for another deep dive into modeling concepts. You will review the most important facts about algorithms and methodologies and focus in particular on evaluating the results of the models used.

Webinar
60 min.

Monday, 12.01.2026
11:00 a.m. - 12:00 p.m.

61402563
Module
7

Q&A session and joint deep-dive into modeling

You will use the fourth webinar for another deep dive into modeling concepts. You will review the most important facts about algorithms and methodologies and focus in particular on evaluating the results of the models used.

Webinar
60 min.

Monday, 16.03.2026
09:00 am - 10:00 am

61417124
Module
7

Q&A session and joint deep-dive into modeling

You will use the fourth webinar for another deep dive into modeling concepts. You will review the most important facts about algorithms and methodologies and focus in particular on evaluating the results of the models used.

Webinar
60 min.

Monday, 06.07.2026
09:00 am - 10:00 am

61417176
Module
8

Deployment, monitoring and troubleshooting

The fourth self-learning phase deals with the final steps in the CRISP-DM process, the evaluation and deployment of the data model. Here, too, you will proceed step by step using instructions and your own practical tasks in KNIME. At the end, you will put the entire process into production and transfer it to an automated KNIME workflow:

  • Implementation of the model
  • Structured testing of the model
  • Systematic troubleshooting
  • Monitoring and verification
  • Maintenance and updating
  • Exporting data to databases
  • Creating and sending reports 
  • Automated execution of the KNIME workflow
  • Practice project on evaluation and deployment
Self-study phase
6 weeks
61402562
Module
8

Deployment, monitoring and troubleshooting

The fourth self-learning phase deals with the final steps in the CRISP-DM process, the evaluation and deployment of the data model. Here, too, you will proceed step by step using instructions and your own practical tasks in KNIME. At the end, you will put the entire process into production and transfer it to an automated KNIME workflow:

  • Implementation of the model
  • Structured testing of the model
  • Systematic troubleshooting
  • Monitoring and verification
  • Maintenance and updating
  • Exporting data to databases
  • Creating and sending reports 
  • Automated execution of the KNIME workflow
  • Practice project on evaluation and deployment
Self-study phase
6 weeks
61402563
Module
8

Deployment, monitoring and troubleshooting

The fourth self-learning phase deals with the final steps in the CRISP-DM process, the evaluation and deployment of the data model. Here, too, you will proceed step by step using instructions and your own practical tasks in KNIME. At the end, you will put the entire process into production and transfer it to an automated KNIME workflow:

  • Implementation of the model
  • Structured testing of the model
  • Systematic troubleshooting
  • Monitoring and verification
  • Maintenance and updating
  • Exporting data to databases
  • Creating and sending reports 
  • Automated execution of the KNIME workflow
  • Practice project on evaluation and deployment
Self-study phase
6 weeks
61417124
Module
8

Deployment, monitoring and troubleshooting

The fourth self-learning phase deals with the final steps in the CRISP-DM process, the evaluation and deployment of the data model. Here, too, you will proceed step by step using instructions and your own practical tasks in KNIME. At the end, you will put the entire process into production and transfer it to an automated KNIME workflow:

  • Implementation of the model
  • Structured testing of the model
  • Systematic troubleshooting
  • Monitoring and verification
  • Maintenance and updating
  • Exporting data to databases
  • Creating and sending reports 
  • Automated execution of the KNIME workflow
  • Practice project on evaluation and deployment
Self-study phase
6 weeks
61417176
Module
9

Reflection on the exercise task from Module 8 and conclusion

 

In the final webinar, you will focus on the exercise project from Module 8. In addition to insights for solutions to the task, you will also receive tips and advice from the instructor on how to prepare for the final exam.

Webinar
60 min.

Monday, 15.12.2025
11:00 a.m. - 12:00 p.m.

61402562
Module
9

Reflection on the exercise task from Module 8 and conclusion

 

In the final webinar, you will focus on the exercise project from Module 8. In addition to insights for solutions to the task, you will also receive tips and advice from the instructor on how to prepare for the final exam.

Webinar
60 min.

Monday, 23.02.2026
11:00 a.m. - 12:00 p.m.

61402563
Module
9

Reflection on the exercise task from Module 8 and conclusion

 

In the final webinar, you will focus on the exercise project from Module 8. In addition to insights for solutions to the task, you will also receive tips and advice from the instructor on how to prepare for the final exam.

Webinar
60 min.

Monday, 04.05.2026
09:00 am - 10:00 am

61417124
Module
9

Reflection on the exercise task from Module 8 and conclusion

 

In the final webinar, you will focus on the exercise project from Module 8. In addition to insights for solutions to the task, you will also receive tips and advice from the instructor on how to prepare for the final exam.

Webinar
60 min.

Monday, 24.08.2026
09:00 am - 10:00 am

61417176

Course overview

Contents

1. business understanding for data analysis

  • Knowledge check on data science and artificial intelligence
  • The skills and role of the data scientist
  • The composition of data teams
  • Vertical fields of application in companies
  • Horizontal fields of application in companies
  • Supervised learning: classification, regression, time series
  • Unsupervised learning: segments, anomalies, patterns
  • Reinforcement learning
  • Collect data and assess relevance
  • Big data, data types, data structures
  • Data integration, data sources, databases
  • Data warehouse, virtual databases and data lake

2. operation and first steps in KNIME

  • Set up installation and workspace
  • The concept of nodes and workflows
  • Getting to know the user interface
  • Excel Reader and configuration menu
  • Outputs and other import nodes
  • Documentation and workflow organization

3. data understanding and data preparation

  • Data preparation: Recognizing problems
  • Strategies for solving problems
  • Harmonization of time series
  • Data visualization and data analysis
  • Tables, diagrams, parameters, covariance
  • Data visualization in KNIME
  • Data cleansing in KNIME
  • Data transformation in KNIME

4. data modeling

  • Properties for modeling
  • Select algorithm and methodology: Classification and KNN
  • Evaluate result: Classification and KNN
  • Machine Learning in KNIME: Classification
  • Select algorithm and methodology: Regression and clustering
  • Evaluate the result: Regression and clustering
  • Machine learning in KNIME: complex classification and clustering

5. deployment, monitoring and troubleshooting

  • Implementation of the model
  • Structured testing of the model
  • Systematic troubleshooting
  • Monitoring and verification
  • Maintenance and updating
  • Exporting data to databases
  • Creating and sending reports 
  • Automated execution of the KNIME workflow

6. final project

After the practical tasks, with which various scenarios and the individual stages of the CRIPS-DM process are practiced, there is a final project at the end of the course in which the entire data analysis process is run through.

This is how you learn in this course

This online course offers you a digital blended concept that has been specially developed for part-time learning. With a time budget of at least 3-4 hours per week, you are sure to reach your goal. Alternatively, you can schedule the learning units flexibly. This is how you learn in the course:

Self-study phases: Learn independently, at your own pace and whenever you want. Our courses offer you didactically high-quality learning material with videos, articles, interactive exercises, quizzes and learning checks.

Live webinars: In regular online seminars, you will meet your trainers in person. You will receive answers to your questions, specific assistance and instructions on how to deepen your knowledge and apply the skills you have acquired in practical exercises.

Practical assignments: To learn the skills in practice, you will be given access to data projects that allow you to apply the techniques and methods you have learned to real-world problems. Through these exercises, you will gain a deep understanding of working with data and develop methods and techniques that you can apply in your everyday work.

Learning community: A digital learning community is available to you throughout the course. Exchange ideas with other participants and the trainers and ask questions.

Certificate of attendance and Open Badge: As a graduate of the course, you will receive a certificate and an Open Badge that you can easily share in professional networks (e.g. LinkedIn).

Future Jobs Club: Get exclusive access to a business network, micro-learningssparks), news and future work hacks.

In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.