Buch, Englisch, 256 Seiten, Format (B × H): 150 mm x 226 mm, Gewicht: 431 g
Transforming Government and Agricultural Policy Using Artificial Intelligence
Buch, Englisch, 256 Seiten, Format (B × H): 150 mm x 226 mm, Gewicht: 431 g
ISBN: 978-0-12-812443-7
Verlag: Elsevier Health Sciences
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.
Zielgruppe
<p>1. Data managers, software engineers, and database administrators at the government (at agencies aiming to inject data science into its operations). </p> <p>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).</p> <p>3. Students and scholars in majors such as: Big Data Analytics, Science and Technology Policy Making.</p> <p>4. USDA economic and agricultural analysts. Especially ones who perform statistical studies.</p> <p>5. Science and Technology policy makers, government officials, and journalists.</p>
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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