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Federal Data Science
Transforming Government and Agricultural Policy Using Artificial Intelligence
- 1st Edition - September 21, 2017
- Editors: Feras A. Batarseh, Ruixin Yang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 2 4 4 3 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 2 4 4 4 - 4
Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying… Read more
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Request a sales quoteFederal 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
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.
Section 1: Injecting Artificial Intelligence into Governmental Systems
1. A Day in the Life of a Federal Analyst and a Federal Contractor
Feras A. Batarseh
2. Disseminating Government Data Effectively in the Age of Open Data
Mirvat Sewadeh and Jeffrey Sisson
3. Machine Learning for the Government: Challenges and Statistical Difficulties
Sam Eisenberg
4. Making the Case for Artificial Intelligence at the Government: Guidelines to Transforming Federal Software Systems
Feras A. Batarseh and Ruixin Yang
Section 2: Governmental Data Science Solutions Around the World
5. Agricultural Data Analytics for Environmental Monitoring in Canada
Ted Huffman, Morten Olesen, Melodie Green, Don Leckie, Jiangui Liu, and Jiali Shang
6. France’s Governmental Big Data Analytics: From Predictive to Prescriptive Using R
Henri Laude
7. Agricultural Remote Sensing and Data Science in China
Zhongxin Chen, Haizhu Pan, Changan Liu, and Zhiwei Jiang
8. Data Visualization of Complex Information Through Mind Mapping in Spain and the European Union
Jose M. Guerrero
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
Feras A. Batarseh and Ruixin Yang
10. Federal Big Data Analytics in the Health Domain: An Ontological Approach to Data Interoperability
Erik W. Kuiler and Connie L. McNeely
11. Geospatial Data Discovery, Management, and Analysis at National Aeronautics and Space Administration
Manzhu Yu and Min Sun
12. Intelligent Automation Tools and Software Engines for Managing Federal Agricultural Data
Feras A. Batarseh, Gowtham Ramamoorthy, Manish Dashora, and Ruixin Yang
13. Transforming Governmental Data Science Teams in the Future
Jay Gendron, Tammy Crane, Steve Mortimer, and Candace Eshelman-Haynes
- No. of pages: 256
- Language: English
- Edition: 1
- Published: September 21, 2017
- Imprint: Academic Press
- Paperback ISBN: 9780128124437
- eBook ISBN: 9780128124444
FB
Feras A. Batarseh
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.
Affiliations and expertise
Associate Professor, Department of Biological Systems Engineering at Virginia Tech (VT) USARY
Ruixin Yang
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.
Affiliations and expertise
Associate Professor, College of Science, Geography and Geoinformation Services, George Mason UniversityRead Federal Data Science on ScienceDirect