• PoC as a basis: What is a proof of concept? 
• The importance of hypotheses: How hypothesis-driven work creates structure and focus. 
• Data exploration: Identifying opportunities, risks, and potential at an early stage. 
• Role of the data structure: The significance of the existing data structure for the subsequent data product. 

• PoC as a basis: What is a proof of concept? 
• The importance of hypotheses: How hypothesis-driven work creates structure and focus. 
• Data exploration: Identifying opportunities, risks, and potential at an early stage. 
• Role of the data structure: The significance of the existing data structure for the subsequent data product. 

Statistics as a starting point: Using the basics correctly 

• Data at a glance: Initial orientation and key questions
• Basic concepts of mathematics: measures of location, measures of dispersion, distributions
• Normal distribution: significance and practical classification
• Visualization: Making data visible and comparable
• Systematic overview of the most important characteristics of the data

Data exploration: Recognizing the structure behind the data  

• Missing values: Types and technical significance
• Outliers & data problems: Distinguishing between outliers and measurement errors; role of subject matter expertise 
• Simple data transformations: fundamentals and relevance
• Documentation: Systematic recording of findings for the data project

Statistics as a starting point: Using the basics correctly 

• Data at a glance: Initial orientation and key questions
• Basic concepts of mathematics: measures of location, measures of dispersion, distributions
• Normal distribution: significance and practical classification
• Visualization: Making data visible and comparable
• Systematic overview of the most important characteristics of the data

Data exploration: Recognizing the structure behind the data  

• Missing values: Types and technical significance
• Outliers & data problems: Distinguishing between outliers and measurement errors; role of subject matter expertise 
• Simple data transformations: fundamentals and relevance
• Documentation: Systematic recording of findings for the data project

• Hypothesis work: Structure, logic, and preparation for hypothesis formation. 

• Hypothesis testing: basic concept, use of test cards and learning cards. 

• Reflection: Deriving new questions based on results. 

• Fundamentals of time series: significance in AI and overview of time series types. 

• Hypothesis work: Structure, logic, and preparation for hypothesis formation. 

• Hypothesis testing: basic concept, use of test cards and learning cards. 

• Reflection: Deriving new questions based on results. 

• Fundamentals of time series: significance in AI and overview of time series types. 

Time series: Discovering and interpreting patterns 

• Time series in practice: typical forms and patterns
• Exploration: Basic analysis methods and tests
• Practical examination: Recognizing structure, behavior, and special features
• Interpretation: Derive initial conclusions from time series. 

Data maturity, documentation – significance for the data product 

• Maturity model: Five stages of a data-driven company; determining the status of the data set
• Documentation: Exploration logbook and documentation according to CRISP-DM Phase 3
• Preparation for modeling: clarification of data status, open issues, risks, and assumptions

Time series: Discovering and interpreting patterns 

• Time series in practice: typical forms and patterns
• Exploration: Basic analysis methods and tests
• Practical examination: Recognizing structure, behavior, and special features
• Interpretation: Derive initial conclusions from time series. 

Data maturity, documentation – significance for the data product 

• Maturity model: Five stages of a data-driven company; determining the status of the data set
• Documentation: Exploration logbook and documentation according to CRISP-DM Phase 3
• Preparation for modeling: clarification of data status, open issues, risks, and assumptions

• Analysis and consolidation: clarification of the state of knowledge, identification of risks and relevant patterns. 
• Derivation for data preparation: Necessary cleanups, transformations, and feature requirements. 
• Transition to PoC: Criteria for "Fit for PoC" and go/no-go fundamentals. 

• Analysis and consolidation: clarification of the state of knowledge, identification of risks and relevant patterns. 
• Derivation for data preparation: Necessary cleanups, transformations, and feature requirements. 
• Transition to PoC: Criteria for "Fit for PoC" and go/no-go fundamentals.