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Programming with Data
Here is a thorough and authoritative guide to the latest version of the S language and its programming environment. Programming With Data describes a new and greatly extended version of S, written by the chief designer of the language itself. It is a guide to the complete programming process, starting from simple, interactive use, and continuing through ambitious software projects. The focus is on the needs of the programmer/user, with the aim of turning ideas into software, quickly and faithfully. The new version of S provides a powerful class/method structure, new techniques to deal with large objects, extended interfaces to other languages and files, object-based documentation compatible with HTML, and powerful new interactive programming techniques. This version of S underlies the S-Plus system, versions 5.0 and higher.
R 1,586.80


Introduction to Parallel Programming
In modern computer science, there exists no truly sequential computing system; and most advanced programming is parallel programming. This is particularly evident in modern application domains like scientific computation, data science, machine intelligence, etc. This lucid introductory textbook will be invaluable to students of computer science and technology, acting as a self-contained primer to parallel programming. It takes the reader from introduction to expertise, addressing a broad gamut of issues. It covers different parallel programming styles, describes parallel architecture, includes parallel programming frameworks and techniques, presents algorithmic and analysis techniques and discusses parallel design and performance issues. With its broad coverage, the book can be useful in a wide range of courses; and can also prove useful as a ready reckoner for professionals in the field.
R 2,588.30

Introduction to Linear Programming
Stressing the use of several software packages based on simplex method variations, this text teaches linear programming's four phases through actual practice. It shows how to decide whether LP models should be applied, set up appropriate models, use software to solve them, and examine solutions to a
R 6,684.29




