Cambridge International AS and A Level Computer Science offers a complete set of resources to accompany the 9608 syllabus. This revision guide helps students to prepare and practice skills for the Cambridge AS and A Level Computer Science examination. It contains clear explanations and key information to support learners, with additional practice questions to help students feel confident and reinforce their understanding of key concepts.

Wireless Sensor Networks have a wide range of applications in different areas. Their main constraint is the limited and irreplaceable power source of the sensor nodes. In many applications, energy conservation of the sensor nodes and their replacement or replenishment due to the hostile nature of the environment is the most challenging issue. Energy efficient clustering and routing are the two main important topics studied extensively for this purpose. This book focuses on the energy efficient clustering and routing with a great emphasis on the evolutionary approaches. It provides a comprehensive and systematic introduction of the fundamentals of WSNs, major issues and effective solutions.

Influence action through data! This is not a book. It is a one-of-a-kind immersive learning experience through which you can become--or teach others to be--a powerful data storyteller. Let's practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data's foundational lessons, Let's practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes: Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact The lessons and exercises found within this comprehensive guide will empower you to master--or develop in others--data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!

Unlike any other programming language, Haskell is purely functional with a strong type system and lazy evaluation. It is arguable the most interesting language but also has the reputation of being one of the most challenging to learn. Learning Haskell doesn't have to be difficult, and this book can help! Get Programming with Haskell introduces you to the Haskell language without drowning you in academic jargon and heavy functional programming theory. By working through 43 easy-to-follow lessons, you'll learn Haskell by doing Haskell. This book starts with first concepts, building your knowledge with concrete examples and exercises. You'll learn to think the Haskell way, as you start to understand the language and how to use it effectively. And you'll really appreciate the crystal-clear illustrations, quick-checks, and open-ended tasks that make sure you're solid on each new concept before you move along! KEY FEATURES * Full of interesting examples * Easy approach to functional programming basics * Write safe, predictable code with fewer bugs * Lots of illustrations and exercises Readers should have some experience with JavaScript, Python or Ruby. No functional programming or math skills required. ABOUT THE TECHNOLOGY Haskell is a purely functional programming language. It treats programs as much like math as you can, requires you to think more than you type, and can express the idea of infinite lists in code! These are just the more obvious features that make Haskell unlike any other programming language AUTHOR BIO Will Kurt currently works as a Data Scientist at Quick Sprout. With a formal background in both Computer Science (MS) and English Literature (BA) he is fascinated with explaining complex technical topics as clearly and generally as possible. He has taught a course section on Haskell at the University of Nevada, Reno and given workshops on Functional Programming. He also blogs about probability at CountBayesie.com.

Web 2.0 provides dynamic and comprehensive coverage of the most current information available on Web 2.0 today. You will gain a solid understanding of the current trends in technology and concepts associated with interactive information sharing and new web applications. You will gain knowledge of web-based communities, social-networking, video and filing sharing sites as well as blogging, wikis and more.

More physicists today are taking on the role of software developer as part of their research, but software development isn't always easy or obvious, even for physicists. This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field. Written by two PhDs in nuclear engineering, this book includes practical examples drawn from a working knowledge of physics concepts. You'll learn how to use the Python programming language to perform everything from collecting and analyzing data to building software and publishing your results.In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your findings, and collaborate efficiently; dive into software licenses, ownership, and copyright procedures

Optimize Designs in Less Time An essential element of equipment and system design, computer aided design (CAD) is commonly used to simulate potential engineering problems in order to help gauge the magnitude of their effects. Useful for producing 3D models or drawings with the selection of predefined objects, Computer Aided Design: A Conceptual Approach directs readers on how to effectively use CAD to enhance the process and produce faster designs with greater accuracy. Learn CAD Quickly and Efficiently This handy guide provides practical examples based on different CAD systems, and incorporates automation, mechanism, and customization guidelines, as well as other outputs of CAD in the design process. It explains the mathematical tools used in related operations and covers general topics relevant to any CAD program. Comprised of 12 chapters, this instructional reference addresses: Automation concepts and examples Mechanism design concepts Tie reduction through customization Practical industrial component and system design Reduce Time by Effectively Using CAD Computer Aided Design: A Conceptual Approach concentrates on concept generation, functions as a tutorial for learning any CAD software, and was written with mechanical engineering professionals and post-graduate engineering students in mind.

An Invitation to Computational Homotopy is an introduction to elementary algebraic topology for those with an interest in computers and computer programming. It expertly illustrates how the basics of the subject can be implemented on a computer through its focus on fully-worked examples designed to develop problem solving techniques. The transition from basic theory to practical computation raises a range of non-trivial algorithmic issues which will appeal to readers already familiar with basic theory and who are interested in developing computational aspects. The book covers a subset of standard introductory material on fundamental groups, covering spaces, homology, cohomology and classifying spaces as well as some less standard material on crossed modules. These topics are covered in a way that hints at potential applications of topology in areas of computer science and engineering outside the usual territory of pure mathematics, and also in a way that demonstrates how computers can be used to perform explicit calculations within the domain of pure algebraic topology itself. The initial chapters include in-depth examples from data mining, biology and digital image analysis, while the later chapters cover a range of computational examples on the cohomology of classifying spaces that are likely beyond the reach of a purely paper-and-pen approach to the subject. An Invitation to Computational Homotopy serves as a self-contained and informal introduction to these topics and their implementation in the sphere of computer science. Written in a dynamic and engaging style, it skilfully showcases a range of useful machine computations, and will serve as an invaluable aid to graduate students working with algebraic topology.

This book provides the foundations of both linear and nonlinear analysis necessary for understanding and working in twenty-first century applied and computational mathematics. In addition to the standard topics, this text includes several key concepts of modern applied mathematical analysis that should be, but are not typically, included in advanced undergraduate and beginning graduate mathematics curricula. This material is the introductory foundation upon which algorithm analysis, optimization, probability, statistics, differential equations, machine learning, and control theory are built. When used in concert with the free supplemental lab materials, this text teaches students both the theory and the computational practice of modern mathematical analysis. Foundations of Applied Mathematics, Volume 1: Mathematical Analysis includes several key topics not usually treated in courses at this level, such as uniform contraction mappings, the continuous linear extension theorem, Daniell-Lebesgue integration, resolvents, spectral resolution theory, and pseudospectra. Ideas are developed in a mathematically rigorous way and students are provided with powerful tools and beautiful ideas that yield a number of nice proofs, all of which contribute to a deep understanding of advanced analysis and linear algebra. Carefully thought out exercises and examples are built on each other to reinforce and retain concepts and ideas and to achieve greater depth. Associated lab materials are available that expose students to applications and numerical computation and reinforce the theoretical ideas taught in the text. The text and labs combine to make students technically proficient and to answer the age-old question, ""When am I going to use this?

Many things around us have properties that depend on their shape - for example, the drag characteristics of a rigid body in a flow. This self-contained overview of differential geometry explains how to differentiate a function (in the calculus sense) with respect to a "shape variable". This approach, which is useful for understanding mathematical models containing geometric partial differential equations (PDEs), allows readers to obtain formulas for geometric quantities (such as curvature) that are clearer than those usually offered in differential geometry texts. Readers will learn how to compute sensitivities with respect to geometry by developing basic calculus tools on surfaces and combining them with the calculus of variations. Several applications that utilize shape derivatives and many illustrations that help build intuition are included.

Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.

This textbook is about computer science. 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. This textbook has three key features: A strong focus on problem solving introduces students to the fundamental data structures and algorithms by providing a very readable text without introducing an overwhelming amount of new language syntax. Algorithm analysis in terms of Big-O running time is introduced early and applied throughout. Python is used to facilitate the success of beginning students in using and mastering data structures and algorithms.

Exam board: AQA Level: A-level Subject: Computer Science First teaching: September 2016 First exams: Summer 2017 (AS); Summer 2018 (A-level) Covering what you really need to know for AQA A-level Computer Science- in just 120 pages- this revision guide makes revision easy - whether you're getting started early or you need to do some last-minute cramming. - Find key facts at your fingertips with quick summaries of the content, concepts and terms from the AQA A-level Computer Science specification - Get better grades in your exams with tips on exam technique, mistakes to avoid and important things to remember - Revise and practise using end-of-topic questions and synoptic questions at the end of each section - Benefit from the knowledge of experienced teacher and Master Teacher for CAS, Stuart Davison

If you are new to programming with Microsoft Access VBA and are looking for a solid introduction, this is the book for you. Developed by computer science professors, books in the for the absolute beginner series teach the principles of programming through simple game creation. Covering Access 2007, Microsoft Access VBA Programming for the Absolute Beginner focuses on VBA programming, but also covers beginning database concepts for those who lack that cursory knowledge of relational databases and/or Microsoft Access. Topics range from beginning SQL concepts, user interface upgrades, new data types, essential programming constructs, and much more. The book not only shows you how to apply the concepts learned to real-world Access 2007 scenarios, but by the time you finish the book, you'll be able to apply the basic principles learned to the next programming language you tackle.

This book introduces readers to novel, efficient and user-friendly software tools for power systems studies, to issues related to distributed and dispersed power generation, and to the correlation between renewable power generation and electricity demand. Discussing new methodologies for addressing grid stability and control problems, it also examines issues concerning the safety and protection of transmission and distribution networks, energy storage and power quality, and the application of embedded systems to these networks. Lastly, the book sheds light on the implications of these new methodologies and developments for the economics of the power industry. As such, it offers readers a comprehensive overview of state-of-the-art research on modern electricity transmission and distribution networks.

What exactly is a cloud-native platform? It's certainly a hot topic in IT, as enterprises today assess this option for developing and delivering software quickly and repeatedly. This O'Reilly report explains the capabilities of cloud-native platforms and examines the fundamental changes enterprises need to make in process, organization, and culture if they're to take real advantage of this approach. Author Duncan Winn focuses on the open source platform Cloud Foundry, one of the more prominent cloud-native providers. You'll learn how cloud-native applications are designed to be "infrastructure unaware" so they can thrive and move at will in the highly distributed and constantly evolving cloud environment.With this report, you'll explore: Technical driving forces that are rapidly changing the way organizations develop and deliver software today How key concepts underpinning the Cloud Foundry platform leverage each of the technical forces discussed How cloud-native platforms remove the requirement to perform undifferentiated heavy lifting, such as provisioning VMs, middleware, and databases Why cloud-native platforms enable fast feedback loops as you move from agile development to agile deployment Recommended changes and practical considerations for organizations that want to build cloud-native applications.

Absolute clarity is the aim with a new generation of revision guide for the 2020s. This guide has been expertly compiled and edited by successful former teachers of Computer Science, highly experienced examiners and a good dollop of scientific research into what makes revision most effective. Past examinations questions are essential to good preparation, improving understanding and confidence. This guide has combined revision with tips and more practice questions than you could shake a stick at. All the essential ingredients for getting a grade you can be really proud of. Each specification topic has been referenced and distilled into the key points to make in an examination for top marks. Questions on all topics assessing knowledge, application and analysis are all specifically and carefully devised throughout this book.

Students are guided through the latest trends in computer concepts and technology in an exciting and easy-to-follow format. Updated for currency, ENHANCED DISCOVERING COMPUTERS, INTRODUCTORY: YOUR INTERACTIVE GUIDE TO THE DIGITAL WORLD, International Edition provides readers with the most up-to-date information on the latest technology in today's digital world.

Building on his highly successful textbook on C++, David Yevick provides a concise yet comprehensive one-stop course in three key programming languages, C++, Java and Octave (a freeware alternative to MATLAB). Employing only public-domain software, this book presents a unique overview of numerical and programming techniques, including object-oriented programming, elementary and advanced topics in numerical analysis, physical system modelling, scientific graphics, software engineering and performance issues. Compact, transparent code in all three programming languages is applied to the fundamental equations of quantum mechanics, electromagnetics, mechanics and statistical mechanics. Uncommented versions of the code that can be immediately modified and adapted are provided online for the more involved programs. This compact, practical text is an invaluable introduction for students in all undergraduate- and graduate-level courses in the physical sciences or engineering that require numerical modelling, and also a key reference for instructors and scientific programmers.

This book explains how the biological systems and their functions are driven by genetic information stored in the DNA, and their expression driven by different factors. The soft computing approach recognizes the different patterns in DNA sequence and try to assign the biological relevance with available information.The book also focuses on using the soft-computing approach to predict protein-protein interactions, gene expression and networks. The insights from these studies can be used in metagenomic data analysis and predicting artificial neural networks.

Although numerical approximation and statistical inference are traditionally covered as entirely separate subjects, they are intimately connected through the common purpose of making estimations with partial information. This book explores these connections from a game and decision theoretic perspective, showing how they constitute a pathway to developing simple and general methods for solving fundamental problems in both areas. It illustrates these interplays by addressing problems related to numerical homogenization, operator adapted wavelets, fast solvers, and Gaussian processes. This perspective reveals much of their essential anatomy and greatly facilitates advances in these areas, thereby appearing to establish a general principle for guiding the process of scientific discovery. This book is designed for graduate students, researchers, and engineers in mathematics, applied mathematics, and computer science, and particularly researchers interested in drawing on and developing this interface between approximation, inference, and learning.