Christopher Hadnagy and Michele Fincher address the growing scourge of phishing emails, and provides actionable defensive techniques and tools to help you steer clear of malicious emails. Phishing is analyzed from the viewpoint of human decision–making and the impact of deliberate influence and manipulation on the recipient. Using detailed examples, the authors provide insight into the financial, corporate espionage, nation state, identity the goals of the attackers, and teach you how to spot a spoofed e–mail or cloned website.

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Described in its subtitle as "Practical Programming for Total Beginners" this is a book for non-programmers. In it Al Sweigart shows how to perform tasks that take hours to do by hand, such as renaming files or updating spreadsheet cells, with Python programs. Step-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks. As the blurb puts it, don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work.

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Quite soon, the world’s information infrastructure is going to reach a level of scale and complexity that will force us to approach it in an entirely new way. The familiar notions of command and control are being thwarted by realities of a faster, denser world of communication where choice, variety, and indeterminism rule. The myth of the machine that does exactly what we tell it has come to an end. Mark Burgess focuses on the impact of computers and information on our modern infrastructure going from the roots of science to the principles behind system operation and design. To shape the future of technology, we need to understand how it works—or else what we don’t understand will end up shaping us.

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This step-by-step tutorial from Krishna Sankar and Holden Karau is for software developers who want to learn how to write distributed programs with Spark. In it you will develop a machine learning system with Spark's MLlib and scalable algorithms and deploy Spark jobs to various clusters such as Mesos, EC2, Chef, YARN, EMR, and so on. No previous experience with distributed programming is necessary. However it assumes knowledge of either Java, Scala, or Python.

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A practical guide to securing all mobile applications by approaching the issue from a hacker′s point of view. Dominic Chell, Tyrone Erasmus, Shaun Colley and Ollie Whitehouse provide expert guidance toward discovering and exploiting flaws in mobile applications on the iOS, Android, Blackberry, and Windows Phone platforms. You will learn a proven methodology for approaching mobile application assessments, and the techniques used to prevent, disrupt, and remediate the various types of attacks. Coverage includes data storage, cryptography, transport layers, data leakage, injection attacks, runtime manipulation, security controls, and cross–platform apps, with vulnerabilities highlighted and detailed information on the methods hackers use to get around standard security. 

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch with Python. If you have an aptitude for mathematics and some programming skills, Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with provides a crash course in Python to implement them. 

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Five senior IBM architects show how to use information-centric views to give data a central role in project design and delivery. Using Common Information Models (CIM), readers will learn how to standardize the way they represent information, making it easier to design, deploy, and evolve even the most complex systems. Using a complete case study, the authors explain what CIMs are, how to build them, and how to maintain them, as well as how to clarify the structure, meaning, and intent of any information that is exchanged.

Requirements engineering tasks have become increasingly complex. In order to ensure a high level of knowledge and competency among requirements engineers, the International Requirements Engineering Board (IREB) has developed standardized qualifications. Designed for self-study, this second edition is aligned with Version 2.2 of the Certified Professional for Requirements Engineering Foundation Level exam. 

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Go is an open-source language from Google that's a bit like C. Designed for programmer productivity, it's got a clean syntax, and emphasizes concurrency. Mal Curtis outlines the basic concepts - language structures, the standard library, and Go tools - then tackles more advanced features like concurrency concepts, testing methodologies, and package structures. At each step, you'll get advice for better coding in Go as well as hints and tips gleaned from real world experience of developing web applications with Go.

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GNU make is the most widely used build automation tool, but it can be intimidating for new users and its terse language can be tough to parse for even experienced programmers. Those who run into difficulties face a long, involved struggle, often leaving unsolved problems behind and GNU make's vast potential untapped. John Graham-Cumming demystifies GNU make and shows you how to use its best features. You'll find a fast, thorough rundown of the basics of variables, rules, targets, and makefiles. Learn how to fix wastefully long build times and other common problems, and gain insight into more advanced capabilities, such as complex pattern rules.

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With the subtitle "Essential Techniques for Predictive Analysis", Michael Bowles shows you how to analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice.

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With the subtitle "42 Specific Ways to Improve Your Use of C++11 and C++14" Scott Myers follows the guideline-based, example-driven format of his earlier books to help C++ developers use the new features of C++11 and C++14 -  auto type declarations, move semantics, lambda expressions, and concurrency support - effectively so that software is correct, efficient, maintainable, and portable. Myers shows how best practices in "old" C++ programming (i.e., C++98) require revision for software development in modern C++

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