Numerical Python: A Practical Techniques Approach for Industry, by Robert Johansson
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Numerical Python: A Practical Techniques Approach for Industry, by Robert Johansson
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Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more.
After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games.
Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.
Numerical Python: A Practical Techniques Approach for Industry, by Robert Johansson- Amazon Sales Rank: #211851 in Books
- Published on: 2015-10-02
- Released on: 2015-09-28
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.16" w x 7.00" l, .0 pounds
- Binding: Paperback
- 487 pages
From the Back Cover Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include:
- How to work with vectors and matrices using NumPy
About the Author Robert Johansson is a numerical Python expert, computational scientist. He has experience with SciPy, NumPy and works on QuTiP, an open-source python framework for simulating the dynamics of quantum systems.
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Most helpful customer reviews
0 of 0 people found the following review helpful. Practical indeed, very usable, and an interesting read beyond one's own domain. By Llewelyn In the last 50 years there are two things that have emerged in a technological world. First, applied mathematics has moved much more into numerical methods than in trying to solve problems analytically. The second thing that has emerged is that computing has both led and followed the numerical computing revolution. Python, amongst languages, is arguably a language with links to optimized code (such as C or Fortran) plus a language capable of a plethora of tasks, including scientific calculation, statistical modelling, network analysis, machine learning, language processing, and so forth. Johansson's book fits beautifully into a niche where serious science or other endeavour requires both some cookbook code and explanation of some basics. This book steps beautifully through from setting up to topics that will help a person with intermediate mathematical understanding and basic Python programming skill implement practical and useful code. There is a coding consistency that allows the user to add and modularise code blocks, if required. There is the support of code online. As a fairly critical consumer of literature purporting to be of practical industry use, my sense is that this book exceeds expectations.
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