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An Introduction to Python and Computer Programming

An Introduction to Python and Computer Programming

by Yue Zhang
308 Pages · 2015 · 5.01 MB · 2,279 Downloads · New!
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" Happiness doesn't result from what we get, but from what we give. ” ― Ben Carson
A Primer on Scientific Programming with Python, 4th edition
by Hans Petter Langtangen
872 Pages · 2014 · 7.15 MB · 1,675 Downloads · New!
The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches “Matlab-style” and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.
A Student’s Guide to Python for Physical Modeling
by Jesse M. Kinder
160 Pages · 2015 · 4.91 MB · 2,045 Downloads · New!
Python is a computer programming language that is rapidly gaining popularity throughout the sciences. A Student’s Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed.
Advanced Analytics in Power BI with R and Python
by Ryan Wade
437 Pages · 2020 · 6.9 MB · 3,435 Downloads · New!
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services.
Advanced Data Analytics Using Python
by Sayan Mukhopadhyay
186 Pages · 2018 · 2.1 MB · 2,992 Downloads · New!
Advanced Python Development
by Matthew Wilkes
627 Pages · 2020 · 7.9 MB · 2,624 Downloads · New!
This book builds on basic Python tutorials to explain various Python language features that aren’t routinely covered: from reusable console scripts that play double duty as micro-services by leveraging entry points, to using asyncio efficiently to collate data from a large number of sources. Along the way, it covers type-hint based linting, low-overhead testing and other automated quality checking to demonstrate a robust real-world development process.
An Introduction to Statistics with Python
by Thomas Haslwanter
278 Pages · 2016 · 4.7 MB · 2,643 Downloads · New!
This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.