Book description
Implement stateoftheart techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack
About This Book
 Master the theory and algorithms behind numerical recipes and how they can be applied to realworld problems
 Learn to combine the most appropriate builtin functions from the SciPy stack by understanding the connection between the sources of your problem, volume of data, or computer architecture
 A comprehensive coverage of all the mathematical techniques needed to solve the presented topics, with a discussion of the relevant algorithms built in the SciPy stack
Who This Book Is For
If you are a mathematician, engineer, or computer scientist with a proficiency in Python and familiarity with IPython, this is the book for you. Some basic knowledge of numerical methods in scientific computing would be helpful.
What You Will Learn
 Master relevant algorithms used in symbolic or numerical mathematics to address approximation, interpolation, differentiation, integration, rootfinding, and optimization of scalar or multivariate functions
 Develop different algorithms and strategies to efficiently store and manipulate large matrices of data, in particular to solve systems of linear equations, or compute their eigenvalues/eigenvectors
 Understand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve them
 Perform statistical analysis, hypothesis test design and resolution, or data mining at a higher level, and apply them to reallife problems in the field of data analysis
 Gain insights on the power of distances, Delaunay triangulations and Voronoi diagrams for Computational Geometry, and apply them to various engineering problems
 Familiarize yourself with different techniques in signal/image processing, including filtering audio, images, or video to extract information, features, or remove components
In Detail
The SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells. This environment offers a cuttingedge platform for numerical computation, programming, visualization and publishing, and is used by some of the world's leading mathematicians, scientists, and engineers. It works on any operating system that supports Python and is very easy to install, and completely free of charge! It can effectively transform into a dataprocessing and systemprototyping environment, directly rivalling MATLAB and Octave.
This book goes beyond a mere description of the different builtin functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization. You will gain an insight into the best practices with numerical methods depending on the amount or type of data, properties of the mathematical tools employed, or computer architecture, among other factors.
The book kicks off with a concise exploration of the basics of numerical linear algebra and graph theory for the treatment of problems that handle large data sets or matrices. In the subsequent chapters, you will delve into the depths of algorithms in symbolic algebra and numerical analysis to address modeling/simulation of various realworld problems with functions (through interpolation, approximation, or creation of systems of differential equations), and extract their representing features (zeros, extrema, integration or differentiation).
Lastly, you will move on to advanced concepts of data analysis, image/signal processing, and computational geometry.
Style and approach
Packed with realworld examples, this book explores the mathematical techniques needed to solve the presented topics, and focuses on the algorithms built in the SciPy stack.
Publisher resources
Table of contents

Mastering SciPy
 Table of Contents
 Mastering SciPy
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Numerical Linear Algebra
 2. Interpolation and Approximation
 3. Differentiation and Integration
 4. Nonlinear Equations and Optimization
 5. Initial Value Problems for Ordinary Differential Equations
 6. Computational Geometry
 7. Descriptive Statistics
 8. Inference and Data Analysis
 9. Mathematical Imaging
 Index
Product information
 Title: Mastering SciPy
 Author(s):
 Release date: November 2015
 Publisher(s): Packt Publishing
 ISBN: 9781783984749
You might also like
book
Statistics for Machine Learning
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics …
book
SciPy Recipes
Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy About This …
book
Learn Python by Building Data Science Applications
Understand the constructs of the Python programming language and use them to build data science projects …
book
40 Algorithms Every Programmer Should Know
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental …