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.

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.

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.

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

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

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

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

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

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

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

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

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

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.

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.

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.

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

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

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

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.

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.

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.

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

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

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

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.

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.

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.