This text serves as an introduction to the programming language Java for scientists and engineers, as well as experienced programmers wishing to learn Java as an additional language. The authors have specifically taken a hands-on approach to get the reader writing and running programs immediately. In addition, the book focuses on how Java, and object-oriented programming, can be used to solve science and engineering problems.

Essential Java serves as an introduction to the programming language, Java, for scientists and engineers, and can also be used by experienced programmers wishing to learn Java as an additional language. The book focuses on how Java, and object-oriented programming, can be used to solve science and engineering problems. Many examples are included from a number of different scientific and engineering areas, as well as from business and everyday life. Pre-written packages of code are provided to help in such areas as input/output, matrix manipulation and scientific graphing. Java source code and solutions to selected exercises are available at: www.bh.com/companions/0750659912 A free solutions manual is available to lecturers at: http://textbooks.elsevier.com * Takes a 'dive-in' approach, getting the reader writing and running programs immediately * Teaches object-oriented programming for problem-solving in engineering and science * Fully supported with online resources and listings to download

Java is currently enjoying immense success and is taught in hundreds of universities around the world. It is a modern, portable, object-oriented language and before long, it could also be the language of choice for many science and engineering students. Introductory Java for Scientists and Engineers provides an extremely accessible and thorough introduction to Java for science and engineering students. It takes the reader gradually through the language features, standard libraries and object orientation before moving on to discuss a scientific graphics library and a numerical library for Java. All the examples perform the kind of computations that will be of interest to a scientific programmer.

This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use PythonTM to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page. Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download at http://pythonessentials.com. Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.

Gibbons shows developers how to move a J2EE application to .NET at the enterprise level, with detailed and serious discussions of how to port Servlet, JSP or EJB-based applications to ASP.NET.

This volume contains 19 contributions from the International Symposium for Computational Science, 1999. Topics covered include delivery mechanisms for numerial algorithms, intelligent systems for recommending scientific software and the architecture of scientific problem-solving environments.

Essential MATLAB for Engineers and Scientists, Seventh Edition, provides a concise, balanced overview of MATLAB's functionality, covering both fundamentals and applications. The essentials are illustrated throughout, featuring complete coverage of the software's windows and menus. Program design and algorithm development are presented, along with many examples from a wide range of familiar scientific and engineering areas. This edition has been updated to include the latest MATLAB versions through 2018b. This is an ideal book for a first course on MATLAB, but is also ideal for an engineering problem-solving course using MATLAB. Updated to include all the newer features through MATLAB R2018b Includes new chapter on useful toolboxes Provides additional examples on engineering applications

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned. Learn one concept at a time: tackle complex topics in a series of small steps with examples Understand how to formulate problems, think creatively about solutions, and write programs clearly and accurately Determine which development techniques work best for you, and practice the important skill of debugging Learn relationships among input and output, decisions and loops, classes and methods, strings and arrays Work on exercises involving word games, graphics, puzzles, and playing cards

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

Bronson's second edition makes C++ accessible to first-level engineering students. The book teaches the fundamentals of the C++ language with a gradual refinement of programming skills from procedural to object-oriented. Part One presents procedural programming with an emphasis on modular program design. Part Two, on object-oriented programming, and Part Three, on data structures, are interchangeable to allow for teaching flexibility. In addition, students are introduced to the fundamentals of software engineering with an emphasis on problem-solving techniques, making the text an ideal choice for both one- and two-semester C++ programming courses.