Learning Scientific Programming with Python
Learning Scientific Programming with Python
Learning Scientific Programming with Python
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Learning Scientific Programming with Python
Learning Scientific Programming with Python
Learning Scientific Programming with Python
Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering.
Learning Scientific Programming with Python
Artikulli #: 26270763

Learning Scientific Programming with Python

83% of respondents would recommend this to a friend

Artikulli #: 26270763

€ 151

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Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering.
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What Stands Out

Hands-On Learning
Engage in practical projects that reinforce programming concepts, making the learning process interactive and effective for beginners and advanced learners alike.
Comprehensive Resources
Access a wealth of materials, including exercises, examples, and expert insights, to deepen your understanding and enhance your programming skills in scientific applications.
Real-World Applications
Apply Python programming to real scientific problems, bridging the gap between theory and practice, and preparing users for challenges in academic and research settings.

Detajet e produktit

Discover the power of scientific programming using Python with the 1st edition book. Buy now from Ubuy Kosovo to enhance your coding skills and explore the world of science and technology.
  • 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 quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming.
Publisher Cambridge University Press
Publication date March 29, 2016
Edition 1st
Language English
Print length 458 pages
ISBN-10 110742822X
ISBN-13 978-1107428225
Item Weight 1.98 pounds (900 grams)
Dimensions 6.85 x 1.04 x 9.72 inches (17.4 x 2.6 x 24.7 cm)

Who Should Buy?

Suitable For
  • Beginner Programmers

    Ideal for those new to programming, providing foundational concepts and practical applications in scientific contexts.

  • STEM Students

    Suitable for students in science, technology, engineering, and mathematics looking to enhance their programming skills with Python.

  • Self-learners

    Great for individuals pursuing self-study in coding who prefer structured learning with hands-on projects and examples.

Not Suitable For
  • Advanced Developers

    Not suitable for experienced programmers seeking advanced techniques or deep knowledge beyond the basics of Python.

PËRSHKRIMI I PRODUKTIT

Keni ndonjë pyetje? Bisedoni me ne

Pyetjet dhe Përgjigjet e Klientit

  • Pyetje: Who is this textbook suitable for?

    Përgjigje: This textbook is suitable for students and researchers at all levels who want to gain proficiency in Python programming.
  • Pyetje: What topics does this textbook cover?

    Përgjigje: This textbook covers general programming concepts, numerical programming, data visualization, and challenging topics like floating-point precision and algorithm stability.
  • Pyetje: Are there any online resources accompanying this textbook?

    Përgjigje: Yes, there are extensive online resources available to support advanced study.
  • Pyetje: Is this book suitable for someone with no previous programming experience?

    Përgjigje: Yes, the book covers the essentials of the Python language and provides examples and exercises for hands-on practice.
  • Pyetje: Does this book cover machine learning and neural networks?

    Përgjigje: No, this book focuses on scientific programming and does not cover machine learning and neural networks.

Introductory & Beginning Editorial Review

Learning Scientific Programming with Python 1st Edition is a highly recommended resource for individuals in the sciences who want to learn Python in the context of scientific applications. The book teaches the essentials of the Python language and covers key libraries such as Numpy, Matplotlib, and SciPy. It provides plenty of examples and exercises, and includes solutions for the exercises. The book is not comprehensive in terms of all possible uses for Python, but it is complete in its coverage of scientific programming. The choice of examples and problems is outstanding and the price is reasonable.

Customer Reviews & Ratings

85 vlerësimet nga klientët
  • 5 yll
    91%
  • 4 yll
    5%
  • 3 yll
    0%
  • 2 yll
    4%
  • 1 yll
    0%

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Ndaji mendimet me klientë të tjerë

Pro

  • Teaches the essentials of Python, including key libraries
  • Plenty of examples and exercises with solutions
  • Focuses on the application of Python to scientific programming
  • Reasonably priced

Kundër

  • Not comprehensive in terms of all uses for Python

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€ 151

Porosit tani dhe merr dërgesën afërsisht Sunday, Tetor 18

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Karakteristikat dhe Përfitimet

  • Master basic programming tasks from scratch.
  • Real-life scientifically relevant examples and solutions.
  • Suitable for students and researchers at all levels.
  • Introduction to Python programming and advanced concepts in one complete volume.
  • Discusses numerical programming and data visualization with libraries like NumPy, SciPy, and Matplotlib.
  • Includes a chapter on challenging topics like floating-point precision and algorithm stability.

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