This text is user-focused and has been highly updated including topics, pictures and examples. The Williams text contains less theory and more application to engage students who might be more familiar with technology. Continually published and updated for over 15 years, Using Information Technology was the first text to foresee and define the impact of digital convergence--the fusion of computers and communications. It was also the first text to acknowledge the new priorities imposed by the Internet and World Wide Web and bring discussion of them from late in the course to the beginning. Today, it is directed toward the "Always On" generation that is at ease with digital technology--comfortable with iPhones, MySpace, Facebook, Twitter, Wikipedia, and the blogosphere--but not always savvy about its processes, possibilities, and liabilities. This 8th edition continues to address the two most significant challenges that instructors face in teaching this course: -Trying to make the course interesting and challenging, and -Trying to teach to students with a variety of computer backgrounds. In addition, this text correlates with Simnet Online for full integration of resources within the Computing Concepts course.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

    But if one per year is good, how about Seven Languages in Seven Weeks? In this book you'll get a hands-on tour of Clojure, Haskell, Io, Prolog, Scala, Erlang, and Ruby. Whether or not your favorite language is on that list, you'll broaden your perspective of programming by examining these languages side-by-side. You'll learn something new from each, and best of all, you'll learn how to learn a language quickly. Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them. Among this group of seven, you'll explore the most critical programming models of our time. Learn the dynamic typing that makes Ruby, Python, and Perl so flexible and compelling. Understand the underlying prototype system that's at the heart of JavaScript. See how pattern matching in Prolog shaped the development of Scala and Erlang. Discover how pure functional programming in Haskell is different from the Lisp family of languages, including Clojure. Explore the concurrency techniques that are quickly becoming the backbone of a new generation of Internet applications. Find out how to use Erlang's let-it-crash philosophy for building fault-tolerant systems. Understand the actor model that drives concurrency design in Io and Scala. Learn how Clojure uses versioning to solve some of the most difficult concurrency problems. It's all here, all in one place. Use the concepts from one language to find creative solutions in another-or discover a language that may become one of your favorites.

    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

    You'll learn how to install, configure, operate, manage, and secure the latest release.Covers all the new features and capabilities of the much-anticipated new release of VMware vSphere Discusses the planning, installation, operation, and management for the latest release Reviews migration to the latest vSphere software Offers hands-on instruction and clear explanations with real-world examples Mastering VMware vSphere is the strategic guide you need to maximize the opportunities of virtualization.

    Even though most engineers don't think much about them, this short book shows you why logs are worthy of your attention.Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses--data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models.Go ahead and take the plunge with logs; you're going love them.Learn how logs are used for programmatic access in databases and distributed systemsDiscover solutions to the huge data integration problem when more data of more varieties meet more systemsUnderstand why logs are at the heart of real-time stream processingLearn the role of a log in the internals of online data systemsExplore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn

    His aim is to present calculus as the first real encounter with mathematics: it is the place to learn how logical reasoning combined with fundamental concepts can be developed into a rigorous mathematical theory rather than a bunch of tools and techniques learned by rote. Since analysis is a subject students traditionally find difficult to grasp, Spivak provides leisurely explanations, a profusion of examples, a wide range of exercises and plenty of illustrations in an easy-going approach that enlightens difficult concepts and rewards effort. Calculus will continue to be regarded as a modern classic, ideal for honours students and mathematics majors, who seek an alternative to doorstop textbooks on calculus, and the more formidable introductions to real analysis.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    Now, security expert Brian Carrier has written the definitive reference for everyone who wants to understand and be able to testify about how file system analysis is performed. Carrier begins with an overview of investigation and computer foundations and then gives an authoritative, comprehensive, and illustrated overview of contemporary volume and file systems: Crucial information for discovering hidden evidence, recovering deleted data, and validating your tools. Along the way, he describes data structures, analyzes example disk images, provides advanced investigation scenarios, and uses today's most valuable open source file system analysis tools--including tools he personally developed. Coverage includes Preserving the digital crime scene and duplicating hard disks for dead analysis Identifying hidden data on a disk's Host Protected Area (HPA) Reading source data: Direct versus BIOS access, dead versus live acquisition, error handling, and more Analyzing DOS, Apple, and GPT partitions; BSD disk labels; and Sun Volume Table of Contents using key concepts, data structures, and specific techniques Analyzing the contents of multiple disk volumes, such as RAID and disk spanning Analyzing FAT, NTFS, Ext2, Ext3, UFS1, and UFS2 file systems using key concepts, data structures, and specific techniques Finding evidence: File metadata, recovery of deleted files, data hiding locations, and more Using The Sleuth Kit (TSK), Autopsy Forensic Browser, and related open source tools When it comes to file system analysis, no other book offers this much detail or expertise. Whether you're a digital forensics specialist, incident response team member, law enforcement officer, corporate security specialist, or auditor, this book will become an indispensable resource for forensic investigations, no matter what analysis tools you use.

    Though an increasing number of criminals are using computers and computer networks, few investigators are well-versed in the evidentiary, technical, and legal issues related to digital evidence. As a result, digital evidence is often overlooked, collected incorrectly, and analyzed ineffectively. The aim of this hands-on resource is to educate students and professionals in the law enforcement, forensic science, computer security, and legal communities about digital evidence and computer crime. This work explains how computers and networks function, how they can be involved in crimes, and how they can be used as a source of evidence. As well as gaining a practical understanding of how computers and networks function and how they can be used as evidence of a crime, readers will learn about relevant legal issues and will be introduced to deductive criminal profiling, a systematic approach to focusing an investigation and understanding criminal motivations. Readers will receive access to the author's accompanying Web site which contains simulated cases that integrate many of the topics covered in the text. Frequently updated, these cases teaching individuals about: • Components of computer networks • Use of computer networks in an investigation • Abuse of computer networks • Privacy and security issues on computer networks • The law as it applies to computer networks• Provides a thorough explanation of how computers and networks function, how they can be involved in crimes, and how they can be used as a source of evidence • Offers readers information about relevant legal issues • Features coverage of the abuse of computer networks and privacy and security issues on computer networks• Free unlimited access to author's Web site which includes numerous and frequently updated case examples

    The authors, who developed the metaobject protocol andwho were among the group that developed CLOS, introduce this new approach toprogramming language design, describe its evolution and design principles, andpresent a formal specification of a metaobject protocol for CLOS.Kiczales, desRivi?res, and Bobrow show that the "art of metaobject protocol design" lies increating a synthetic combination of object-oriented and reflective techniques thatcan be applied under existing software engineering considerations to yield a newapproach to programming language design that meets a broad set of designcriteria.One of the major benefits of including the metaobject protocol inprogramming languages is that it allows users to adjust the language to better suittheir needs. Metaobject protocols also disprove the adage that adding moreflexibility to a programming language reduces its performance. In presenting theprinciples of metaobject protocols, the authors work with actual code for asimplified implementation of CLOS and its metaobject protocol, providing anopportunity for the reader to gain hands-on experience with the design process. Theyalso include a number of exercises that address important concerns and openissues.Gregor Kiczales and Jim des Rivi?res, are Members of the Research Staff, andDaniel Bobrow is a Research Fellow, in the System Sciences Laboratory at Xerox PaloAlto Research Center.

    Git tasks displayed on two-page spreads provide all the context you need, without the extra fluff. Get up to speed on Git right now with Pragmatic Guide to Git. Task-oriented two-page spreads get you up and running with minimal fuss. Each left-hand page dives into the underlying implementation for each task. The right-hand page contains commands that focus on the task at hand, and cross references to other tasks that are related. You'll find what you need fast. Git is rapidly becoming the de-facto standard for the open source community. Its excellent merging capabilities, coupled with its speed and relative ease of use, make it an indispensable tool for any developer. New Git users will learn the basic tasks needed to work with Git every day, including working with remote repositories, dealing with branches and tags, exploring the history, and fixing problems when things go wrong. If you're already familiar with Git, this book will be your go-to reference for Git commands and best practices. You won't find a more practical approach to learning Git than Pragmatic Guide to Git.

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.