applied data analysis modeling von henze gregor (11 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Zustand: Brand New. 2nd edition. 630 pages. 11.02x8.26x11.02 inches. In Stock.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. pp. 632.

Taschenbuch. Zustand: Neu. Neuware -Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature.Applied Data Analysis and Modeling for Energy Engineers and Scientists is designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter not Elektronisches Buch and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 632 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature.Applied Data Analysis and Modeling for Energy Engineers and Scientistsis designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter not Elektronisches Buch and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.

Buch. Zustand: Neu. Neuware -Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature.Applied Data Analysis and Modeling for Energy Engineers and Scientists is designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter not Elektronisches Buch and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 632 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature.Applied Data Analysis and Modeling for Energy Engineers and Scientistsis designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter not Elektronisches Buch and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.