Federal Data Science: Transforming Government and Agricultural Policy Using Artificial Intelligence, 1st Edition

  • Feras A. Batarseh
  • Ruixin Yang
  • Published By:
  • ISBN-10: 012812444X
  • ISBN-13: 9780128124444
  • DDC: 352.380285
  • Grade Level Range: College Freshman - College Senior
  • 256 Pages | eBook
  • Original Copyright 2018 | Published/Released June 2018
  • This publication's content originally published in print form: 2018

  • Price:  Sign in for price



This text 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. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
List of Contributors.
About the Editors.
Note From the Editors.
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 Government: Guidelines to Transforming Federal Software Systems.
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.
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 (NASA).
12: Intelligent Automation Tools and Software Engines for Managing Federal Agricultural Data.
13: Transforming Governmental Data Science Teams in the Future.