Last Updated: December 2020 Based on Julia v1.3+ and JuMP v0.21+ The main motivation of writing this book was to help the author himself. He is a professor in the field of operations research, and his daily activities involve building models of mathematical optimization, developing algorithms for solving the problems, implementing those algorithms using computer programming languages, experimenting with data, etc. Three languages are involved: human language, mathematical language, and computer language. His team of students need to go over three different languages, which requires "translation" among the three languages. As this book was written to teach his research group how to translate, this book will also be useful for anyone who needs to learn how to translate in a similar situation. The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science. For more information, visit: http://www.chkwon.net/julia

The education system is constantly growing and developing as more ways to teach and learn are implemented into the classroom. Recently, there has been a growing interest in teaching computational thinking with schools all over the world introducing it to the curriculum due to its ability to allow students to become proficient at problem solving using logic, an essential life skill. In order to provide the best education possible, it is imperative that computational thinking strategies, along with programming skills and the use of robotics in the classroom, be implemented in order for students to achieve maximum thought processing skills and computer competencies. The Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom is an all-encompassing reference book that discusses how computational thinking, programming, and robotics can be used in education as well as the benefits and difficulties of implementing these elements into the classroom. The book includes strategies for preparing educators to teach computational thinking in the classroom as well as design techniques for incorporating these practices into various levels of school curriculum and within a variety of subjects. Covering topics ranging from decomposition to robot learning, this book is ideal for educators, computer scientists, administrators, academicians, students, and anyone interested in learning more about how computational thinking, programming, and robotics can change the current education system.

Once a radical notion, object-oriented programming is one of today's most active research areas. It is especially well suited to the design of very large software projects involving many programmers all working on the same project. The original contributions in this book will provide researchers and students in programming languages, databases, and programming semantics with the most complete survey of the field available. Broad in scope and deep in its examination of substantive issues, the book focuses on the major topics of object-oriented languages, models of computation, mathematical models, object-oriented databases, and object-oriented environments. The object-oriented languages include Beta, the Scandinavian successor to Simula (a chapter by Bent Kristensen, whose group has had the longest experience with object-oriented programming, reveals how that experience has shaped the group's vision today); CommonObjects, a Lisp-based language with abstraction; Actors, a low-level language for concurrent modularity; and Vulcan, a Prolog-based concurrent object-oriented language. New computational models of inheritance, composite objects, block-structure layered systems, and classification are covered, and theoretical papers on functional object-oriented languages and object-oriented specification are included in the section on mathematical models. The three chapters on object-oriented databases (including David Maier's "Development and Implementation of an Object-Oriented Database Management System," which spans the programming and database worlds by integrating procedural and representational capability and the requirements of multi-user persistent storage) and the two chapters on object-oriented environments provide a representative sample of good research in these two important areas. Bruce Shriver is a researcher at IBM's Thomas J. Watson Research Center. Peter Wegner is a professor in the Department of Computer Science at Brown University. Research Directions in Object-Oriented Programmingis included in the Computer Systems series, edited by Herb Schwetman.

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

The influx of computer technology into classrooms during the past decade raises the questions -- how can we teach children to use computers productively and what effect will learning to program computers have on them? During this same period, researchers have investigated novice learning of computer programming. Teaching and Learning Computer Programming unites papers and perspectives by respected researchers of teaching and learning computer science while it summarizes and integrates major theoretical and empirical contributions. It gives a current and concise account of how instructional techniques affect student learning and how learning of programming affects students' cognitive skills. This collection is an ideal supplementary text for students and a valuable reference for professionals and researchers of education, technology and psychology, computer science, communication, developmental psychology, and industrial organization.

Programming is hard. Building a large program is like constructing a steam locomotive through a hole the size of a postage stamp. An artefact that is the fruit of hundreds of person-years is only ever seen by anyone through a lOO-line window. In some ways it is astonishing that such large systems work at all. But parallel programming is much, much harder. There are so many more things to go wrong. Debugging is a nightmare. A bug that shows up on one run may never happen when you are looking for it - but unfailingly returns as soon as your attention moves elsewhere. A large fraction of the program's code can be made up of marshalling and coordination algorithms. The core application can easily be obscured by a maze of plumbing. Functional programming is a radical, elegant, high-level attack on the programming problem. Radical, because it dramatically eschews side-effects; elegant, because of its close connection with mathematics; high-level, be cause you can say a lot in one line. But functional programming is definitely not (yet) mainstream. That's the trouble with radical approaches: it's hard for them to break through and become mainstream. But that doesn't make functional programming any less fun, and it has turned out to be a won derful laboratory for rich type systems, automatic garbage collection, object models, and other stuff that has made the jump into the mainstream.