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Kenneth R. Baker ... 392 pages - Publisher: Wiley; 3rd edition (July, 2015) ... Language: English - ISBN-10: 1118937694 - ISBN-13: 978-1118937693.

Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software.

The Third Edition includes many practical applications of optimization models as well as a systematic framework that illuminates the common structures found in many successful models. With focused coverage on linear programming, nonlinear programming, integer programming, and heuristic programming, Optimization Modeling with Spreadsheets, Third Edition features: An emphasis on model building using Excel Solver as well as appendices with additional instructions on more advanced packages such as Analytic Solver Platform and OpenSolver * Additional space devoted to formulation principles and model building as opposed to algorithms * New end-of-chapter homework exercises specifically for novice model builders * Presentation of the Sensitivity Toolkit for sensitivity analysis with Excel Solver * Classification of problem types to help readers see the broader possibilities for application * Specific chapters devoted to network models and data envelopment analysis * A companion website with interactive spreadsheets and supplementary homework exercises for additional practice. Optimization Modeling with Spreadsheets, Third Edition is an excellent textbook for upper-undergraduate and graduate-level courses that include deterministic models, optimization, spreadsheet modeling, quantitative methods, engineering management, engineering modeling, operations research, and management science. The book is an ideal reference for readers wishing to advance their knowledge of Excel and modeling and is also a useful guide for MBA students and modeling practitioners in business and non-profit sectors interested in spreadsheet optimization.

Olaf Wolkenhauer  ... 296 pages - Publisher: Wiley-Interscience; (July, 2001) ... Language: English - ISBN-10: 0471416568 - ISBN-13: 978-0471416562.

A survey of the philosophical implications and practical applications of fuzzy systems: Fuzzy mathematical concepts such as fuzzy sets, fuzzy logic, and similarity relations represent one of the most exciting currents in modern engineering and have great potential in applications ranging from control theory to bioinformatics. Data Engineering guides the reader through a number of concepts interconnected by fuzzy mathematics and discusses these concepts from a systems engineering perspective to showcase the continuing vitality, attractiveness, and applicability of fuzzy mathematics.

The author discusses the fundamental aspects of data analysis, systems modeling, and uncertainty calculi. He avoids a narrow discussion of specialized methodologies and takes a holistic view of the nature and application of fuzzy systems, considering principles, paradigms, and methodologies along the way. This broad coverage includes: * Fundamentals of modeling, identification, and clustering * System analysis * Uncertainty techniques * Random-set modeling and identification * Fuzzy inference engines * Fuzzy classification, control, and mathematics. In the important emerging field of bioinformatics, the book sets out how to encode a natural system in mathematical models, describes methods to identify interrelationships and interactions from data, and thereby helps the practitioner to decide which variables to measure and why. Data Engineering serves as an up-to-date and informative survey of the theoretical and practical tools for analyzing complex systems. It offers a unique treatment of complex issues that is accessible to students and researchers from a variety of backgrounds.

Peter Bruce, Andrew Bruce ... 320 pages - Publisher: O'Reilly; (May, 2017) ... Language: English - ISBN-10: 1491952962 - ISBN-13: 978-1491952962. 

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science - How random sampling can reduce bias and yield a higher quality dataset, even with big data - How the principles of experimental design yield definitive answers to questions - How to use regression to estimate outcomes and detect anomalies - Key classification techniques for predicting which categories a record belongs to - Statistical machine learning methods that “learn” from data - Unsupervised learning methods for extracting meaning from unlabeled data.

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