Stay in the know, spot trends as they happen, and push your business to new limits. This e-book introduces Microsoft Power BI basics through a practical, scenario-based guided tour of the tool, showing you how to build analytical solutions using Power BI. Get an overview of Power BI, or dig deeper and follow along on your PC using the book's examples.

    It presents in a unified manner an introduction to the mathematical theory underlying computer graphic applications. It covers topics of keen interest to students in engineering and computer science: transformations, projections, 2-D and 3-D curve definition schemes, and surface definitions. It also includes techniques, such as B-splines, which are incorporated as part of the software in advanced engineering workstations. A basic knowledge of vector and matrix algebra and calculus is required.

    This book covers a range of genres, including action, adventure, and puzzle games complete with professional quality sound effects and visuals. It discusses game design theory and features practical examples of how this can be applied to making games that are more fun to play. Game Maker allows games to be created using a simple drag-and-drop interface, so you don't need to have any prior coding experience. It includes an optional programming language for adding advanced features to your games, when you feel ready to do so. You can obtain more information by visiting book.gamemaker.nl. The authors include the creator of the Game Maker tool and a former professional game programmer, so you'll glean understanding from their expertise. The book also includes a DVD containing Game Maker software and all of the game projects that are created in the book—plus a host of professional-quality graphics and sound effects that you can use in your own games.

    Its easy-to-read treatment offers an intuitive approach, featuring informal discussions followed by thematically arranged exercises. Intended for undergraduate courses in abstract algebra, it is suitable for junior- and senior-level math majors and future math teachers. This second edition features additional exercises to improve student familiarity with applications. An introductory chapter traces concepts of abstract algebra from their historical roots. Succeeding chapters avoid the conventional format of definition-theorem-proof-corollary-example; instead, they take the form of a discussion with students, focusing on explanations and offering motivation. Each chapter rests upon a central theme, usually a specific application or use. The author provides elementary background as needed and discusses standard topics in their usual order. He introduces many advanced and peripheral subjects in the plentiful exercises, which are accompanied by ample instruction and commentary and offer a wide range of experiences to students at different levels of ability.

    Widely accepted as the authoritative text and reference on electronic circuit design, both analog and digital, this book revolutionized the teaching of electronics by emphasizing the methods actually used by circuit designers -- a combination of some basic laws, rules of thumb, and a large bag of tricks. The result is a largely nonmathematical treatment that encourages circuit intuition, brainstorming, and simplified calculations of circuit values and performance. The new Art of Electronics retains the feeling of informality and easy access that helped make the first edition so successful and popular. It is an ideal first textbook on electronics for scientists and engineers and an indispensable reference for anyone, professional or amateur, who works with electronic circuits.

    With adoptions at over 225 colleges, the first edition was one of the leading C textbooks of the last ten years. The second edition maintains all the book's popular features and brings it up to date with coverage of the C99 standard. The new edition also adds a significant number of exercises and longer programming projects, and includes extensive revisions and updates.

    Introduction to Modern Cryptography provides a rigorous yet accessible treatment of modern cryptography, with a focus on formal definitions, precise assumptions, and rigorous proofs.The authors introduce the core principles of modern cryptography, including the modern, computational approach to security that overcomes the limitations of perfect secrecy. An extensive treatment of private-key encryption and message authentication follows. The authors also illustrate design principles for block ciphers, such as the Data Encryption Standard (DES) and the Advanced Encryption Standard (AES), and present provably secure constructions of block ciphers from lower-level primitives. The second half of the book focuses on public-key cryptography, beginning with a self-contained introduction to the number theory needed to understand the RSA, Diffie-Hellman, El Gamal, and other cryptosystems. After exploring public-key encryption and digital signatures, the book concludes with a discussion of the random oracle model and its applications.Serving as a textbook, a reference, or for self-study, Introduction to Modern Cryptography presents the necessary tools to fully understand this fascinating subject.

    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.

    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.

    Haskell emerged in the last decade as a standard for lazy functional programming, a programming style where arguments are evaluated only when the value is actually needed. Haskell is a marvellous demonstration tool for logic and maths because its functional character allows implementations to remain very close to the concepts that get implemented, while the laziness permits smooth handling of infinite data structures.This book does not assume the reader to have previous experience with either programming or construction of formal proofs, but acquaintance with mathematical notation, at the level of secondary school mathematics is presumed. Everything one needs to know about mathematical reasoning or programming is explained as we go along. After proper digestion of the material in this book the reader will be able to write interesting programs, reason about their correctness, and document them in a clear fashion. The reader will also have learned how to set up mathematical proofs in a structured way, and how to read and digest mathematical proofs written by others.

    The blades start moving in 60 seconds. What do you do? If you want to work at Google, or any of America's best companies, you need to have an answer to this and other puzzling questions. Are You Smart Enough to Work at Google? guides readers through the surprising solutions to dozens of the most challenging interview questions. The book covers the importance of creative thinking, ways to get a leg up on the competition, what your Facebook page says about you, and much more. Are You Smart Enough to Work at Google? is a must-read for anyone who wants to succeed in today's job market.

    You will be able to look over the shoulder of major coding and design experts to see problems through their eyes.This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to break rules. Beautiful Code is an opportunity for master coders to tell their story. All author royalties will be donated to Amnesty International.

    While the mathematics is there, the effort is not all concentrated on proofs. Strang's emphasis is on understanding. He explains concepts, rather than deduces. This book is written in an informal and personal style and teaches real mathematics. The gears change in Chapter 2 as students reach the introduction of vector spaces. Throughout the book, the theory is motivated and reinforced by genuine applications, allowing pure mathematicians to teach applied mathematics.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    Starting with the fundamentals, it describes the most advanced features of the most advanced language: Common Lisp. Only the top percentile of programmers use lisp and if you can understand this book you are in the top percentile of lisp programmers. If you are looking for a dry coding manual that re-hashes common-sense techniques in whatever langue du jour, this book is not for you. This book is about pushing the boundaries of what we know about programming. While this book teaches useful skills that can help solve your programming problems today and now, it has also been designed to be entertaining and inspiring. If you have ever wondered what lisp or even programming itself is really about, this is the book you have been looking for.