This is the sixth volume in the Simula SpringerBriefs series and provides a compact and practical introduction to programming that quickly enables readers to start writing their own programs.
The Simula SpringerBriefs on Computing aims to provide introductions to select areas of research in computing. Each volume aims to provide an introduction to, and overview over, research fields that can otherwise be inaccessible. This is the sixth volume in this series.
Introduction to Scientific Programming with Python by Joakim Sundnes provides a practical introduction to the essential building blocks of programs for data-centric and computational applications. This book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming.
The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming, and provides a sound basis for further computer science and programming studies.
Introduction to Scientific Programming with Python is now available for download. As with all the Simula SpringerBriefs on Computing, this volume is open access under a CC BY 4.0 license and was published by SpringerOpen.
About the author
Joakim Sundnes is a Chief Research Scientist at Simula, and teaches computational modeling and introductory programming at the University of Oslo. He holds a PhD in Scientific Computing from the University of Oslo, and his research interests include mathematical modeling of physiology and biology, numerical methods for differential equations, and application of computational models in medicine.