Introduction to Computation and Programming Using Python

Author: John V. Guttag
Publisher: MIT Press
ISBN: 0262337398
Format: PDF, ePub, Docs
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This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.

Program Development in Java

Author: Barbara Liskov
Publisher: Pearson Education
ISBN: 076868496X
Format: PDF, ePub
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Written by a world-renowned expert on programming methodology, and the winner of the 2008 Turing Award, this book shows how to build production-quality programs--programs that are reliable, easy to maintain, and quick to modify. Its emphasis is on modular program construction: how to get the modules right and how to organize a program as a collection of modules. The book presents a methodology effective for either an individual programmer, who may be writing a small program or a single module in a larger one; or a software engineer, who may be part of a team developing a complex program comprised of many modules. Both audiences will acquire a solid foundation for object-oriented program design and component-based software development from this methodology. Because each module in a program corresponds to an abstraction, such as a collection of documents or a routine to search the collection for documents of interest, the book first explains the kinds of abstractions most useful to programmers: procedures; iteration abstractions; and, most critically, data abstractions. Indeed, the author treats data abstraction as the central paradigm in object-oriented program design and implementation. The author also shows, with numerous examples, how to develop informal specifications that define these abstractions--specifications that describe what the modules do--and then discusses how to implement the modules so that they do what they are supposed to do with acceptable performance. Other topics discussed include: Encapsulation and the need for an implementation to provide the behavior defined by the specification Tradeoffs between simplicity and performance Techniques to help readers of code understand and reason about it, focusing on such properties as rep invariants and abstraction functions Type hierarchy and its use in defining families of related data abstractions Debugging, testing, and requirements analysis Program design as a top-down, iterative process, and design patterns The Java programming language is used for the book's examples. However, the techniques presented are language independent, and an introduction to key Java concepts is included for programmers who may not be familiar with the language.

Research Directions in Computer Science

Author: Albert R. Meyer
Publisher: MIT Press (MA)
ISBN: 9780262132572
Format: PDF, ePub
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Research Directions in Computer Science celebrates the twenty-fifth anniversary of the founding of MIT's Project MAC. It covers the full range of ongoing computer science research at the MIT Laboratory for Computer Science and the MIT Artificial Intelligence Laboratory, both of which grew out of the original Project MAC. Leading researchers from the faculties and staffs of the laboratories highlight current research and future activities in multiprocessors and parallel computer architectures, in languages and systems for distributed computing, in intelligent systems (AI) and robotics, in complexity and learning theory, in software methodology, in programming language theory, in software for engineering research and education, and in the relation between computers and economic productivity. The contributors includeHarold Abelson, Arvind, Rodney Brooks, David Clark, Fernando Corbato, William Daily, Michael Dertouzos, John Guttag, Berthold K. P. Horn, Barbara Liskov, Albert Meyer, Nicholas Negroponte, Marc Raibert, Ronald Rivest, Michael Sipser, Gerald Sussman, Peter Szolovits, and John Updike.

Python Programming

Author: John M. Zelle
Publisher: Franklin, Beedle & Associates, Inc.
ISBN: 1887902996
Format: PDF, Docs
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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.

A Gentle Introduction to Effective Computing in Quantitative Research

Author: Harry J. Paarsch
Publisher: MIT Press
ISBN: 0262333996
Format: PDF, Kindle
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This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; students entering PhD programs in business, economics, and other social or natural sciences; and those seeking quantitative jobs in industry. No background in computer science is assumed; a learner need only have a computer with access to the Internet. Using the example as its principal pedagogical device, the book offers tried-and-true prototypes that illustrate many important computational tasks required in quantitative research. The best way to use the book is to read it at the computer keyboard and learn by doing.The book begins by introducing basic skills: how to use the operating system, how to organize data, and how to complete simple programming tasks. For its demonstrations, the book uses a UNIX-based operating system and a set of free software tools: the scripting language Python for programming tasks; the database management system SQLite; and the freely available R for statistical computing and graphics. The book goes on to describe particular tasks: analyzing data, implementing commonly used numerical and simulation methods, and creating extensions to Python to reduce cycle time. Finally, the book describes the use of LaTeX, a document markup language and preparation system.

Cloud Computing for Science and Engineering

Author: Ian Foster
Publisher: MIT Press
ISBN: 0262037246
Format: PDF, Docs
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The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.

Programming for the Puzzled

Author: Srini Devadas
Publisher: MIT Press
ISBN: 0262343193
Format: PDF, ePub, Mobi
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This book builds a bridge between the recreational world of algorithmic puzzles (puzzles that can be solved by algorithms) and the pragmatic world of computer programming, teaching readers to program while solving puzzles. Few introductory students want to program for programming's sake. Puzzles are real-world applications that are attention grabbing, intriguing, and easy to describe. Each lesson starts with the description of a puzzle. After a failed attempt or two at solving the puzzle, the reader arrives at an Aha! moment -- a search strategy, data structure, or mathematical fact -- and the solution presents itself. The solution to the puzzle becomes the specification of the code to be written. Readers will thus know what the code is supposed to do before seeing the code itself. This represents a pedagogical philosophy that decouples understanding the functionality of the code from understanding programming language syntax and semantics. Python syntax and semantics required to understand the code are explained as needed for each puzzle. Readers need only the rudimentary grasp of programming concepts that can be obtained from introductory or AP computer science classes in high school. The book includes more than twenty puzzles and more than seventy programming exercises that vary in difficulty. Many of the puzzles are well known and have appeared in publications and on websites in many variations. They range from scheduling selfie time with celebrities to solving Sudoku problems in seconds to verifying the six degrees of separation hypothesis. The code for selected puzzle solutions is downloadable from the book's website; the code for all puzzle solutions is available to instructors.

Abstraction and Specification in Program Development

Author: Barbara Liskov
Publisher: McGraw-Hill
ISBN: 9780070379961
Format: PDF, ePub, Mobi
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Abstraction and Specification in Program Development offers professionals in program design and software engineering a methodology that will enable them to construct programs that are reliable and reasonably easy to understand, modify, and maintain. Good programming involves the systematic mastery of complexity, and this book provides the first unified treatment of the techniques of abstraction and specification, which, the authors argue, are the linchpin of any effective approach to programming. They place particular emphasis on the use of data abstraction to produce highly modular programs. The authors focus on the process of decomposing large program projects into independent modules that can be assigned to independent working groups. They discuss methods of decomposition, the kinds of modules that are most useful in this process, and techniques to increase the likelihood that modules produced can in fact be recombined to solve the original programming problem. There are many examples of abstractions throughout the text, and each chapter ends with pertinent references and exercises. Most of the sample implementations in the book are written in CLU, one of a growing number of languages able to support data abstraction. Sufficient material is included, however, to allow the reader to work in Pascal as well. The material in this book was developed by the authors during a decade of teaching undergraduate, graduate, and professional-level courses. Barbara Liskov, the developer of CLU, is Professor and John Guttag an Associate Professor of Computer Science at MIT. Abstraction and Specification in Program Development is included in the MIT Electrical Engineering and Computer Science series.