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Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino, Second Edition

Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino, Second Edition

by Charles Bell
742 Pages · 2020 · 14.5 MB · 2,887 Downloads · New!
" 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 Python and Computer Programming
by Yue Zhang
308 Pages · 2015 · 5.01 MB · 2,279 Downloads · New!
This book introduces Python programming language and fundamental concepts in algorithms and computing. Its target audience includes students and engineers with little or no background in programming, who need to master a practical programming language and learn the basic thinking in computer science/programming. The main contents come from lecture notes for engineering students from all disciplines, and has received high ratings. Its materials and ordering have been adjusted repeatedly according to classroom reception. Compared to alternative textbooks in the market, this book introduces the underlying Python implementation of number, string, list, tuple, dict, function, class, instance and module objects in a consistent and easy-to-understand way, making assignment, function definition, function call, mutability and binding environments understandable inside-out. By giving the abstraction of implementation mechanisms, this book builds a solid understanding of the Python programming language.