applied data analysis modeling de reddy agami (39 resultados)

Condición: New. In.

paperback. Condición: New.

Condición: 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.

Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Paperback. Condición: New. Second Edition 2023. 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 notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.

Condición: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

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

Condición: New. pp. 632.

Condición: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Condición: New. pp. 632.

Paperback. Condición: new. Paperback. 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 notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.

Condición: New. pp. 632.

Condición: New. SUPER FAST SHIPPING.

Condición: New.

Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.

Hardcover. Condición: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 2.74.

Condición: Like New. Like New; Hardcover; This book is brand new and still sealed in the publisher's original shrinkwrap; One corner is slightly "bumped" through the plastic; This book will be stored and delivered in a sturdy cardboard box with foam padding; Large Format (Quatro, 10.75" - 11.75" tall); Red and blue covers with title in white lettering; 2011, Springer-Verlag Publishing; 430 pages; "Applied Data Analysis and Modeling for Energy Engineers and Scientists," by T. Agami Reddy.

Paperback. Condición: new. Paperback. 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 notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condición: New. In.

Condición: New.

Paperback. Condición: New. Second Edition 2023. 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 notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.

Hardcover. Condición: New. Brand New! Fast Delivery Textbooks and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Condición: New. pp. 430.

Paperback. Condición: new. Paperback. 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 notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author's own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the 'processes'along with the tools.

Paperback. Condición: Like New. Like New. book.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author's own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the 'processes'along with the tools.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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, r.