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Item #:
26270763
Learning Scientific Programming with Python
83% of respondents would recommend this to a friend
Item #:
26270763
FJD 409
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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.
Product Details
| 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?
-
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.
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Self-learners
Great for individuals pursuing self-study in coding who prefer structured learning with hands-on projects and examples.
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Advanced Developers
Not suitable for experienced programmers seeking advanced techniques or deep knowledge beyond the basics of Python.
Product Description
Learning Scientific Programming with Python
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Customer Questions & Answers
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Question:
Who is this textbook suitable for?
Answer: This textbook is suitable for students and researchers at all levels who want to gain proficiency in Python programming. -
Question:
What topics does this textbook cover?
Answer: This textbook covers general programming concepts, numerical programming, data visualization, and challenging topics like floating-point precision and algorithm stability. -
Question:
Are there any online resources accompanying this textbook?
Answer: Yes, there are extensive online resources available to support advanced study. -
Question:
Is this book suitable for someone with no previous programming experience?
Answer: Yes, the book covers the essentials of the Python language and provides examples and exercises for hands-on practice. -
Question:
Does this book cover machine learning and neural networks?
Answer: 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.
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Pros
- 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
Cons
- Not comprehensive in terms of all uses for Python
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FJD 409
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Features & Benefits
- 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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