My Review on Book "Scientific Computing with Python Second Edition" by Packt Publication

This is an era of digital transformation and artificial intelligence, where every domain uses scientific methods and programming to achieve research experiments. Scientific computing is widely used to perform any experimental, observational study. Python is in demand for its ever-increasing and widely used programming language in research.

Today, I am sharing this book with you that can help you build these abilities with fundamentals, and you will be able to learn how to develop the scientific formula in programming with python.?

Book Name: Scientific Computing with Python Second Edition by Claus?Fuhrer,?Oliver Verdier, Jan Erik?Solem

?????This book is commendable for those aspirants, Scientists, researchers, Programmers who want to understand to explore python programming for Scientific Research.

Official Link : https://www.packtpub.com/in/data/scientific-computing-with-python-second-edition

Overview:

This book is incredible for any individual who want to begin learning python and develop their ability to executing the scientific algorithms with Python programming. This book assists you to composing everything distinctly for instance you would like to transform any mathematical formulation into Programming. This book follows step by step manner with best practices such as functional programming, Object oriented programming, Advance python technique. It also offers computational framework knowledge (SciPy, Pandas, Numpy) which are promptly utilized for data analysis, data science, developers.

What I Like :

• Explain all the basics very clearly and in detail.
• Every example I have come across while reading this book is working perfectly with mathematical mapping.
• Described framework with metrics and higher-order tensor examples with step-by-step algebra operations that I like the most which are widely used in data science algorithms.
• Error handling is very core to any object-oriented programming and functional programming, with various error conditions and custom error handling.
• Mathematical variable declaration, its types and how to get them with functional programming.
• SciPy, Pandas, Matplotlib, NumPy, SymPy Usage All the complex examples explained in a simple and clear way that make this book unique to me.
• Testing code that works for the defined formula.
• Parallel computing module mpi4py is very new to me but in my research this framework uses distributed memory which increases the speed of programming.

What I do not like:

This is well written for the beginner and for Industry person. I only want to drive author attention toward the terminology mathematical terms. Terminology is bit difficult to understand for normal user.

What I would like to see:

I would like to see more advance mathematical modeling and it's conversion on the future edition of this book.

I must recommend this book for every beginner, teacher, industry expert, data scientist, data analyst to buy because python related all stuff i enjoyed while reading this book.

Happy learning….!!!