
Federal Data Science
Transforming Government and Agricultural Policy Using Artificial Intelligence
Description
Key Features
- 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
Readership
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.
Table of Contents
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 YangSection 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. GuerreroSection 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
Product details
- No. of pages: 256
- Language: English
- Copyright: © Academic Press 2017
- Published: September 21, 2017
- Imprint: Academic Press
- eBook ISBN: 9780128124444
- Paperback ISBN: 9780128124437
About the Editors
Feras A. Batarseh
Affiliations and Expertise
Ruixin Yang
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
JackfromIBM Fri Nov 09 2018
Great for governments
Data science will change your government pretty soon, this book helps you understand why, how and what to do to harvest the benefits of data.
Reader1 Wed Jan 31 2018
Great and insightful
If you want to learn about the 'real' data science, get this book.