Numerical and Scientific Computing with SciPy
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# Numerical and Scientific Computing with SciPy

Master the capabilties of SciPy and put them to use to solve your numeric and scientific computing problems
4.5 (2 ratings)
9 students enrolled
Created by Packt Publishing
Last updated 6/2017
English
Current price: \$10 Original price: \$125 Discount: 92% off
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Includes:
• 3.5 hours on-demand video
• 1 Supplemental Resource
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Get to know the benefits of using the combination of the Python SciPy Stack (NumPy, Scipy, and Matplotlib) as a programming environment for technical and scientific purposes
• The use of the SciPy Stack in general applications of Engineering and scientific numerical problem solving.
• The use of the SciPy Stack for solving fundamental basic Machine Learning models.
• Create and manipulate Numpy array objects to perform numerical computations fast and efficiently.
• Use of the Scipy library to compute eigenvalues and eigenvectors and apply it to Principal Component Analysis
• Make use of the SciPy Stack to collect, organize, analyze, and interpret data.
• Analize linear and non-linear regression problems via gradient descent.
View Curriculum
Requirements
• The course does not assume any previous experience with SciPy, although it assumes a strong working knowledge of Python at the instrumental level. In addition, familiarity with probability theory, linear algebra and statistics is required.
Description

The SciPy Stack is a collection of Open-Source Python libraries finding their application in many areas of technical and scientific computing. It builds on the capabilities of the NumPy array object for faster computations, and contains modules and libraries for linear algebra, signal and image processing, visualization, and much more. Accordingly, gaining a solid working knowledge on some of the basic functionality of the SciPy Stack to solve mathematical models numerically is clearly the first step before one can start using it to tackle large-scale computational projects either in the industry or in the academic world.

This practical course begins with an introduction to the Python SciPy Stack and a coverage of its basic usage cases. You will then delve right into the different functionalities offered by the main modules comprising the SciPy Stack (Numpy, Scipy, and Matplotlib) and see the basics on how they can be implemented in real-life scenarios. You will see how you can make the most of the algorithms in the SciPy Stack to solve problems in linear algebra, numerical analysis, visualization, and much more, including some practical examples drawn from the field of Machine Learning. By the end of this course, you will have all the knowledge you need to take your understanding of the SciPy Stack to a new level altogether, and tackle the trickiest problems in numerical and scientific computational programming with ease and confidence.

Sergio Rojas is currently a Full Professor of Physics at the Universidad Simón Bolívar, Venezuela. Regarding his formal studies, he earned in 1991 a B.S in Physics with Thesis on Numerical Relativity from the Universidad de Oriente, Estado Sucre, Venezuela, and then, in 1998, he earned a Ph.D. in Physics from the Physics Department of the City College of the City University of New York, where he worked on the applications of Fluid Dynamics in the flow of fluids in porous media, gaining and developing since then a vast experience in programming as an aid to scientific research via fortran77/90 and C/C++. In 2001, he also earned a Master’s degree in computational finance from The Oregon Graduate Institute of Science and Technology.

Sergio’s teaching activities involve lecturing undergraduate and graduated physics courses at his home university, Universidad Simón Bolívar, Venezuela, including a course on Monte Carlo Methods and other on Computational Finance. His research interests include physics education research, fluid flow in porous media, and the application of the theory of complex systems and statistical mechanics in Financial Engineering. More recently, Sergio has been involved in Machine Learning and its applications in Science and Engineering via the Python programming language.

Sergio’s is Co-author of the book Learning SciPy for Numerical and Scientific Computing - Second Edition (2015) and of the self-published book (in Spanish) Aprendiendo a programar en Python con mi computador: Primeros pasos rumbo a cómputos de gran escala en las Ciencias e Ingenierías, (2016).

Who is the target audience?
• If you are a data scientist, a programmer or an self-motivated student in any field who wants to learn and master the intricacies of Python SciPy Stack (Numpy, Scipy, and Matplotlib) to build solutions for your numeric and scientific computational problems, this is the video course for you.
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