Federal Data Science
Transforming Government and Agricultural Policy Using Artificial Intelligence
Published on 22. September 2017
Book
Paperback/Softback
256 pages
978-0-12-812443-7 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
1. Data managers, software engineers, and database administrators at the government (at agencies aiming to inject data science into its operations).
2. Industry's data science consultants and specialists who build analytical projects for the government (ones from vendors such as: Salient, Tableau, SAS, SPSS, Oracle, Microsoft, MicroStrategy, and IBM).
3. Students and scholars in majors such as: Big Data Analytics, Science and Technology Policy Making.
4. USDA economic and agricultural analysts. Especially ones who perform statistical studies.
5. Science and Technology policy makers, government officials, and journalists.
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 226 mm
Width: 150 mm
Thickness: 18 mm
Weight
431 gr
ISBN-13
978-0-12-812443-7 (9780128124437)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Feras A. Batarseh | Ruixin Yang
Federal Data Science
Transforming Government and Agricultural Policy Using Artificial Intelligence
E-Book
09/2017
Academic Press
€91.95
Available for download
Persons
Feras A. Batarseh is an Associate Professor with the Department of Biological Systems Engineering at Virginia Tech (VT) and the Director of A3 (AI Assurance and Applications) Lab. His research spans the areas of AI Assurance, Cyberbiosecurity, AI for Agriculture and Water, and Data-Driven Public Policy. His work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published multiple chapters and books, his two recent books are: "Federal Data Science", and "Data Democracy", both by Elsevier's Academic Press.
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.
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.
Content
Section 1: Injecting Artificial Intelligence into Governmental Systems
1. A Day in the Life of a Federal Analyst and a Federal Contractor
2. Disseminating Government Data Effectively in the Age of Open Data
3. Machine Learning for the Government: Challenges and Statistical Difficulties
4. Making the Case for Artificial Intelligence at the Government: Guidelines to Transforming Federal Software
Section 2: Governmental Data Science Solutions Around the World
5. Agricultural Data Analytics for Environmental Monitoring in Canada
6. France's Governmental Big Data Analytics: From Predictive to Prescriptive Using R
7. Agricultural Remote Sensing and Data Science in China
8. Data Visualization of Complex Information Through Mind Mapping in Spain and the European Union
Section 3: Federal Data Science Use Cases at the US Government
9. A Deployment Life Cycle Model for Agricultural Data Systems Using Kansei Engineering and Association Rules
10. Federal Big Data Analytics in the Health Domain: An Ontological Approach to Data Interoperability
11. Geospatial Data Discovery, Management, and Analysis at National Aeronautics and Space Administration
12. Intelligent Automation Tools and Software Engines for Managing Federal Agricultural Data
13. Transforming Governmental Data Science Teams in the Future
1. A Day in the Life of a Federal Analyst and a Federal Contractor
2. Disseminating Government Data Effectively in the Age of Open Data
3. Machine Learning for the Government: Challenges and Statistical Difficulties
4. Making the Case for Artificial Intelligence at the Government: Guidelines to Transforming Federal Software
Section 2: Governmental Data Science Solutions Around the World
5. Agricultural Data Analytics for Environmental Monitoring in Canada
6. France's Governmental Big Data Analytics: From Predictive to Prescriptive Using R
7. Agricultural Remote Sensing and Data Science in China
8. Data Visualization of Complex Information Through Mind Mapping in Spain and the European Union
Section 3: Federal Data Science Use Cases at the US Government
9. A Deployment Life Cycle Model for Agricultural Data Systems Using Kansei Engineering and Association Rules
10. Federal Big Data Analytics in the Health Domain: An Ontological Approach to Data Interoperability
11. Geospatial Data Discovery, Management, and Analysis at National Aeronautics and Space Administration
12. Intelligent Automation Tools and Software Engines for Managing Federal Agricultural Data
13. Transforming Governmental Data Science Teams in the Future