Computer Science Portfolio Examples

  computer science portfolio examples: Code Complete Steve McConnell, 2004-06-09 Widely considered one of the best practical guides to programming, Steve McConnell’s original CODE COMPLETE has been helping developers write better software for more than a decade. 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
  computer science portfolio examples: Grokking Deep Learning Andrew W. Trask, 2019-01-23 Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
  computer science portfolio examples: Writing for Computer Science Justin Zobel, 2004-06-03 A complete update to a classic, respected resource Invaluable reference, supplying a comprehensive overview on how to undertake and present research
  computer science portfolio examples: Necromancer Awakening Nat Russo, 2016-05-28 Knowledge in the absence of wisdom is a dangerous thing. Texas archaeology student Nicolas Murray has an ironic fear of the dead. A latent power connecting him to an ancient order of Necromancers floods his mind with impossible images of battle among hive-mind predators and philosopher fishmen. When a funeral service leaves him shaken and questioning his sanity, the insidious power strands him in a land where the sky kills and earthquakes level cities. A land where the undead serve the living, and Necromancers summon warriors from ancient graves to fight in a war that spans life and afterlife. If Nicolas masters the Three Laws of Necromancy, he can use them to get home. But as he learns to raise and purify the dead-a process that makes him relive entire lifetimes in the span of a moment-the very power that could bring him home may also prevent his return. For the supreme religious leader, the Archmage Kagan, has outlawed Necromancy, and its practitioners risk torture and execution. As warring nations hunt Necromancers to extinction, countless dead in limbo await a purification that may never come. Nicolas's power could be his way home... Or it could save a world that wants him dead.
  computer science portfolio examples: Multicriteria Portfolio Construction with Python Elissaios Sarmas, Panos Xidonas, Haris Doukas, 2020-10-17 This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub. This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters. A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered. In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions. The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.
  computer science portfolio examples: Careers in Computer Science and Programming Jeri Freedman, 2011-01-15 Presents the different computer science and programming careers available today. It provides practical advice on obtaining each of these careers, including educational requirements and necessary training.
  computer science portfolio examples: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.
  computer science portfolio examples: Digital Portfolio Construction Dale Fitch, Mary Ruffolo, Michael J. Austin, 2019-10-15 Digital Portfolio Construction: A Guide for Showcasing Social Work Skills guides students through the process of compiling a digital portfolio--a collection of artifacts that demonstrates the knowledge, skills, and competencies they have mastered and articulates all they have learned throughout their social work program. The first part of the text focuses on constructing a digital portfolio, which includes the processes of gathering, selecting, reflecting, and sharing assignments and artifacts. In the later chapters, readers gain a greater understanding of the connection between what they learn within their courses and the social work competencies that are the hallmark of the profession. Recognizing that the construction and presentation of a digital portfolio requires active engagement and collaboration between students, faculty, and administration, two valuable appendices provide teaching suggestions for social work instructors and describe the programmatic and administrative contexts necessary to support the successful compilation of a digital portfolio and its use as a program outcome measure. Developed to help future practitioners increase their competence and confidence in presenting their knowledge and skills, Digital Portfolio Construction is a guide that can be integrated within social work curriculum and programs, or leveraged as a tool for independent study. Watch author Dale K. Fitch introduce Digital Portfolio Construction and speak to the how the text can help students recognize and reflect upon the competencies they acquire as they progress through their social work program. Watch Samantha Brown, M.S.W. speak to the importance of social work portfolios and how building a portfolio has been instrumental in her job search and professional networking.
  computer science portfolio examples: Experimentation in Software Engineering Claes Wohlin, Per Runeson, Martin Höst, Magnus C. Ohlsson, Björn Regnell, Anders Wesslén, 2012-06-16 Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors’ book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a “cookbook” when evaluating new methods or techniques before implementing them in their organization.
  computer science portfolio examples: Processing for Visual Artists Andrew Glassner, 2011-09-27 Walk with veteran author Andrew Glassner; see exactly how each of his pieces evolves, including the mistakes he's made along the way (and how to fix them!), and the times when he changed direction. As your knowledge and skills grow, you'll understand why Processing is such a powerful tool for self-expression. It offers a 21st-century medium for expressing new ideas. This book gives you everything you need to know to explore new frontiers in your own images, animations, and interactive experiences.
  computer science portfolio examples: Guide to Teaching Computer Science Orit Hazzan, Tami Lapidot, Noa Ragonis, 2011-04-23 This guide presents both a conceptual framework and detailed implementation guidelines for general computer science (CS) teaching. The content is clearly written and structured to be applicable to all levels of CS education and for any teaching organization, without limiting its focus to instruction for any specific curriculum, programming language or paradigm. Features: presents an overview of research in CS education; examines strategies for teaching problem-solving, evaluating pupils, and for dealing with pupils’ misunderstandings; provides learning activities throughout the book; proposes active-learning-based classroom teaching methods, as well as methods specifically for lab-based teaching; discusses various types of questions that a CS instructor, tutor, or trainer can use for a range of different teaching situations; investigates thoroughly issues of lesson planning and course design; describes frameworks by which prospective CS teachers gain their first teaching experience.
  computer science portfolio examples: BASICS OF QUANTUM COMPUTING Dr. Anand Kumar Pandey, Mrs. Rashmi Pandey, 2023-04-27 A computer is a piece of hardware that aids in the processing of information by carrying out preprogrammed instructions. One definition of an algorithm is a procedure that is well-defined and has a finite description for realizing an information-processing task. There is always the possibility of converting an information processing job into a physical one. Working with an idealized computer model may be highly beneficial and is often even necessary when developing sophisticated algorithms and protocols for a variety of information processing jobs. Nevertheless, it is essential to keep in mind the connection between computing and physics whenever one is researching the actual constraints imposed by a piece of computer equipment, particularly when doing so for some kind of useful purpose. The idealized computing model fails to account for the fact that actual computing equipment is embedded in a broader and often more complex physical environment than is shown in the model. Quantum information processing refers to the application of what we learn about the physical world from quantum theory to the objective of carrying out activities that were previously regarded to be either impossible or infeasible. This type of processing was thought to be either impossible or infeasible. Because of this, things that were before thought to be impossible or impossible to achieve are now within reach. Quantum computers are defined as any computers that have the capability of processing quantum information. In this book, we investigate how quantum computers can be used to execute specific tasks more quickly than conventional computers can, as well as how they may be used safely even when errors are likely in the work they are performing. In addition, we analyze the benefits that quantum computers have over conventional computers in terms of the ability to resolve certain categories of problems. The next chapters will expand upon the basis provided in this first chapter by presenting more complex subjects in computation theory and quantum physics. This will be done by building upon the foundation laid in this chapter.
  computer science portfolio examples: Exploring Computer Science with Scheme Oliver Grillmeyer, 2013-04-17 A presentation of the central and basic concepts, techniques, and tools of computer science, with the emphasis on presenting a problem-solving approach and on providing a survey of all of the most important topics covered in degree programmes. Scheme is used throughout as the programming language and the author stresses a functional programming approach to create simple functions so as to obtain the desired programming goal. Such simple functions are easily tested individually, which greatly helps in producing programs that work correctly first time. Throughout, the author aids to writing programs, and makes liberal use of boxes with Mistakes to Avoid. Programming examples include: * abstracting a problem; * creating pseudo code as an intermediate solution; * top-down and bottom-up design; * building procedural and data abstractions; * writing progams in modules which are easily testable. Numerous exercises help readers test their understanding of the material and develop ideas in greater depth, making this an ideal first course for all students coming to computer science for the first time.
  computer science portfolio examples: Effective Python Brett Slatkin, 2015 Effective Python will help students harness the full power of Python to write exceptionally robust, efficient, maintainable, and well-performing code. Utilizing the concise, scenario-driven style pioneered in Scott Meyers's best-selling Effective C++, Brett Slatkin brings together 53 Python best practices, tips, shortcuts, and realistic code examples from expert programmers. Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples.
  computer science portfolio examples: Developing Portfolios in Education Ruth S. Johnson, J. Sabrina Mims-Cox, Adelaide Doyle-Nichols, 2009-07-21 Developing Portfolios in Education: A Guide to Reflection, Inquiry, and Assessment, Second Edition takes preservice and inservice teachers through the process of developing a professional portfolio. It is designed to teach readers how traditional and electronic portfolios are defined, organized, and evaluated. The text also helps teachers to use their portfolios as an action research tool for reflection and professional development.
  computer science portfolio examples: Programming Interviews Exposed John Mongan, Noah Suojanen Kindler, Eric Giguère, 2011-08-10 The pressure is on during the interview process but with the right preparation, you can walk away with your dream job. This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want. What you will learn from this book Tips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.
  computer science portfolio examples: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time.
  computer science portfolio examples: Action Research in Software Engineering Miroslaw Staron, 2019-11-24 This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations. It elaborates on how to find the right research activities and how to distinguish them from non-significant ones. Further, it details how to glean lessons from the research results, no matter whether they are positive or negative. Lastly, it shows how companies can evolve and build talents while expanding their product portfolio. The book’s structure is based on that of AR projects; it sequentially covers and discusses each phase of the project. Each chapter shares new insights into AR and provides the reader with a better understanding of how to apply it. In addition, each chapter includes a number of practical use cases or examples. Taken together, the chapters cover the entire software lifecycle: from problem diagnosis to project (or action) planning and execution, to documenting and disseminating results, including validity assessments for AR studies. The goal of this book is to help everyone interested in industry-academia collaborations to conduct joint research. It is for students of software engineering who need to learn about how to set up an evaluation, how to run a project, and how to document the results. It is for all academics who aren’t afraid to step out of their comfort zone and enter industry. It is for industrial researchers who know that they want to do more than just develop software blindly. And finally, it is for stakeholders who want to learn how to manage industrial research projects and how to set up guidelines for their own role and expectations.
  computer science portfolio examples: Algorithms to Live By Brian Christian, Tom Griffiths, 2016-04-19 'Algorithms to Live By' looks at the simple, precise algorithms that computers use to solve the complex 'human' problems that we face, and discovers what they can tell us about the nature and origin of the mind.
  computer science portfolio examples: Computer Science Edward K. Blum, Alfred V Aho, 2011-12-02 Computer Science: The Hardware, Software and Heart of It focuses on the deeper aspects of the two recognized subdivisions of Computer Science, Software and Hardware. These subdivisions are shown to be closely interrelated as a result of the stored-program concept. Computer Science: The Hardware, Software and Heart of It includes certain classical theoretical computer science topics such as Unsolvability (e.g. the halting problem) and Undecidability (e.g. Godel’s incompleteness theorem) that treat problems that exist under the Church-Turing thesis of computation. These problem topics explain inherent limits lying at the heart of software, and in effect define boundaries beyond which computer science professionals cannot go beyond. Newer topics such as Cloud Computing are also covered in this book. After a survey of traditional programming languages (e.g. Fortran and C++), a new kind of computer Programming for parallel/distributed computing is presented using the message-passing paradigm which is at the heart of large clusters of computers. This leads to descriptions of current hardware platforms for large-scale computing, such as clusters of as many as one thousand which are the new generation of supercomputers. This also leads to a consideration of future quantum computers and a possible escape from the Church-Turing thesis to a new computation paradigm. The book’s historical context is especially helpful during this, the centenary of Turing's birth. Alan Turing is widely regarded as the father of Computer Science, since many concepts in both the hardware and software of Computer Science can be traced to his pioneering research. Turing was a multi-faceted mathematician-engineer and was able to work on both concrete and abstract levels. This book shows how these two seemingly disparate aspects of Computer Science are intimately related. Further, the book treats the theoretical side of Computer Science as well, which also derives from Turing's research. Computer Science: The Hardware, Software and Heart of It is designed as a professional book for practitioners and researchers working in the related fields of Quantum Computing, Cloud Computing, Computer Networking, as well as non-scientist readers. Advanced-level and undergraduate students concentrating on computer science, engineering and mathematics will also find this book useful.
  computer science portfolio examples: Contemporary High Performance Computing Jeffrey S. Vetter, 2019-04-30 Contemporary High Performance Computing: From Petascale toward Exascale, Volume 3 focuses on the ecosystems surrounding the world’s leading centers for high performance computing (HPC). It covers many of the important factors involved in each ecosystem: computer architectures, software, applications, facilities, and sponsors. This third volume will be a continuation of the two previous volumes, and will include other HPC ecosystems using the same chapter outline: description of a flagship system, major application workloads, facilities, and sponsors. Features: Describes many prominent, international systems in HPC from 2015 through 2017 including each system’s hardware and software architecture Covers facilities for each system including power and cooling Presents application workloads for each site Discusses historic and projected trends in technology and applications Includes contributions from leading experts Designed for researchers and students in high performance computing, computational science, and related areas, this book provides a valuable guide to the state-of-the art research, trends, and resources in the world of HPC.
  computer science portfolio examples: ePortfolio Performance Support Systems Katherine V. Wills, Rich Rice, 2013-07-16 ePortfolio Performance Support Systems: Constructing, Presenting, and Assessing Portfolios addresses theories and practices advanced by some of the most innovative and active proponents of ePortfolios.
  computer science portfolio examples: Logic for Computer Scientists Uwe Schöning, 2009-11-03 This book introduces the notions and methods of formal logic from a computer science standpoint, covering propositional logic, predicate logic, and foundations of logic programming. The classic text is replete with illustrative examples and exercises. It presents applications and themes of computer science research such as resolution, automated deduction, and logic programming in a rigorous but readable way. The style and scope of the work, rounded out by the inclusion of exercises, make this an excellent textbook for an advanced undergraduate course in logic for computer scientists.
  computer science portfolio examples: Brand Portfolio Strategy David A. Aaker, 2020-03-24 In this long-awaited book from the world’s premier brand expert and author of the seminal work Building Strong Brands, David Aaker shows managers how to construct a brand portfolio strategy that will support a company’s business strategy and create relevance, differentiation, energy, leverage, and clarity. Building on case studies of world-class brands such as Dell, Disney, Microsoft, Sony, Dove, Intel, CitiGroup, and PowerBar, Aaker demonstrates how powerful, cohesive brand strategies have enabled managers to revitalize brands, support business growth, and create discipline in confused, bloated portfolios of master brands, subbrands, endorser brands, cobrands, and brand extensions. Renowned brand guru Aaker demonstrates that assuring that each brand in the portfolio has a clear role and actively reinforces and supports the other portfolio brands will profoundly affect the firm’s profitability. Brand Portfolio Strategy is required reading not only for brand managers but for all managers with bottom-line responsibility to their shareholders.
  computer science portfolio examples: The Science of Algorithmic Trading and Portfolio Management Robert Kissell, 2013-10-01 The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. - Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. - Helps readers design systems to manage algorithmic risk and dark pool uncertainty. - Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.
  computer science portfolio examples: Structure and Interpretation of Computer Programs Harold Abelson, Gerald Jay Sussman, 2022-05-03 A new version of the classic and widely used text adapted for the JavaScript programming language. Since the publication of its first edition in 1984 and its second edition in 1996, Structure and Interpretation of Computer Programs (SICP) has influenced computer science curricula around the world. Widely adopted as a textbook, the book has its origins in a popular entry-level computer science course taught by Harold Abelson and Gerald Jay Sussman at MIT. SICP introduces the reader to central ideas of computation by establishing a series of mental models for computation. Earlier editions used the programming language Scheme in their program examples. This new version of the second edition has been adapted for JavaScript. The first three chapters of SICP cover programming concepts that are common to all modern high-level programming languages. Chapters four and five, which used Scheme to formulate language processors for Scheme, required significant revision. Chapter four offers new material, in particular an introduction to the notion of program parsing. The evaluator and compiler in chapter five introduce a subtle stack discipline to support return statements (a prominent feature of statement-oriented languages) without sacrificing tail recursion. The JavaScript programs included in the book run in any implementation of the language that complies with the ECMAScript 2020 specification, using the JavaScript package sicp provided by the MIT Press website.
  computer science portfolio examples: Game Engine Architecture Jason Gregory, 2017-03-27 Hailed as a must-have textbook (CHOICE, January 2010), the first edition of Game Engine Architecture provided readers with a complete guide to the theory and practice of game engine software development. Updating the content to match today’s landscape of game engine architecture, this second edition continues to thoroughly cover the major components that make up a typical commercial game engine. New to the Second Edition Information on new topics, including the latest variant of the C++ programming language, C++11, and the architecture of the eighth generation of gaming consoles, the Xbox One and PlayStation 4 New chapter on audio technology covering the fundamentals of the physics, mathematics, and technology that go into creating an AAA game audio engine Updated sections on multicore programming, pipelined CPU architecture and optimization, localization, pseudovectors and Grassman algebra, dual quaternions, SIMD vector math, memory alignment, and anti-aliasing Insight into the making of Naughty Dog’s latest hit, The Last of Us The book presents the theory underlying various subsystems that comprise a commercial game engine as well as the data structures, algorithms, and software interfaces that are typically used to implement them. It primarily focuses on the engine itself, including a host of low-level foundation systems, the rendering engine, the collision system, the physics simulation, character animation, and audio. An in-depth discussion on the gameplay foundation layer delves into the game’s object model, world editor, event system, and scripting system. The text also touches on some aspects of gameplay programming, including player mechanics, cameras, and AI. An awareness-building tool and a jumping-off point for further learning, Game Engine Architecture, Second Edition gives readers a solid understanding of both the theory and common practices employed within each of the engineering disciplines covered. The book will help readers on their journey through this fascinating and multifaceted field.
  computer science portfolio examples: Portfolio Design Harold Linton, Steven Rost, 2003 The ?ible?of portfolio design and production is now in its third edition, revised and expanded to include essential information on the digital and multimedia direction of portfolios today. Whether you work in architecture, urban planning, landscape or interior design, a finely tailored portfolio is the most important element to include in your application for graduate school, a design grant or competition, or to bring to a job interview. In addition to showing you how to assemble a portfolio that will display your talents and qualifications to the best advantage, the third edition of Portfolio Design adds a chapter on digital strategies, discussing all the elements necessary to bring your work together in a digital format. Also new to this edition is commentary and analysis of selected student portfolios by three experienced professionals who offer unique insights to help you develop your own portfolio. From formats, bindings, and cases to reproduction techniques, content, style, sequencing, multimedia, and the latest in promoting yourself on the Internet, Portfolio Design addresses every aspect of portfolio planning and production. Three-hundred samples nearly half of them new to this edition drawn from a wide array of current student and professional portfolios, both print and electronic, illustrate many and varied graphic design alternatives to demonstrate what will capture the reviewer? attention?nd secure you an offer. Portfolio pointers from industry professionals and educators complement the practical advice given by Harold Linton, who has taught portfolio design to generations of students.
  computer science portfolio examples: Book of Majors 2013 College Entrance Examination Board, The College Board, 2012-07-03 An in-depth look at the top 200 college majors and a guide to 3600 colleges offering any or all of these programs.
  computer science portfolio examples: The Future of Computer Science Research in the U.S. United States. Congress. House. Committee on Science, 2005
  computer science portfolio examples: Emerging Technologies in Computing Mahdi H. Miraz, Peter S. Excell, Andrew Ware, Safeeullah Soomro, Maaruf Ali, 2020-09-28 This book constitutes the refereed conference proceedings of the Third International Conference on Emerging Technologies in Computing, iCEtiC 2020, held in London, UK, in August 2020. Due to VOVID-19 pandemic the conference was helt virtually.The 25 revised full papers were reviewed and selected from 65 submissions and are organized in topical sections covering blockchain and cloud computing; security, wireless sensor networks and IoT; AI, big data and data analytics; emerging technologies in engineering, education and sustainable development.
  computer science portfolio examples: Book of Majors 2014 The College Board, 2013-07-02 The Book of Majors 2014 by The College Board helps students answer these questions: What's the major for me? Where can I study it? What can I do with it after graduation? Revised and refreshed every year, this book is the most comprehensive guide to college majors on the market. In-depth descriptions of 200 of the most popular majors are followed by complete listings of every major offered at more than 3,800 colleges, including four-year and two-year colleges and technical schools. The 2014 edition covers every college major identified by the U.S. Department of Education—over 1,200 majors are listed in all. This is also the only guide that shows what degree levels each college offers in a major, whether a certificate, associate, bachelor's, master's or doctorate. The guide features: • insights—from the professors themselves—on how each major is taught, what preparation students will need, other majors to consider and much more. • updated information on career options and employment prospects. • the inside scoop on how students can find out if a college offers a strong program for a particular major, what life is like for students studying that major, and what professional societies and accrediting agencies to refer to for more background on the major.
  computer science portfolio examples: Portfolio Design for Interiors Harold Linton, William Engel, 2017-08-10 The portfolio is the single most important document that a student has to demonstrate his or her expertise. Portfolio Design for Interiors uses real student examples, backed by industry standards and the expertise of the authors, to prepare aspiring interior design professionals to impress.
  computer science portfolio examples: Soft Skills John Sonmez, 2020-11 For most software developers, coding is the fun part. The hard bits are dealing with clients, peers, and managers and staying productive, achieving financial security, keeping yourself in shape, and finding true love. This book is here to help. Soft Skills: The Software Developer's Life Manual is a guide to a well-rounded, satisfying life as a technology professional. In it, developer and life coach John Sonmez offers advice to developers on important subjects like career and productivity, personal finance and investing, and even fitness and relationships. Arranged as a collection of 71 short chapters, this fun listen invites you to dip in wherever you like. A Taking Action section at the end of each chapter tells you how to get quick results. Soft Skills will help make you a better programmer, a more valuable employee, and a happier, healthier person.
  computer science portfolio examples: Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science and Engineering in 2017-2020 National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Committee on Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science in 2017-2020, 2016-08-14 Advanced computing capabilities are used to tackle a rapidly growing range of challenging science and engineering problems, many of which are compute- and data-intensive as well. Demand for advanced computing has been growing for all types and capabilities of systems, from large numbers of single commodity nodes to jobs requiring thousands of cores; for systems with fast interconnects; for systems with excellent data handling and management; and for an increasingly diverse set of applications that includes data analytics as well as modeling and simulation. Since the advent of its supercomputing centers, the National Science Foundation (NSF) has provided its researchers with state-of-the-art computing systems. The growth of new models of computing, including cloud computing and publically available by privately held data repositories, opens up new possibilities for NSF. In order to better understand the expanding and diverse requirements of the science and engineering community and the importance of a new broader range of advanced computing infrastructure, the NSF requested that the National Research Council carry out a study examining anticipated priorities and associated tradeoffs for advanced computing. Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science and Engineering in 2017-2020 provides a framework for future decision-making about NSF's advanced computing strategy and programs. It offers recommendations aimed at achieving four broad goals: (1) position the U.S. for continued leadership in science and engineering, (2) ensure that resources meet community needs, (3) aid the scientific community in keeping up with the revolution in computing, and (4) sustain the infrastructure for advanced computing.
  computer science portfolio examples: Computational Finance and Its Applications II M. Costantino, C. A. Brebbia, 2006 Featuring papers from the Second International Conference on Computational Finance and its Applications, the text includes papers that encompass a wide range of topics such as risk management, derivatives pricing, credit risk, trading strategies, portfolio management and asset allocation, and market analysis.
  computer science portfolio examples: Developer Testing Alexander Tarlinder, 2016-09-07 How do successful agile teams deliver bug-free, maintainable software—iteration after iteration? The answer is: By seamlessly combining development and testing. On such teams, the developers write testable code that enables them to verify it using various types of automated tests. This approach keeps regressions at bay and prevents “testing crunches”—which otherwise may occur near the end of an iteration—from ever happening. Writing testable code, however, is often difficult, because it requires knowledge and skills that cut across multiple disciplines. In Developer Testing, leading test expert and mentor Alexander Tarlinder presents concise, focused guidance for making new and legacy code far more testable. Tarlinder helps you answer questions like: When have I tested this enough? How many tests do I need to write? What should my tests verify? You’ll learn how to design for testability and utilize techniques like refactoring, dependency breaking, unit testing, data-driven testing, and test-driven development to achieve the highest possible confidence in your software. Through practical examples in Java, C#, Groovy, and Ruby, you’ll discover what works—and what doesn’t. You can quickly begin using Tarlinder’s technology-agnostic insights with most languages and toolsets while not getting buried in specialist details. The author helps you adapt your current programming style for testability, make a testing mindset “second nature,” improve your code, and enrich your day-to-day experience as a software professional. With this guide, you will Understand the discipline and vocabulary of testing from the developer’s standpoint Base developer tests on well-established testing techniques and best practices Recognize code constructs that impact testability Effectively name, organize, and execute unit tests Master the essentials of classic and “mockist-style” TDD Leverage test doubles with or without mocking frameworks Capture the benefits of programming by contract, even without runtime support for contracts Take control of dependencies between classes, components, layers, and tiers Handle combinatorial explosions of test cases, or scenarios requiring many similar tests Manage code duplication when it can’t be eliminated Actively maintain and improve your test suites Perform more advanced tests at the integration, system, and end-to-end levels Develop an understanding for how the organizational context influences quality assurance Establish well-balanced and effective testing strategies suitable for agile teams
  computer science portfolio examples: Show Your Work! Austin Kleon, 2015-11-04 Kata Edgar Allan Poe, sebagian besar penulis takut jika proses berkaryanya diketahui orang lain. Sementara itu, Pablo Picasso kerap membuat orang yang berkomunikasi dengannya justru kehilangan energi dan motivasi berkarya. Ya, keduanya memang maestro legendaris, tapi sekarang bukan saatnya lagi berkarya ala mereka. Bukan juga zamannya Mozart sang genius musik. Ini eranya kamu, siapa pun kamu, bisa berkarya! Lalu, apa kuncinya? Tunjukkan saja. Di zaman keterbukaan ini, semua orang punya kesempatan sama untuk jadi hebat. Jangan sembunyikan proses kreatifmu. Undang orang-orang untuk melihatnya. Jangan khawatir kritik, karena itu bahan pelajaran buatmu. Ide yang menurutmu tidak menarik, siapa tahu luar biasa bagi orang lain. Lebih dari itu, karyamu juga bisa menginspirasi orang lain. Jadi, tunggu apa lagi? Tak perlu ragu atau malu. Berbagi karya membuatmu kaya! ... Semakin banyak kamu memberi, semakin banyak yang kembali kepadamu.-Paul Arden [Mizan, Noura Books, Karya, Hidup, Berkarya, Kerja, Indonesia]
  computer science portfolio examples: Exercises in Programming Style Cristina Videira Lopes, 2014-06-02 Using a simple computational task (term frequency) to illustrate different programming styles, Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 33 different styles for writing the term frequency task. The styles are grouped into nine categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, and interactivity. The author verbalizes the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.
  computer science portfolio examples: What Is Computer Science? Daniel Page, Nigel Smart, 2013-12-31 This engaging and accessible text addresses the fundamental question: What Is Computer Science? The book showcases a set of representative concepts broadly connected by the theme of information security, for which the presentation of each topic can be treated as a mini lecture course, demonstrating how it allows us to solve real problems, as well as how it relates to other subjects. The discussions are further supported by numerous examples and practical hands-on exercises. Features: presents a concise introduction to the study of algorithms and describes how computers work; introduces the concepts of data compression, and error detection and correction; highlights the role of data structures; explores the topic of web-search; reviews both historic and modern cryptographic schemes, examines how a physical system can leak information and discusses the idea of randomness; investigates the science of steganography; provides additional supplementary material at an associated website.
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