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

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

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

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

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

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

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

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the first self-study unit, you will learn everything you need to know about data science. You will get to know and differentiate between the terms, understand what purpose data science can serve and gain valuable insights into how data projects should be approached in the company.

• Basic terms and concepts of data science
• Data-driven mindset as a success factor in the company
• Requirements for data-driven companies
• Supervised and unsupervised machine learning
• Methods in supervised and unsupervised learning
• Important roles in data science projects
• What infrastructure is needed for data science projects?

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second webinar, you will learn all about the standard for the data project process, the CRISP-DM process. You will then work with your trainers to develop starting points for your own projects and get an impression of the business cases that exist.

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the second self-study unit, you will dive deep into the central phase of the data analysis process - the preparation of data and the creation of data models that are trained with the data.

• Exploratory data analysis and its forms of presentation
• Position parameters and scattering parameters
• Recognize linear and non-linear relationships
• Assessing the relevance of data
• Clean up and prepare data
• Modeling: Classification, regression and clustering

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.

In the third webinar, you will go through the last two phases of the CRIPS-DM process together with your trainers . You will learn how to evaluate data models, put them into production and monitor them continuously. Finally, the last questions will be answered.