Learning Scientific Programming with Python - Info and Reading Options
By Christian Hill

"Learning Scientific Programming with Python" was published by University of Cambridge ESOL Examinations in 2020 - Cambridge, it has 550 pages and the language of the book is English.
“Learning Scientific Programming with Python” Metadata:
- Title: ➤ Learning Scientific Programming with Python
- Author: Christian Hill
- Language: English
- Number of Pages: 550
- Publisher: ➤ University of Cambridge ESOL Examinations
- Publish Date: 2020
- Publish Location: Cambridge
“Learning Scientific Programming with Python” Subjects and Themes:
- Subjects: ➤ Science - Python (Computer program language) - Data processing - Mathematics - Science, data processing - Science, mathematics - SCIENCE / Mathematical Physics - Python (computer program language) - Sciences - Informatique - Mathématiques - Python (Langage de programmation) - SCIENCE - Mathematical Physics - Wissenschaftliches Rechnen - Python
Edition Identifiers:
- The Open Library ID: OL30175890M - OL20028518W
- ISBN-13: 9781108745918 - 9781108778039
- All ISBNs: 9781108745918 - 9781108778039
AI-generated Review of “Learning Scientific Programming with Python”:
"Learning Scientific Programming with Python" Description:
Open Data:
Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programmin
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