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Written for anyone with either GW-BASIC or BASICA, Albrecht helps users build on applications using graphics, sound, and text. With hands-on exercises and skill checks, readers learn how to write programs for increasing business and personal productivity, as well as for entertainment. Annotation copyrighted by Book News, Inc., Portland, OR
If you've always wanted to learn basic programming skills on your personal computer, but weren't sure where to start, here's the book you need. You can satisfy your curiousity about programming and establish excellent programming fundamentals for your future ventures into QuickBASIC or Turbo BASIC.
Sams Teach Yourself Beginning Programming in 24 Hours, Second Edition explains the basics of programming in the successful 24-Hours format. The book begins with the absolute basics of programming: Why program? What tools to use? How does a program tell the computer what to do? It teaches readers how to program the computer and then moves on by exploring the some most popular programming languages in use. The author starts by introducing the reader to the Basic language and finishes with basic programming techniques for Java, C++, and others.
This reference includes tutorial sections on basic programming concepts and how to control the language. It also includes descriptions of all GW-BASIC functions, statements, and commands, and teaches the art of good programming form.
Microsoft GW-BASIC greets you with a simple user prompt: Ok. An invitation to a world of possibility, the Ok prompt presents a blank canvas with which to code a masterpiece. But GW-BASIC, released over three decades ago, has sadly fallen into disuse, its decline greatly hastened once graphical user interfaces like Windows began to proliferate, leaving text-based MS-DOS (Microsoft Disk Operating System) programs to wither away. And that's just not ok. In "Not Ok," Mark Jones Lorenzo argues that GW-BASIC still has much to offer both the budding and experienced programmer. Packed with delicious type-in algorithmic recipes to stew, "Not Ok" is the last in a long line of mashup BASIC cookbooks that once transformed coding into a delectable romantic pursuit. With the recipes will come the inspiration to cook up your own algorithms, in turn sharpening your programming chops while keeping GW-BASIC fresh and alive. But "Not Ok" is not meant to be an exhaustive tour through GW-BASIC history or commands or statements or functions, nor is it intended as some kind of learn-GW-BASIC-in-ten-easy-steps tutorial, nor is it designed as some sort of teleological work, although it contains bits of all of those things. Instead, this book brushes up against the absolute limits of the GW-BASIC interpreter toolbox, only presenting you with a dose of mathematics when absolutely necessary, whilst shying away from PEEKs, POKEs, and other assembly language-type subroutines. Appropriately, "Not Ok"'s scope is especially vast; the approach is meant to be as accessible as possible while also not sparing many details, serving as both a GW-BASIC appetizer and a main course. And unlike most other computer programming books, in which the keywords or the concepts are the focus, here the programs are the centerpiece from which everything else follows. "Not Ok" rekindles an itch, reminding erstwhile BASIC programmers of the supreme satisfaction (coupled with a heavy dose of nostalgia) to be had while coding in GW-BASIC, and maybe, just maybe, can also introduce the programming language, with all of its simple pleasures and lovable foibles, to a younger set. So don't cue the funeral dirge just yet. With a little luck, and a generous helping of "Not Ok," perhaps a requiem won't be needed for GW-BASIC after all. * GW-BASIC is a registered trademark of the Microsoft Corporation, which did not in any way endorse or assist in the production of this product.
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
Introduces the features of the GW-BASIC programming language, and covers the screen editor, files, functions, variables, and operators
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
Praise for How Learning Works "How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning." —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching "This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching." —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education "Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues." —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching "As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book." —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning