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This title is designed for undergraduate courses in computing or computer applications taken by engineering or science students. A brief introduction to basic computer concepts is followed by discussion of the various categories of software available for meeting the different types of tasks facing the engineer or scientist. The book includes coverage of spreadsheets, equation solving, database management, word processing, communication, graphics and utility.
The National Science Foundation funded a synthesis study on the status, contributions, and future direction of discipline-based education research (DBER) in physics, biological sciences, geosciences, and chemistry. DBER combines knowledge of teaching and learning with deep knowledge of discipline-specific science content. It describes the discipline-specific difficulties learners face and the specialized intellectual and instructional resources that can facilitate student understanding. Discipline-Based Education Research is based on a 30-month study built on two workshops held in 2008 to explore evidence on promising practices in undergraduate science, technology, engineering, and mathematics (STEM) education. This book asks questions that are essential to advancing DBER and broadening its impact on undergraduate science teaching and learning. The book provides empirical research on undergraduate teaching and learning in the sciences, explores the extent to which this research currently influences undergraduate instruction, and identifies the intellectual and material resources required to further develop DBER. Discipline-Based Education Research provides guidance for future DBER research. In addition, the findings and recommendations of this report may invite, if not assist, post-secondary institutions to increase interest and research activity in DBER and improve its quality and usefulness across all natural science disciples, as well as guide instruction and assessment across natural science courses to improve student learning. The book brings greater focus to issues of student attrition in the natural sciences that are related to the quality of instruction. Discipline-Based Education Research will be of interest to educators, policy makers, researchers, scholars, decision makers in universities, government agencies, curriculum developers, research sponsors, and education advocacy groups.
ESource--Prentice Hall's Engineering Source--provides a complete, flexible introductory engineering and computing program. Featuring over 15 modules and growing, ESource allows users to fully customize their series through the ESource website. Users are not only able to pick and choose modules, but also sections of modules, and re-paginate and re-index the complete project. For any Engineer or Computer Scientist interested in a complete, customized reference.
Explores best practices in assisting students in understanding engineering concepts through interactive and virtual environments.
Summary: "Today, the majority of engineers in many varied fields must utilize CAD/CAM systems in their work, but due to the increasing number and sophistication of programs and methods available, no one engineer can possibly be an expert in all of them. This book will help, by offering a detailed and comprehensive survey of all the leading computer-aided engineering methods, effectively providing a map to this sometimes confusing world. It is especially written for design and production engineers practicing in the modern industrial environment, where design, analysis, manufacturing planning, production planning and computer controlled equipment programming are all governed by CAD/CAM systems. The authors, who are engineering professors as well as IT professionals, clearly explain concepts, approaches, principles, and practical methods in purposefully IT-jargon free language, so that engineers will not get lost in a tangle of acronyms. It profides basic theoretical background and examines the relative value of various competitive computer-aided engineering methods, so that engineers will feel confident in making design tool choices, without having to become specialists in the development issues surrounding each system"--Back cover.
This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
This book helps you write computer solutions to mathematical problems at all levels. Now you can use your own computer to solve mathematical problems without having to rely on commercial software.
The focus of the papers presented in these proceedings is on employing various methodologies and approaches for solving real-life problems. Although the mechanisms that the human brain employs to solve problems are not yet completely known, we do have good insight into the functional processing performed by the human mind. On the basis of the understanding of these natural processes, scientists in the field of applied intelligence have developed multiple types of artificial processes, and have employed them successfully in solving real-life problems. The types of approaches used to solve problems are dependant on both the nature of the problem and the expected outcome. While knowledge-based systems are useful for solving problems in well-understood domains with relatively stable environments, the approach may fail when the domain knowledge is either not very well understood or changing rapidly. The techniques of data discovery through data mining will help to alleviate some problems faced by knowledge-based approaches to solving problems in such domains. Research and development in the area of artificial intelligence are influenced by opportunity, needs, and the availability of resources. The rapid advancement of Internet technology and the trend of increasing bandwidths provide an opportunity and a need for intelligent information processing, thus creating an excellent opportunity for agent-based computations and learning. Over 40% of the papers appearing in the conference proceedings focus on the area of machine learning and intelligent agents - clear evidence of growing interest in this area.
This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra.