| can i teach myself computer science: 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. |
| can i teach myself computer science: Mathematics for Computer Science Eric Lehman, F. Thomson Leighton, Albert R. Meyer, 2017-03-08 This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions. |
| can i teach myself computer science: Classic Computer Science Problems in Java David Kopec, 2020-12-21 Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your new problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz |
| can i teach myself computer science: Ultralearning Scott H. Young, 2019-08-06 Now a Wall Street Journal bestseller. Learn a new talent, stay relevant, reinvent yourself, and adapt to whatever the workplace throws your way. Ultralearning offers nine principles to master hard skills quickly. This is the essential guide to future-proof your career and maximize your competitive advantage through self-education. In these tumultuous times of economic and technological change, staying ahead depends on continual self-education—a lifelong mastery of fresh ideas, subjects, and skills. If you want to accomplish more and stand apart from everyone else, you need to become an ultralearner. The challenge of learning new skills is that you think you already know how best to learn, as you did as a student, so you rerun old routines and old ways of solving problems. To counter that, Ultralearning offers powerful strategies to break you out of those mental ruts and introduces new training methods to help you push through to higher levels of retention. Scott H. Young incorporates the latest research about the most effective learning methods and the stories of other ultralearners like himself—among them Benjamin Franklin, chess grandmaster Judit Polgár, and Nobel laureate physicist Richard Feynman, as well as a host of others, such as little-known modern polymath Nigel Richards, who won the French World Scrabble Championship—without knowing French. Young documents the methods he and others have used to acquire knowledge and shows that, far from being an obscure skill limited to aggressive autodidacts, ultralearning is a powerful tool anyone can use to improve their career, studies, and life. Ultralearning explores this fascinating subculture, shares a proven framework for a successful ultralearning project, and offers insights into how you can organize and exe - cute a plan to learn anything deeply and quickly, without teachers or budget-busting tuition costs. Whether the goal is to be fluent in a language (or ten languages), earn the equivalent of a college degree in a fraction of the time, or master multiple tools to build a product or business from the ground up, the principles in Ultralearning will guide you to success. |
| can i teach myself computer science: The Self-Taught Programmer Cory Althoff, 2022-01-13 |
| can i teach myself computer science: Computer Science Robert Sedgewick, Kevin Wayne, 2016-06-17 Named a Notable Book in the 21st Annual Best of Computing list by the ACM! Robert Sedgewick and Kevin Wayne’s Computer Science: An Interdisciplinary Approach is the ideal modern introduction to computer science with Java programming for both students and professionals. Taking a broad, applications-based approach, Sedgewick and Wayne teach through important examples from science, mathematics, engineering, finance, and commercial computing. The book demystifies computation, explains its intellectual underpinnings, and covers the essential elements of programming and computational problem solving in today’s environments. The authors begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, they turn to functions, introducing key modular programming concepts, including components and reuse. They present a modern introduction to object-oriented programming, covering current programming paradigms and approaches to data abstraction. Building on this foundation, Sedgewick and Wayne widen their focus to the broader discipline of computer science. They introduce classical sorting and searching algorithms, fundamental data structures and their application, and scientific techniques for assessing an implementation’s performance. Using abstract models, readers learn to answer basic questions about computation, gaining insight for practical application. Finally, the authors show how machine architecture links the theory of computing to real computers, and to the field’s history and evolution. For each concept, the authors present all the information readers need to build confidence, together with examples that solve intriguing problems. Each chapter contains question-and-answer sections, self-study drills, and challenging problems that demand creative solutions. Companion web site (introcs.cs.princeton.edu/java) contains Extensive supplementary information, including suggested approaches to programming assignments, checklists, and FAQs Graphics and sound libraries Links to program code and test data Solutions to selected exercises Chapter summaries Detailed instructions for installing a Java programming environment Detailed problem sets and projects Companion 20-part series of video lectures is available at informit.com/title/9780134493831 |
| can i teach myself computer science: HT THINK LIKE A COMPUTER SCIEN Jeffrey Elkner, Allen B. Downey, Chris Meyers, 2016-10-04 The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, The way of the program. On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer. |
| can i teach myself computer science: Beyond Coding Marina Umaschi Bers, 2022-03-22 Why children should be taught coding not as a technical skill but as a new literacy—a way to express themselves and engage with the world. Today, schools are introducing STEM education and robotics to children in ever-lower grades. In Beyond Coding, Marina Umaschi Bers lays out a pedagogical roadmap for teaching code that encompasses the cultivation of character along with technical knowledge and skills. Presenting code as a universal language, she shows how children discover new ways of thinking, relating, and behaving through creative coding activities. Today’s children will undoubtedly have the technical knowledge to change the world. But cultivating strength of character, socioeconomic maturity, and a moral compass alongside that knowledge, says Bers, is crucial. Bers, a leading proponent of teaching computational thinking and coding as early as preschool and kindergarten, presents examples of children and teachers using the Scratch Jr. and Kibo robotics platforms to make explicit some of the positive values implicit in the process of learning computer science. If we are to do right by our children, our approach to coding must incorporate the elements of a moral education: the use of narrative to explore identity and values, the development of logical thinking to think critically and solve technical and ethical problems, and experiences in the community to enable personal relationships. Through learning the language of programming, says Bers, it is possible for diverse cultural and religious groups to find points of connection, put assumptions and stereotypes behind them, and work together toward a common goal. |
| can i teach myself computer science: Everything You Need to Ace Computer Science and Coding in One Big Fat Notebook Workman Publishing, 2020-04-28 From the editors of Brain Quest, America’s #1 educational bestseller! This Big Fat Notebook makes it all “sink in” with key concepts, mnemonic devices, definitions, diagrams, and doodles to help you understand computer science. Including: Computing systems Binary code Algorithms Computational thinking Loops, events, and procedures Programming in Scratch and Python Boolean Expressions Web development Cybersecurity HTML CSS …and more! The Big Fat Notebook series is built on a simple and irresistible conceit—borrowing the notes from the smartest kid in class. Each book in the series meets Common Core State Standards, Next Generation Science Standards, and state history standards, and are vetted by National and State Teacher of the Year Award–winning teachers. They make learning fun and are the perfect next step for every kid who grew up on Brain Quest. |
| can i teach myself computer science: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| can i teach myself computer science: Python Programming John M. Zelle, 2004 This book is suitable for use in a university-level first course in computing (CS1), as well as the increasingly popular course known as CS0. It is difficult for many students to master basic concepts in computer science and programming. A large portion of the confusion can be blamed on the complexity of the tools and materials that are traditionally used to teach CS1 and CS2. This textbook was written with a single overarching goal: to present the core concepts of computer science as simply as possible without being simplistic. |
| can i teach myself computer science: Improving Computer Science Education Djordje M. Kadijevich, Charoula Angeli, Carsten Schulte, 2013-02-11 Improving Computer Science Education examines suitable theoretical frameworks for conceptualizing teaching and learning computer science. This highly useful book provides numerous examples of practical, real world applications of major computer science information topics, such as: • Spreadsheets • Databases • Programming Each chapter concludes with a section that summarzies recommendations for teacher professional development. Traditionally, computer science education has been skills-focused and disconnected from the reality students face after they leave the classroom. Improving Computer Science Education makes the subject matter useful and meaningful by connecting it explicitly to students' everyday lives. |
| can i teach myself computer science: Beginning Programming with Python For Dummies John Paul Mueller, 2018-02-13 The easy way to learn programming fundamentals with Python Python is a remarkably powerful and dynamic programming language that's used in a wide variety of application domains. Some of its key distinguishing features include a very clear, readable syntax, strong introspection capabilities, intuitive object orientation, and natural expression of procedural code. Plus, Python features full modularity, supporting hierarchical packages, exception-based error handling, and modules easily written in C, C++, Java, R, or .NET languages, such as C#. In addition, Python supports a number of coding styles that include: functional, imperative, object-oriented, and procedural. Due to its ease of use and flexibility, Python is constantly growing in popularity—and now you can wear your programming hat with pride and join the ranks of the pros with the help of this guide. Inside, expert author John Paul Mueller gives a complete step-by-step overview of all there is to know about Python. From performing common and advanced tasks, to collecting data, to interacting with package—this book covers it all! Use Python to create and run your first application Find out how to troubleshoot and fix errors Learn to work with Anaconda and use Magic Functions Benefit from completely updated and revised information since the last edition If you've never used Python or are new to programming in general, Beginning Programming with Python For Dummies is a helpful resource that will set you up for success. |
| can i teach myself computer science: Computer Architecture John L. Hennessy, David A. Patterson, Krste Asanović, 2012 The computing world is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation. This book focuses on the shift, exploring the ways in which software and technology in the 'cloud' are accessed by cell phones, tablets, laptops, and more |
| can i teach myself computer science: Computer Science in K-12 Shuchi Grover, 2020-04 Coding teaches our students the essence of logical thinking and problem solving while also preparing them for a world in which computing is becoming increasingly pervasive. While there's excitement and enthusiasm about programming becoming an intrinsic part of K-12 curricula the world over, there's also growing anxiety about preparing teachers to teach effectively at all grade levels.This book strives to be an essential, enduring, practical guide for every K-12 teacher anywhere who is either teaching or planning to teach computer science and programming at any grade level. To this end, readers will discover:? An A-to-Z organization that affords comprehensive insight into teaching introductory programming.? 26 chapters that cover foundational concepts, practices and well-researched pedagogies related to teaching introductory programming as an integral part of K-12 computer science. Cumulatively these chapters address the two salient building blocks of effective teaching of introductory programming-what content to teach (concepts and practices) and how to teach (pedagogy).? Concrete ideas and rich grade-appropriate examples inspired by practice and research for classroom use.? Perspectives and experiences shared by educators and scholars who are actively practicing and/or examiningthe teaching of computer science and programming in K-12 classrooms. |
| can i teach myself computer science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| can i teach myself computer science: Computer Science Education Sue Sentance, Erik Barendsen, Nicol R. Howard, Carsten Schulte, 2023-02-23 Drawing together the most up-to-date research from experts all across the world, the second edition of Computer Science Education offers the most up-to-date coverage available on this developing subject, ideal for building confidence of new pre-service and in-service educators teaching a new discipline. It provides an international overview of key concepts, pedagogical approaches and assessment practices. Highlights of the second edition include: - New sections on machine learning and data-driven (epistemic) programming - A new focus on equity and inclusion in computer science education - Chapters updated throughout, including a revised chapter on relating ethical and societal aspects to knowledge-rich aspects of computer science education - A new set of chapters on the learning of programming, including design, pedagogy and misconceptions - A chapter on the way we use language in the computer science classroom. The book is structured to support the reader with chapter outlines, synopses and key points. Explanations of key concepts, real-life examples and reflective points keep the theory grounded in classroom practice. The book is accompanied by a companion website, including online summaries for each chapter, 3-minute video summaries by each author and an archived chapter on taxonomies and competencies from the first edition. |
| can i teach myself computer science: The Self-Taught Computer Scientist Cory Althoff, 2021-09-16 The follow-up to Cory Althoff's bestselling The Self-Taught Programmer, which inspired hundreds of thousands of professionals to learn to program outside of school! Fresh out of college and with just a year of self-study behind him, Cory Althoff was offered a dream first job as a software engineer for a well-known tech company, but he quickly found himself overwhelmed by the amount of things he needed to know, but hadn’t learned yet. This experience combined with his personal journey learning to program inspired his widely praised guide, The Self-Taught Programmer. Now Cory's back with another guide for the self-taught community of learners focusing on the foundations of computer science. The Self-Taught Computer Scientist introduces beginner and self-taught programmers to computer science fundamentals that are essential for success in programming and software engineering fields. Computer science is a massive subject that could cover an entire lifetime of learning. This book does not aim to cover everything you would learn about if you went to school to get a computer science degree. Instead, Cory's goal is to give you an introduction to some of the most important concepts in computer science that apply to a programming career. With a focus on data structures and algorithms, The Self-Taught Computer Scientist helps you fill gaps in your knowledge, prepare for a technical interview, feel knowledgeable and confident on the job, and ultimately, become a better programmer. Learn different algorithms including linear and binary search and test your knowledge with feedback loops Understand what a data structure is and study arrays, linked lists, stacks, queues, hash tables, binary trees, binary heaps, and graphs Prepare for technical interviews and feel comfortable working with more experienced colleagues Discover additional resources and tools to expand your skillset and continue your learning journey It's as simple as this: You have to study computer science if you want to become a successful programmer, and if you don't understand computer science, you won't get hired. Ready for a career in programming, coding, or software engineering and willing to embrace an always be learning mindset? The Self-Taught Computer Scientist is for you. |
| can i teach myself computer science: Game Programming Patterns Robert Nystrom, 2014-11-03 The biggest challenge facing many game programmers is completing their game. Most game projects fizzle out, overwhelmed by the complexity of their own code. Game Programming Patterns tackles that exact problem. Based on years of experience in shipped AAA titles, this book collects proven patterns to untangle and optimize your game, organized as independent recipes so you can pick just the patterns you need. You will learn how to write a robust game loop, how to organize your entities using components, and take advantage of the CPUs cache to improve your performance. You'll dive deep into how scripting engines encode behavior, how quadtrees and other spatial partitions optimize your engine, and how other classic design patterns can be used in games. |
| can i teach myself computer science: Computer Programming for Absolute Beginners Joakim Wassberg, 2020-07-31 Get to grips with the building blocks of programming languages and get started on your programming journey without a computer science degree Key FeaturesUnderstand the fundamentals of a computer program and apply the concepts you learn to different programming languagesGain the confidence to write your first computer programExplore tips, techniques, and best practices to start coding like a professional programmerBook Description Learning how to code has many advantages, and gaining the right programming skills can have a massive impact on what you can do with your current skill set and the way you advance in your career. This book will be your guide to learning computer programming easily, helping you overcome the difficulties in understanding the major constructs in any mainstream programming language. Computer Programming for Absolute Beginners starts by taking you through the building blocks of any programming language with thorough explanations and relevant examples in pseudocode. You'll understand the relationship between computer programs and programming languages and how code is executed on the computer. The book then focuses on the different types of applications that you can create with your programming knowledge. You'll delve into programming constructs, learning all about statements, operators, variables, and data types. As you advance, you'll see how to control the flow of your programs using control structures and reuse your code using functions. Finally, you'll explore best practices that will help you write code like a pro. By the end of this book, you'll be prepared to learn any programming language and take control of your career by adding coding to your skill set. What you will learnGet to grips with basic programming language concepts such as variables, loops, selection and functionsUnderstand what a program is and how the computer executes itExplore different programming languages and learn about the relationship between source code and executable codeSolve problems using various paradigms such as procedural programming, object oriented programming, and functional programmingWrite high-quality code using several coding conventions and best practicesBecome well-versed with how to track and fix bugs in your programsWho this book is for This book is for beginners who have never programmed before and are looking to enter the world of programming. This includes anyone who is about to start studying programming and wants a head start, or simply wants to learn how to program on their own. |
| can i teach myself computer science: Getting Smart Tom Vander Ark, 2011-09-20 A comprehensive look at the promise and potential of online learning In our digital age, students have dramatically new learning needs and must be prepared for the idea economy of the future. In Getting Smart, well-known global education expert Tom Vander Ark examines the facets of educational innovation in the United States and abroad. Vander Ark makes a convincing case for a blend of online and onsite learning, shares inspiring stories of schools and programs that effectively offer personal digital learning opportunities, and discusses what we need to do to remake our schools into smart schools. Examines the innovation-driven world, discusses how to combine online and onsite learning, and reviews smart tools for learning Investigates the lives of learning professionals, outlines the new employment bargain, examines online universities and smart schools Makes the case for smart capital, advocates for policies that create better learning, studies smart cultures |
| can i teach myself computer science: Computer Science Distilled Wladston Ferreira Filho, 2017-01-17 A walkthrough of computer science concepts you must know. Designed for readers who don't care for academic formalities, it's a fast and easy computer science guide. It teaches the foundations you need to program computers effectively. After a simple introduction to discrete math, it presents common algorithms and data structures. It also outlines the principles that make computers and programming languages work. |
| can i teach myself computer science: Computer Science National Research Council, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Committee on the Fundamentals of Computer Science: Challenges and Opportunities, 2004-10-06 Computer Science: Reflections on the Field, Reflections from the Field provides a concise characterization of key ideas that lie at the core of computer science (CS) research. The book offers a description of CS research recognizing the richness and diversity of the field. It brings together two dozen essays on diverse aspects of CS research, their motivation and results. By describing in accessible form computer science's intellectual character, and by conveying a sense of its vibrancy through a set of examples, the book aims to prepare readers for what the future might hold and help to inspire CS researchers in its creation. |
| can i teach myself computer science: The Elements of Computing Systems Noam Nisan, Shimon Schocken, 2008 This title gives students an integrated and rigorous picture of applied computer science, as it comes to play in the construction of a simple yet powerful computer system. |
| can i teach myself computer science: Your First Year Teaching Computer Science Chris Gregg, 2021-01-15 Your First Year Teaching Computer Science is a comprehensive guide to teaching computer science geared to new instructors in the field. It can be used as a guide and a reference, and it provides multiple examples of how to construct teaching materials, how to prepare lectures, how to write assignments, how to train TAs, and how to advise students, among many other topics. It is both motivational and instructive, and it provides a foundation on which to become a great CS instructor. Teaching computer science involves more than just teaching the material, and this book details all of the other parts of teaching that you will need to know to do the job. If you are wondering where to begin as a computer science teacher, this is the book for you.Features-Serves as a comprehensive guide to teaching introductory computer science for new teachers, and experienced teachers can refer to it on specific points. -Provides examples of teaching materials, grading guides, multiple lists, and other valuable resource for helping new teachers to launch their first computer science courses. -Includes information about training TAs, holding office hours, advising students, and many other practical information that is not specifically about the technical part of teaching computer science. -Written in a conversational tone and is premised on the belief that teaching should be rewarding, fun, and engaging. |
| can i teach myself computer science: Guide to Teaching Computer Science Orit Hazzan, Tami Lapidot, Noa Ragonis, 2015-01-07 This textbook presents both a conceptual framework and detailed implementation guidelines for computer science (CS) teaching. Updated with the latest teaching approaches and trends, and expanded with new learning activities, the content of this new edition is clearly written and structured to be applicable to all levels of CS education and for any teaching organization. Features: provides 110 detailed learning activities; reviews curriculum and cross-curriculum topics in CS; explores the benefits of CS education research; describes strategies for cultivating problem-solving skills, for assessing learning processes, and for dealing with pupils’ misunderstandings; proposes active-learning-based classroom teaching methods, including lab-based teaching; discusses various types of questions that a CS instructor or trainer can use for a range of teaching situations; investigates thoroughly issues of lesson planning and course design; examines the first field teaching experiences gained by CS teachers. |
| can i teach myself computer science: Make It Stick Peter C. Brown, Henry L. Roediger III, Mark A. McDaniel, 2014-04-14 To most of us, learning something the hard way implies wasted time and effort. Good teaching, we believe, should be creatively tailored to the different learning styles of students and should use strategies that make learning easier. Make It Stick turns fashionable ideas like these on their head. Drawing on recent discoveries in cognitive psychology and other disciplines, the authors offer concrete techniques for becoming more productive learners. Memory plays a central role in our ability to carry out complex cognitive tasks, such as applying knowledge to problems never before encountered and drawing inferences from facts already known. New insights into how memory is encoded, consolidated, and later retrieved have led to a better understanding of how we learn. Grappling with the impediments that make learning challenging leads both to more complex mastery and better retention of what was learned. Many common study habits and practice routines turn out to be counterproductive. Underlining and highlighting, rereading, cramming, and single-minded repetition of new skills create the illusion of mastery, but gains fade quickly. More complex and durable learning come from self-testing, introducing certain difficulties in practice, waiting to re-study new material until a little forgetting has set in, and interleaving the practice of one skill or topic with another. Speaking most urgently to students, teachers, trainers, and athletes, Make It Stick will appeal to all those interested in the challenge of lifelong learning and self-improvement. |
| can i teach myself computer science: How to Design Programs, second edition Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, 2018-05-25 A completely revised edition, offering new design recipes for interactive programs and support for images as plain values, testing, event-driven programming, and even distributed programming. This introduction to programming places computer science at the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process, presenting program design guidelines that show the reader how to analyze a problem statement, how to formulate concise goals, how to make up examples, how to develop an outline of the solution, how to finish the program, and how to test it. Because learning to design programs is about the study of principles and the acquisition of transferable skills, the text does not use an off-the-shelf industrial language but presents a tailor-made teaching language. For the same reason, it offers DrRacket, a programming environment for novices that supports playful, feedback-oriented learning. The environment grows with readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks. This second edition has been completely revised. While the book continues to teach a systematic approach to program design, the second edition introduces different design recipes for interactive programs with graphical interfaces and batch programs. It also enriches its design recipes for functions with numerous new hints. Finally, the teaching languages and their IDE now come with support for images as plain values, testing, event-driven programming, and even distributed programming. |
| can i teach myself computer science: Computation Structures Stephen A. Ward, Robert H. Halstead, 1990 Computer Systems Organization -- general. |
| can i teach myself computer science: Foundations of Computer Science Alfred V. Aho, Jeffrey D. Ullman, 1994-10-15 |
| can i teach myself computer science: Computer Science Illuminated Nell B. Dale, John Lewis, 2013 Revised and updated with the latest information in the field, the Fifth Edition of best-selling Computer Science Illuminated continues to provide students with an engaging breadth-first overview of computer science principles and provides a solid foundation for those continuing their study in this dynamic and exciting discipline. Authored by two of today's most respected computer science educators, Nell Dale and John Lewis, the text carefully unfolds the many layers of computing from a language-neutral perspective, beginning with the information layer, progressing through the hardware, programming, operating systems, application, and communication layers, and ending with a discussion on the limitations of computing. Separate program language chapters are available as bundle items for instructors who would like to explore a particular programming language with their students. Ideal for introductory computing and computer science courses, the fifth edition's thorough presentation of computing systems provides computer science majors with a solid foundation for further study, and offers non-majors a comprehensive and complete introduction to computing. New Features of the Fifth Edition: - Includes a NEW chapter on computer security (chapter 17) to provide readers with the latest information, including discussions on preventing unauthorized access and guidelines for creating effective passwords, types of malware anti-virus software, problems created by poor programming, protecting your online information including data collection issues with Facebook, Google, etc., and security issues with mobile and portable devices. - A NEW section on cloud computing (chapter 15) offers readers an overview of the latest way in which businesses and users interact with computers and mobile devices. - The section on social networks (moved to chapter 16) has been rewritten to include up-to-date information, including new data on Google+ and Facebook. - The sections covering HTML have been updated to include HTML5. - Includes revised and updated Did You Know callouts in the chapter margins. - The updated Ethical Issues at the end of each chapter have been revised to tie the content to the recently introduced tenth strand recommended by the ACM stressing the importance of computer ethics. Instructor Resources: -Answers to the end of chapter exercises -Answers to the lab exercises -PowerPoint Lecture Outlines -PowerPoint Image Bank -Test Bank Every new copy is packaged with a free access code to the robust Student Companion Website featuring: Animated Flashcards; Relevant Web Links; Crossword Puzzles; Interactive Glossary; Step by step tutorial on web page development; Digital Lab Manual; R. Mark Meyer's labs, Explorations in Computer Science; Additional programming chapters, including Alice, C++, Java, JavaScript, Pascal, Perl, Python, Ruby, SQL, and VB.NET; C++ Language Essentials labs; Java Language Essentials labs; Link to Download Pep/8 |
| can i teach myself computer science: Feynman Lectures On Computation Richard P. Feynman, 2018-07-03 When, in 1984?86, Richard P. Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a ?Feynmanesque? overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers. |
| can i teach myself computer science: A to Z Computer Science Activity Book Sakina Fakhruddin, 2020-05-25 An awesome and fascinating introduction to computer science concepts, one letter at a time!The Computer Science A to Z Activity Book introduces kids (and adults) to coding and computer science vocabulary, from letter A to Z. Explore the collection of fun, engaging and innovative activities in this book and learn the basics of programming and computer science! On every page, you will find a brief description of the word mentioned and a fun activity with it. You will learn about how the Internet works, algorithms, and also learn about people like Grace Hopper, Tim Berners-Lee and more. This book has places for you to draw and color, word searches, and a ton of other awesome stuff! Draw a flowchart for baking a cookie, learn sorting using playing cards, debug some code, and HAVE FUN! |
| can i teach myself computer science: Touch of Class Bertrand Meyer, 2009-08-28 This text combines a practical, hands-on approach to programming with the introduction of sound theoretical support focused on teaching the construction of high-quality software. A major feature of the book is the use of Design by Contract. |
| can i teach myself computer science: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| can i teach myself computer science: The Cambridge Handbook of Computing Education Research Sally A. Fincher, Anthony V. Robins, 2019-02-13 This is an authoritative introduction to Computing Education research written by over 50 leading researchers from academia and the industry. |
| can i teach myself computer science: Calculus Revisited R.W. Carroll, 2002-12-31 In this book the details of many calculations are provided for access to work in quantum groups, algebraic differential calculus, noncommutative geometry, fuzzy physics, discrete geometry, gauge theory, quantum integrable systems, braiding, finite topological spaces, some aspects of geometry and quantum mechanics and gravity. |
| can i teach myself computer science: A Programmer's Guide to Computer Science William M Springer II, 2020-01-03 You know how to code..but is it enough? Do you feel left out when other programmers talk about asymptotic bounds? Have you failed a job interview because you don't know computer science? The author, a senior developer at a major software company with a PhD in computer science, takes you through what you would have learned while earning a four-year computer science degree. Volume one covers the most frequently referenced topics, including algorithms and data structures, graphs, problem-solving techniques, and complexity theory. When you finish this book, you'll have the tools you need to hold your own with people who have - or expect you to have - a computer science degree. |
| can i teach myself computer science: How to Prove It Daniel J. Velleman, 2006-01-16 Many students have trouble the first time they take a mathematics course in which proofs play a significant role. This new edition of Velleman's successful text will prepare students to make the transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs. The book begins with the basic concepts of logic and set theory, to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for a step-by-step breakdown of the most important techniques used in constructing proofs. The author shows how complex proofs are built up from these smaller steps, using detailed 'scratch work' sections to expose the machinery of proofs about the natural numbers, relations, functions, and infinite sets. To give students the opportunity to construct their own proofs, this new edition contains over 200 new exercises, selected solutions, and an introduction to Proof Designer software. No background beyond standard high school mathematics is assumed. This book will be useful to anyone interested in logic and proofs: computer scientists, philosophers, linguists, and of course mathematicians. |
| can i teach myself computer science: Rev Up Robotics Jorge Valenzuela, 2022-08-18 Author Jorge Valenzuela lays out the foundational skills of computational thinking required for programming with robotics. Unlike other robotics books and curriculum, Rev Up Robotics takes a cross-curricular approach, showing educators how to begin incorporating robotics into their content area lessons and in conjunction with other subjects. You’ll get an overview of standards-based skills that can be covered in English language arts, math, science, social studies and robotics electives. Teachers also get tips for selecting the robot that works for them and for students, and details on the functions of gears, motors and sensors. Also included is a deep dive into more advanced topics like the intersections of computer science, mechanical engineering and electrical engineering with robotics. Finally, you’ll find advice for getting students involved with competitive robotics, and case studies that offer empirical evidence for using robotics successfully in instruction. The book: • Shows how to help students recognize and apply the four elements of computational thinking to familiar situations. • Provides a pathway from working with visual blocks to programming in C++. • Discusses building and programming robots, with tips for adding your own code and troubleshooting. • Demonstrates how to manipulate basic movement to better understand the functions of gears, motors and sensors. With activities and examples for grade levels K-8, teachers come away with easy-to-implement cross-curricular ideas to engage students in computer science and engineering activities. |
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