Fundamental Algorithms Nyu
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Fundamental Algorithms for Computer Graphics
Author: Rae Earnshaw
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Algorithms provide the basic foundation for all computational processes. This volume presents algorithms at the foundational level and also at the various levels between this level and the user application. Some of these algorithms are classical and have become well established in the field. This material is therefore a rich source of information and is still relevant and up to date. The basic primitives of computer graphics have remained unchanged: lines, circles, conics, curves and characters. This volume contains reference material in all these areas. The higher levelsof contouring and surface drawing are also well covered. Developments in hardware architectures have continued since the first printing, but the basic principles of hardware/software trade-offs remain valid. This reprint is being published as a Study Edition to make the material more accessible to students and researchers in the field of computer graphics andits applications. The continuing popularity of the original book demonstrates the value and timeliness of its contents.
Fundamental Algorithms in Computational Fluid Dynamics
Author: Thomas H. Pulliam
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-03-31
Intended as a textbook for courses in computational fluid dynamics at the senior undergraduate or graduate level, this book is a follow-up to the book Fundamentals of Computational Fluid Dynamics by the same authors, which was published in the series Scientific Computation in 2001. Whereas the earlier book concentrated on the analysis of numerical methods applied to model equations, this new book concentrates on algorithms for the numerical solution of the Euler and Navier-Stokes equations. It focuses on some classical algorithms as well as the underlying ideas based on the latest methods. A key feature of the book is the inclusion of programming exercises at the end of each chapter based on the numerical solution of the quasi-one-dimensional Euler equations and the shock-tube problem. These exercises can be included in the context of a typical course and sample solutions are provided in each chapter, so readers can confirm that they have coded the algorithms correctly.
Data Science for Business
Author: Foster Provost
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2013-07-27
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates