Federal Data Science

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
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