Data Science Applied to Sustainability Analysis
Language: English
Published by Elsevier Science Mai 2021, 2021
- Softcover
- New
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Seller Inventory # 9780128179765
- Title
- Data Science Applied to Sustainability Analysis
- Author
- Jennifer Dunn
- Publisher
- Elsevier Science Mai 2021
- Publication year
- 2021
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128179767
- ISBN 13
- 9780128179765
- Item weight
- 628 grams
- Dimensions
- 232x187x18 mm
Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas.
- Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery
- Includes considerations sustainability analysts must evaluate when applying big data
- Features case studies illustrating the application of data science in sustainability analyses
"Synopsis" may belong to another edition of this title.
About the Author
Prasanna Balaprakash is a computer scientist in the Mathematics and Computer Science Division with the joint appointment in the Leadership Computing Facility at Argonne National Laboratory. He is also a Fellow in the Northwestern-Argonne Institute of Science and Engineering of the Northwestern University. His research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. Currently, his research focus is on the automated design and development of scalable algorithms for solving large-scale problems that arise in scientific data analysis and in automating application performance modeling and tuning. He holds a Ph.D. in engineering sciences from CoDE-IRIDIA (AI Lab), Université libre de Bruxelles, Brussels, Belgium, where he was a Marie Curie fellow and later an FNRS Aspirant.
"About the title" may belong to another edition of this title.
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