Computing & Internet Computer Science Books

AWS Command Line Interface: User Guide

This is an official Amazon Web Services (AWS) guide for the AWS Command Line Interface (AWS CLI). You can download the Kindle version from this page, or find the online version and PDF at docs.aws.amazon.com/cli. The AWS CLI is an open source tool built using the AWS SDK for Python (Boto) that provides commands for interacting with AWS services. With minimal configuration, you can start using all of the functionality provided by the AWS Management Console from your favorite terminal program. This guide provides instructions for installing, configuring, and using the AWS CLI on Windows, macOS, and Linux. Learn how to use the AWS CLI to access the public API of any AWS service and write scripts to manage your AWS resources.

Network security

This 25-hour free course discussed network security and the intricacies of maintaining system resilience. It assumed an advanced knowledge of computing.

Introduction to cyber security: stay safe online

This 24-hour free course introduced online security: how to recognise threats and take steps to reduce the chances that they will occur.

What is DevOps?

Have we entered the age of NoOps infrastructures? Hardly. Old-style system administrators may be disappearing in the face of automation and cloud computing, but operations have become more significant than ever. As this O’Reilly Radar Report explains, we’re moving into a more complex arrangement known as "DevOps."Mike Loukides, O’Reilly’s VP of Content Strategy, provides an incisive look into this new world of operations, where IT specialists are becoming part of the development team. In an environment with thousands of servers, these specialists now write the code that maintains the infrastructure. Even applications that run in the cloud have to be resilient and fault tolerant, need to be monitored, and must adjust to huge swings in load. That was underscored by Amazon’s EBS outage last year.From the discussions at O’Reilly’s Velocity Conference, it’s evident that many operations specialists are quickly adapting to the DevOps reality. But as a whole, the industry has just scratched the surface. This report tells you why.

Data and Text Processing for Health and Life Sciences (Advances in Experimental Medicine and Biology Book 1137)

This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show how simple command line tools can be used and combined  to retrieve data and text from web resources, to filter and mine literature, and to explore the semantics encoded in biomedical ontologies.  To store data this book relies on open standard text file formats, such as TSV, CSV, XML, and OWL, that can be open by any text editor or spreadsheet application. The first two chapters, Introduction and Resources, provide a brief introduction to the shell scripting and describe popular data resources in Health and Life Sciences. The third chapter, Data Retrieval, starts by introducing a common data processing task that involves multiple data resources. Then, this chapter explains how to automate each step of that task by introducing the required commands line tools one by one. The fourth chapter, Text Processing, shows how to filter and analyze text by using simple string matching techniques and regular expressions. The last chapter, Semantic Processing, shows how XPath queries and shell scripting is able to process complex data, such as the graphs used to specify ontologies. Besides being almost immutable for more than four decades and being available in most of our personal computers, shell scripting is relatively easy to learn by Health and Life specialists as a sequence of independent commands. Comprehending them is like conducting a new laboratory protocol by testing and understanding its procedural steps and variables, and combining their intermediate results. Thus, this book is particularly relevant to Health and Life specialists or students that want to easily learn how to process data and text, and which in return may facilitate and inspire them to acquire deeper bioinformatics skills in the future.

Developing Graphics Frameworks with Python and OpenGL

Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds.You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.

HACKS TO CRUSH PLC PROGRAM FAST & EFFICIENTLY EVERYTIME... : CODING, SIMULATING & TESTING PROGRAMMABLE LOGIC CONTROLLER WITH EXAMPLES

★ Hacks To Crush PLC Programs From Beginning. Start Designing, Building, Simulating and Testing Programs in IEC Languange (This book guides only on LD (Ladder Diagram)★This book will get you crushing PLC-HMI programming environment as well as familiarize you with (LD) ladder logic programming.You'll gain a deeper understanding of the LD programming and the editing interface, the practical methods used to build a PLC program, and how to . We also cover the basics of ladder logic programming that every beginner should know, and provide ample practical examples to help you gain a better understanding. By the end of this book you will be able to create a PLC-HMI program from start to finish, that can take on any real-world task.If you know how to write & test the PLC-HMI codes then you're on your way to work on any PLC environment.

Programming for Computations - Python: A Gentle Introduction to Numerical Simulations with Python 3.6 (Texts in Computational Science and Engineering Book 15)

This book is published open access under a CC BY 4.0 license. This book presents computer programming as a key method for solving mathematical problems. This second edition of the well-received book has been extensively revised: All code is now written in Python version 3.6 (no longer version 2.7). In addition, the two first chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout the book, the explanations provided are now more detailed, previous examples have been modified, and new sections, examples and exercises have been added. Also, a number of small errors have been corrected. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style employed is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows students to write simple programs for solving common mathematical problems with numerical methods in the context of engineering and science courses. The emphasis is on generic algorithms, clean program design, the use of functions, and automatic tests for verification.

Introduction to Scientific Programming with Python (Simula SpringerBriefs on Computing Book 6)

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.