Federal Data Science: Transforming Government and Agricultural Policy Using Artificial Intelligence
Language: English
Published by Academic Press, 2017
- Softcover
- New
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- Title
- Federal Data Science: Transforming Government and Agricultural Policy Using Artificial Intelligence
- Author
- Batarseh, Feras A.
- Publisher
- Academic Press
- Publication year
- 2017
- Condition
- new
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0128124431
- ISBN 13
- 9780128124437
- Item weight
- 0.43 kilograms
Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making.
No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective.
- Offers a range of data science models, engineering tools, and federal use-cases
- Provides foundational observations into government data resources and requirements
- Introduces experiences and examples of data openness from the US and other countries
- A step-by-step guide for the conversion of government towards data-driven policy making
- Focuses on presenting data models that work within the constraints of the US government
- Presents the why, the what, and the how of injecting AI into federal culture and software systems
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Batarseh is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), the Agricultural and Applied Economical Association (AAEA), and the Association for the Advancement of Artificial Intelligence (AAAI). He has taught AI and Data Science courses at multiple universities including George Mason University (GMU), University of Maryland - Baltimore County (UMBC), Georgetown University, and George Washington University (GWU).
Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), a Juris Masters of Law from GMU (2022), and a Graduate Certificate in Project Leadership from Cornell University (2016). He currently holds courtesy appointments with the Center for Advanced Innovation in Agriculture (CAIA), National Security Institute (NSI), and the Department of Electrical and Computer Engineering at VT.
Ruixin Yang is an Associate Professor in the Department of Geography and GeoInformation Sciences (GGS) ― College of Science at George Mason University (GMU), Fairfax, VA. He received his PhD in Aerospace Engineering from University of Southern California (USC) in 1990. His research work ranged from Fluid Dynamics to Astrophysics and General Relativity to Data Science, Information Systems, Data Mining, and Earth Systems Science. Dr. Yang led a software development team that built several prototypes for earth science information systems. His recent research is focused on data mining methods for hurricane-related earth science. He has published several referred papers on earth science data search, online analysis, metadata management, content-based search, and big data analytics.
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Brook Bookstore On Demand
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