Back See overview
Course
2

Booking no:

42577

Fundamentals for data-driven products

This course teaches the fundamental skills required to understand data thoroughly and use it to make sound decisions for a proof of concept (PoC). You will learn how to systematically explore data, formulate hypotheses, analyze time series, and document findings in such a way that they can be directly transferred to the next project phase.

20 hours over 4 weeks
approx. 20 hours
Online
German
Junior, Professional and Master Class

Date preview

Start date
Last module
Availability
Location
2.11.2026
30.11.2026
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
1.2.2027
1.3.2027
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
3.5.2027
28.5.2027
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
6.9.2027
1.10.2027
Places free
Maximum planning security
Implementation already secured
Hook on!
Next booking secures the
implementation
Live-Online
Book course individually

Module overview

The following module overview shows dates for the course start on
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Module
1

Think in PoCs: Data Understanding

  • 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. 
Webinar
3 h

Monday, November 2, 2026
, 9:00 a.m. – 12:00 p.m.

61461553
Module
1

Think in PoCs: Data Understanding

  • 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. 
Webinar
3 h

Monday, February 1, 2027
, 9:00 a.m. – 12:00 p.m.

61461528
Module
1

Think in PoCs: Data Understanding

  • 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. 
Webinar
3 h

Monday, May 3, 2027
9:00 a.m. – 12:00 p.m.

61480575
Module
1

Think in PoCs: Data Understanding

  • 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. 
Webinar
3 h

Monday, September 6, 2027,
, 9:00 a.m. – 12:00 p.m.

61480571
Module
2

Self-study phase 1

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
Self-study phase
6 h
61461553
Module
2

Self-study phase 1

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
Self-study phase
6 h
61461528
Module
2

Self-study phase 1

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
Self-study phase
6 h
61480575
Module
2

Self-study phase 1

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
Self-study phase
6 h
61480571
Module
3

From assumptions to knowledge: hypotheses, tests, and early evidence

  • 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. 

 

 

Webinar
4 h

Monday, November 16, 2026
9:00 a.m. - 1:00 p.m.

61461553
Module
3

From assumptions to knowledge: hypotheses, tests, and early evidence

  • 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. 

 

 

Webinar
4 h

Monday, February 15, 2027
9:00 a.m. - 1:00 p.m.

61461528
Module
3

From assumptions to knowledge: hypotheses, tests, and early evidence

  • 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. 

 

 

Webinar
4 h

Friday, May 14, 2027
8:00 a.m. – 12:00 p.m.

61480575
Module
3

From assumptions to knowledge: hypotheses, tests, and early evidence

  • 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. 

 

 

Webinar
4 h

Monday, September 20, 2027,
, 8:00 a.m. – 12:00 p.m.

61480571
Module
4

Self-study phase 2

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
Self-study phase
6 h
61461553
Module
4

Self-study phase 2

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
Self-study phase
6 h
61461528
Module
4

Self-study phase 2

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
Self-study phase
6 h
61480575
Module
4

Self-study phase 2

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
Self-study phase
6 h
61480571
Module
5

From understanding to decision: Ready for the PoC?

  • 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. 
Webinar
3 h

Monday, November 30, 2026
9:00 a.m. - 12:00 p.m.

61461553
Module
5

From understanding to decision: Ready for the PoC?

  • 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. 
Webinar
3 h

Monday, March 1, 2027
, 9:00 a.m. – 12:00 p.m.

61461528
Module
5

From understanding to decision: Ready for the PoC?

  • 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. 
Webinar
3 h

Friday, May 28, 2027
9:00 a.m. – 12:00 p.m.

61480575
Module
5

From understanding to decision: Ready for the PoC?

  • 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. 
Webinar
3 h

Friday, October 1, 2027,
, 9:00 a.m. – 12:00 p.m.

61480571

Course overview

Contents and course schedule

1. Understand data before making decisions 

  • What a PoC really is – and what role data understanding plays 
  • How hypotheses bring structure, focus, and speed to data projects 
  • The importance of data structures for subsequent modeling 

2. Apply statistical principles confidently 

  • Location measures, dispersion, distributions, and normal distribution 
  • Visual exploration as the starting point for every analysis 

3. Explore data and identify typical challenges 

  • Understanding missing values and classifying them correctly 
  • Distinguishing between outliers and measurement errors 
  • Relevant transformations and documentation of findings 

4. From assumptions to real knowledge 

  • Develop and test hypotheses logically 
  • Using Test Cards & Learning Cards 
  • Reflect critically on results 

5. Analyzing time series in a practical manner 

  • Recognizing patterns, understanding trends and seasonality 
  • Use simple tests and visual exploration 
  • Derive initial interpretations for PoCs 

6. Document findings professionally 

  • Determining the maturity level of a data set 
  • Set up an exploration logbook 
  • Clearly identify risks, assumptions, and open issues 

7. Create clarity for the PoC 

  • Consolidation of all findings 
  • Identification of opportunities, risks, and hypotheses 
  • Prepare go/no-go decisions carefully 

This is how you learn in this course

This course offers a digital blended learning approach designed for working professionals. Through a flexible combination of online seminars and self-study sessions, you’ll be sure to achieve your goals. Here’s what you’ll learn in this training program:

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

Self-study phases: Learn independently, at your own pace and whenever you want. Our courses offer you didactically high-quality learning material. 

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.

Learning Community:A digital learning community will be available to you throughout the course. trainers with other participants and the trainers , and ask any questions you may have.

Certificate of Completion and Open Badge:As a graduate of the class, you will receive a certificate of completion and an open badge, which you can easily share on professional networks (such as LinkedIn).

Back See overview