08-30-2026, 10:10 PM
Data Modeling: From Physical Processes to Machine Learning
Book name:Data Modeling: From Physical Processes to Machine Learning
Author: Elmar Rueckert
Publication date: 1 August 2026 (eBook); 3 August 2026 (softcover)
Publisher: Springer Singapore
Edition: 1st edition
Pages: XIII + 114
ISBN: 978-981-95-7592-3 (eBook); 978-981-95-7591-6 (print)
Main areas: Data Science, Machine Learning, Information Theory, Artificial Intelligence, Data Analysis
Summary
Data Modeling is an introductory textbook that explains how real-world physical processes are converted into data and ultimately into predictive models. Rather than beginning immediately with machine-learning algorithms, Elmar Rueckert follows the complete data lifecycle: observing a physical phenomenon, deciding what and how frequently to measure, collecting measurements with sensors, representing and storing the resulting data, extracting information from it, analysing it, and finally using it in machine-learning systems. This makes the book particularly useful for understanding where machine-learning data actually comes from rather than treating datasets as ready-made objects.
The early chapters introduce the fundamentals of data modeling, followed by physical processes, data granularity, and sensors. An important theme is that measurements are only approximations of real processes: choices about sampling frequency, resolution, sensor accuracy, and representation determine what information can later be extracted. The book then moves to the concept of data itself and introduces information theory, providing a conceptual bridge between raw measurements and meaningful information. A substantial section is devoted to data analysis and to the methods used to explore and interpret collected datasets.
The final part introduces the foundations of machine learning, particularly how data must be organised before it can be used for learning. Rueckert then presents selected machine-learning applications, showing how predictive models emerge from the data-processing pipeline developed in the earlier chapters. The approach is deliberately accessible: Springer describes the book as intended for first-semester undergraduate students, and it is accompanied by interactive Jupyter Notebooks, fact sheets, programming tutorials, and practical exercises. Thus, it functions less as a comprehensive mathematical treatment of machine learning and more as a compact introduction connecting physical measurement, data science, information theory, and AI.
Main contents
The core progression of the book is:
Physical processes → Sensors → Measurements → Data → Information → Analysis → Data organization → Machine learning → Predictions
Its principal chapters cover Fundamentals of Data Modeling, Processes and Sensors, Data and Information Theory, and Machine Learning Fundamentals, including sections on data granularity, sensors, information theory, analysis, data organization, and selected ML applications.
Key takeaways
Springer book page
Book name:Data Modeling: From Physical Processes to Machine Learning
Author: Elmar Rueckert
Publication date: 1 August 2026 (eBook); 3 August 2026 (softcover)
Publisher: Springer Singapore
Edition: 1st edition
Pages: XIII + 114
ISBN: 978-981-95-7592-3 (eBook); 978-981-95-7591-6 (print)
Main areas: Data Science, Machine Learning, Information Theory, Artificial Intelligence, Data Analysis
Summary
Data Modeling is an introductory textbook that explains how real-world physical processes are converted into data and ultimately into predictive models. Rather than beginning immediately with machine-learning algorithms, Elmar Rueckert follows the complete data lifecycle: observing a physical phenomenon, deciding what and how frequently to measure, collecting measurements with sensors, representing and storing the resulting data, extracting information from it, analysing it, and finally using it in machine-learning systems. This makes the book particularly useful for understanding where machine-learning data actually comes from rather than treating datasets as ready-made objects.
The early chapters introduce the fundamentals of data modeling, followed by physical processes, data granularity, and sensors. An important theme is that measurements are only approximations of real processes: choices about sampling frequency, resolution, sensor accuracy, and representation determine what information can later be extracted. The book then moves to the concept of data itself and introduces information theory, providing a conceptual bridge between raw measurements and meaningful information. A substantial section is devoted to data analysis and to the methods used to explore and interpret collected datasets.
The final part introduces the foundations of machine learning, particularly how data must be organised before it can be used for learning. Rueckert then presents selected machine-learning applications, showing how predictive models emerge from the data-processing pipeline developed in the earlier chapters. The approach is deliberately accessible: Springer describes the book as intended for first-semester undergraduate students, and it is accompanied by interactive Jupyter Notebooks, fact sheets, programming tutorials, and practical exercises. Thus, it functions less as a comprehensive mathematical treatment of machine learning and more as a compact introduction connecting physical measurement, data science, information theory, and AI.
Main contents
The core progression of the book is:
Physical processes → Sensors → Measurements → Data → Information → Analysis → Data organization → Machine learning → Predictions
Its principal chapters cover Fundamentals of Data Modeling, Processes and Sensors, Data and Information Theory, and Machine Learning Fundamentals, including sections on data granularity, sensors, information theory, analysis, data organization, and selected ML applications.
Key takeaways
- Data modeling begins before machine learning: deciding what to measure and how to represent it strongly affects every later result.
- Sensors connect physical reality to computational data, making measurement and data granularity fundamental parts of ML systems.
- Information theory helps explain the difference between merely collecting data and extracting useful information from it.
- Good machine learning depends heavily on data organization, representation, and analysis, not only on the choice of algorithm.
- The book provides a concise, practical introduction to the entire pipeline from physical processes to predictive models, supported by Jupyter-based exercises.
Springer book page
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