But just how does the magic happen?In Black Hat Python, the latest from Justin Seitz (author of the best-selling Gray Hat Python), you'll explore the darker side of Python's capabilities writing network sniffers, manipulating packets, infecting virtual machines, creating stealthy trojans, and more. You'll learn how to:Create a trojan command-and-control using GitHubDetect sandboxing and automate common malware tasks, like keylogging and screenshottingEscalate Windows privileges with creative process controlUse offensive memory forensics tricks to retrieve password hashes and inject shellcode into a virtual machineExtend the popular Burp Suite web-hacking toolAbuse Windows COM automation to perform a man-in-the-browser attackExfiltrate data from a network most sneakilyInsider techniques and creative challenges throughout show you how to extend the hacks and how to write your own exploits.When it comes to offensive security, your ability to create powerful tools on the fly is indispensable. Learn how in Black Hat Python."

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    This book will help the C programmer reach new heights as a professional. Organized to make it easy for the reader to scan to sections that are relevant to their immediate needs.

    It contains sample programs to illustrate practical solutions to the type of real-world problems professional developers encounter.

    

    Master Python Programming with a unique Hands-On Project Have you always wanted to learn computer programming but are afraid it'll be too difficult for you? Or perhaps you know other programming languages but are interested in learning the Python language fast? This book is for you. You no longer have to waste your time and money learning Python from lengthy books, expensive online courses or complicated Python tutorials. What this book offers... Python for Beginners Complex concepts are broken down into simple steps to ensure that you can easily master the Python language even if you have never coded before. Carefully Chosen Python Examples Examples are carefully chosen to illustrate all concepts. In addition, the output for all examples are provided immediately so you do not have to wait till you have access to your computer to test the examples. Careful selection of topics Topics are carefully selected to give you a broad exposure to Python, while not overwhelming you with information overload. These topics include object-oriented programming concepts, error handling techniques, file handling techniques and more. Learn The Python Programming Language Fast Concepts are presented in a "to-the-point" style to cater to the busy individual. With this book, you can learn Python in just one day and start coding immediately. How is this book different... The best way to learn Python is by doing. This book includes a complete project at the end of the book that requires the application of all the concepts taught previously. Working through the project will not only give you an immense sense of achievement, it"ll also help you retain the knowledge and master the language. Are you ready to dip your toes into the exciting world of Python coding? This book is for you. With the first edition of this book being a #1 best-selling programming ebook on Amazon for more than a year, you can rest assured that this new and improved edition is the perfect book for you to learn the Python programming language fast. Click the BUY button and download it now. What you'll learn: - What is Python? - What software you need to code and run Python programs? - What are variables? - What are the common data types in Python? - What are Lists and Tuples? - How to format strings - How to accept user inputs and display outputs - How to control the flow of program with loops - How to handle errors and exceptions - What are functions and modules? - How to define your own functions and modules - How to work with external files - What are objects and classes - How to write your own class - What is inheritance - What are properties - What is name mangling .. and more... Finally, you'll be guided through a hands-on project that requires the application of all the topics covered. Click the BUY button and download the book now to start learning Python. Learn it fast and learn it well. Tags: ------------ Python, Object-oriented Python, Python course, Python book, learning Python, Python language, Python examples, Python tutorials, Python programming language, Python coding, Pyth

    If the idea of a computer science book without computers upsets you, please close your eyes until you’ve finished reading the rest of this page.The truth is that computer science is not really about the computer. It is just a tool to help you see ideas more clearly. You can see the moon and stars without a telescope, smell the flowers without a fluoroscope, have fun without a funoscope, and be silly sans oscilloscope.You can also play with computer science without... you-know-what. Ideas are the real stuff of computer science. This book is about those ideas, and how to find them.

    Now this classic book has been fully updated and revised with leading-edge practices--and hundreds of new code samples--illustrating the art and science of software construction. Capturing the body of knowledge available from research, academia, and everyday commercial practice, McConnell synthesizes the most effective techniques and must-know principles into clear, pragmatic guidance. No matter what your experience level, development environment, or project size, this book will inform and stimulate your thinking--and help you build the highest quality code. Discover the timeless techniques and strategies that help you: Design for minimum complexity and maximum creativity Reap the benefits of collaborative development Apply defensive programming techniques to reduce and flush out errors Exploit opportunities to refactor--or evolve--code, and do it safely Use construction practices that are right-weight for your project Debug problems quickly and effectively Resolve critical construction issues early and correctly Build quality into the beginning, middle, and end of your project

    Includes chapters on orbital mechanics, spacecraft construction, satellite-path radio wave propagation, modulation techniques, multiple access, and a detailed analysis of the communications link.

     In the Guide, you'll learn how to: -Use the Ubuntu command line. -Manage users, groups, and file permissions. -Install software on a Ubuntu system, both from the command line and the GUI. -Configure network settings. -Use the vi editor to edit system configuration files. -Install and configure a Samba server for file sharing. -Install SSH for remote system control using public key/private key encryption. -Install a DHCP server for IP address management. -Install a LAMP server. -Install web applications like WordPress and Drupal. -Configure an FTP server. -Manage ebooks. -Convert digital media. -Manage and configure Unity, the default Ubuntu environment. -Manage and halt processes from the command line. -Set up both a VNC server and a client. -Enjoy games on Ubuntu. -And many other topics.

    Significant numbers of poorly designed programs have been created by less-experienced developers, resulting in applications that are inefficient and hard to maintain and extend. Increasingly, software system professionals are discovering just how difficult it is to work with these inherited, non-optimal applications. For several years, expert-level object programmers have employed a growing collection of techniques to improve the structural integrity and performance of such existing software programs. Referred to as refactoring, these practices have remained in the domain of experts because no attempt has been made to transcribe the lore into a form that all developers could use... until now. In Refactoring: Improving the Design of Existing Software, renowned object technology mentor Martin Fowler breaks new ground, demystifying these master practices and demonstrating how software practitioners can realize the significant benefits of this new process.

    Billions of interconnected devices will be monitoring the environment, transportation systems, factories, farms, forests, utilities, soil and weather conditions, oceans and resources Many of these sensors and actuators will be networked into autonomous sets, with much of the information being exchanged machine-to-machine directly and without human involvement. Machine-to-machine communications are typically terse. Most sensors and actuators will report or act upon small pieces of information - chirps. Burdening these devices with current network protocol stacks is inefficient, unnecessary and unduly increases their cost of ownership. This must change. The architecture of the Internet of Things will entail a widely distributed topology incorporating simpler chirp protocols towards at the edges of the network. Rethinking the Internet of Things describes reasons why we must rethink current approaches to the Internet of Things. Appropriate architectures that will coexist with existing networking protocols are described in detail. An architecture comprised of integrator functions, propagator nodes, and end devices, along with their interactions, is explored. What you'll learn Teaches the difference between the "normal" Internet and the Internet of Things, Describes a new architecture and its components in the "chirp" context. Explains the shortcomings of IP for IoT. Describes the anatomy of the IoT. Re-frames key ideas such as reliability. Describes how to build the IoT Who this book is forThought leaders, executives, architectural, standards and development leaders in the evolving IoT industry

    This practical guide from renowned software architect Neal Ford helps you transition from a Java-writing imperative programmer to a functional programmer, using Java, Clojure, and Scala as examples.Rather than focus on specific language features, Functional Thinking looks at a variety of common practices in OOP languages and then shows you how to solve the same problems with a functional language. For instance, you know how to achieve code-reuse in Java via mechanisms such as inheritance and polymorphism. Code reuse is also possible in functional languages, using high-order functions, composition, and multi-methods.Ford encourages you to value results over steps, so you can begin to think like a functional programmer. Expect your mind to be bent, but you’ll finish with a much better understanding of both the syntax and semantics of functional languages.

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    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.