Judgement
Evaluate observations critically
Data and technical outputs are not equated with reality. Assumptions, interpretations, uncertainty and possible reasoning errors are made visible.
Common subject framework
From observations, data and models to sustainable software
The four titles were not published as a continuing book series. They are independent technical books, but they share a clear thematic connection: each examines a different stage or perspective of responsible technical problem-solving.
The common path begins with the critical examination of observations and assumptions. It continues through data quality, analysis and modelling to decisions, requirements, software architecture, implementation, testing and operation.
Problem and reality
Clarify observations, terms, goals and context.
Data and requirements
Examine quality, provenance, assumptions and constraints.
Models and alternatives
Compare predictions, metrics, risks and options.
Decision
Weigh uncertainty, trade-offs and consequences.
Technical implementation
Design architecture, code and tests for long-term use.
Operation and responsibility
Secure maintainability, monitoring and human accountability.
Relevance for study and professional practice
Why these four perspectives belong together
Technical projects do not fail only because knowledge about algorithms or programming languages is missing. Difficulties often arise earlier: unclear questions, unchecked assumptions, inadequate data, unsuitable metrics, conflicting requirements or solutions that are selected too early.
Data practice
Move from data to dependable insight
Problem definition, data quality, analysis, reproducibility, communication and practical use are treated as one connected process.
Model evaluation
Do not confuse predictions with decisions
Models support decisions. They do not replace expert judgement or the evaluation of error costs, fairness, explainability and consequences.
Software quality
Turn code into operable solutions
Working code is only one part of a solution. Requirements, architecture, testing, maintainability and operation determine whether it remains viable over time.
The four technical books
Data Science and Machine Learning with Python, and Software Engineering with Java
Each book has its own focus and can be read independently. Together, the four titles provide a broader view of problem understanding, data, models, decisions, software development and responsibility.
Critical Thinking · Data Science · Python
Critical Thinking in Data Science with Python
From Observation to Responsible Decision-Making
German edition title: Kritisches Denken in Data Science mit Python
This book develops the epistemological and argumentative foundations of critical Data Science. It examines observations, assumptions, hypotheses, uncertainty, evidence, argumentation and responsible decisions.
- Distinguish observation from interpretation
- Make assumptions, bias and uncertainty visible
- Evaluate arguments, evidence and conclusions
Data Science · Python
From Data to Solutions in Data Science with Python
From Problem to Responsible Solution
German edition title: Von Daten zu Lösungen in Data Science mit Python
This book applies critical thinking to the complete Data Science process: from problem definition and data quality to analysis, modelling, deployment, monitoring and communication.
- Clarify problems and stakeholder requirements
- Examine data quality and data provenance
- Turn reproducible analyses into practical solutions
Machine Learning · Python
From Models to Decisions in Machine Learning with Python
From Model to Responsible Decision-Making
German edition title: Von Modellen zu Entscheidungen in Machine Learning mit Python
This book focuses on the transition from Machine Learning models to human decisions. Predictions are assessed in relation to metrics, uncertainty, error costs, fairness, robustness and explainability.
- Compare models with meaningful baselines
- Evaluate metrics and error costs in context
- Consider fairness, robustness and explainability
Software Engineering · Java
From Code to Solutions in Software Engineering with Java
From Problem to Responsible Solution
German edition title: Von Code zu Lösungen im Software Engineering mit Java
This book transfers the problem-solving and responsibility-oriented approach to Software Engineering. Requirements, architecture, implementation, testing, operation and maintainability are treated as connected decisions.
- Analyse requirements in a traceable way
- Justify architectural decisions
- Develop testable, maintainable and operable Java code
Intended readers
Who these technical books are relevant for
Study and entry level
Students and independent learners
For readers who want to connect technical foundations with critical thinking, problem understanding and responsible use rather than learning isolated tools.
Data practice
Data analysts and data scientists
For projects in which data quality, model performance, uncertainty, reproducibility and communication must be considered together.
Machine Learning
Machine Learning practitioners and decision-makers
For the responsible interpretation of predictions, metrics, error costs, fairness, explainability and human accountability.
Software development
Developers and software architects
For translating business problems and requirements into testable, maintainable and sustainably operable software systems.
Education
Lecturers, teachers and trainers
As a basis for courses, seminars, exercises, workshops and discussions about technology, quality and responsibility.
Project responsibility
Project managers and subject-matter owners
For the structured clarification of goals, stakeholders, alternatives, risks, requirements and the long-term consequences of technical decisions.
The books teach general ways of thinking and working. They do not replace domain-specific analysis, legal review, security assessment or concrete project planning.
Shared competencies
What readers can develop across the four books
Problem understanding
Distinguish symptoms, causes, goals, constraints and stakeholder interests.
Critical data competence
Examine data provenance, quality, representativeness, uncertainty and possible distortion.
Model judgement
Understand models as simplifications and avoid equating their outputs with reality or decisions.
Comparison of alternatives
Compare baselines, non-technical options, models and architectures using transparent criteria.
Software quality
Treat requirements, architecture, testing, maintainability and operation as connected parts of a viable solution.
Responsibility
Justify decisions, communicate uncertainty and consider the effects of technical systems on affected people.
Frequently asked questions
Questions about the four technical books
Are the books an officially published series?
No. The four titles were published as independent technical books. They are, however, closely related in content because they examine different perspectives on the same underlying process of responsible technical problem-solving.
In which order can the books be read?
Each book can be read independently. For a continuous learning path, a useful order is critical thinking, Data Science, Machine Learning and Software Engineering.
Who are the books intended for?
The books are intended for students, learners, educators and practitioners in Data Science, Machine Learning and software development, as well as project owners who need to assess technical results and use them responsibly.
Which programming languages are used?
The books on Data Science and Machine Learning use Python. The book on Software Engineering uses Java.
How do these books differ from purely programming-focused books?
The focus is not limited to syntax and tools. The books also address problem definition, data and requirements quality, assumptions, model evaluation, alternatives, communication, operation and human responsibility.
Do all four books have to be read?
No. Readers can select the title that matches their main area of interest. The common framework becomes especially clear when several books are combined.
Questions about books, workshops and teaching
For workshops, seminars, courses, professional discussions or questions about individual books, you can get in touch by phone or email. Content, target audience, duration and fees are agreed in advance.
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