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
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
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
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:
- © Academic Press 2018
- 22nd September 2017
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Feras A. Batarseh is a Research Assistant Professor in the College of Science at George Mason University (GMU). His research spans the areas of Data Science, Artificial Intelligence, Federal Data Analytics, and Context-Aware Software Systems. Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (in 2007 and 2011); and a Graduate Certificate in Project Leadership from Cornell University (in 2016). His research work has been published at prestigious journals and international conferences, as well as several book chapters. He taught data science and software engineering courses at multiple universities including GMU, UCF as well as George Washington University (GWU). Prior to joining GMU, Dr. Batarseh was a Program Manager with the Data Mining and Advanced Analytics team at MicroStrategy. Throughout his career, Dr. Batarseh worked with multiple federal and state government agencies on a variety of data science applications and analytical deployments. Additionally, he helped several organizations develop intelligent systems, make sense of their data, and gain insights into improving their operations. He is currently working with the Economic Research Services (ERS at the US Department of Agriculture) towards building intelligent data management systems. He is a member of IEEE, ACM, BICA and STP professional communities. For more information and contact details, please refer to his webpage: https://cos.gmu.edu/ggs/people/faculty-staff/feras-batarseh-2/.
Research Assistant Professor, College of Science, George Mason University
Ruixin Yang is an Associate Professor in the Department of Geography and GeoInformation Sciences (GGS) - College of Science at George Mason University. He received his PhD in Aerospace Engineering from University of Southern California in 1990. His research areas ranged from Fluid Dynamics, to Astrophysics and General Relativity, to Data Science, Data Information Systems, Data Analysis, Data Mining, and Earth Systems Science. On software engineering, Dr. Yang led a software development team that built several prototypes for earth science data information systems. The systems include Virtual Domain Application Data Center (VDADC) prototype system for on-line Earth science data analysis through Web technology; Seasonal to Inter-annual Earth Science Information Partner (SIESIP) online data search and analysis system, which integrates Oracle Database Management System, Java web technology, and GrADS, a data analysis and visualization software package; and an XML-based Distributed Metadata Server (DIMES) for representing, storing, retrieving and interoperating metadata in a distributed environment. Recently, Dr. Yang’s research is focused on exploratory data analysis and/or data mining with advanced data analysis methods to hurricane related earth science data. He has published several referred papers on earth science data search, access, online analysis, metadata management, content-based search, bid data analytics, and data mining for rapidly intensifying tropical cyclones. He can be reached at: firstname.lastname@example.org.
Associate Professor, College of Science, Geography and Geoinformation Services, George Mason University