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| data science air force: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science air force: Strengthening U.S. Air Force Human Capital Management National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Human-Systems Integration, Committee on Strengthening U.S. Air Force Human Capital Management, 2021-03-02 The USAir Force human capital management (HCM) system is not easily defined or mapped. It affects virtually every part of the Air Force because workforce policies, procedures, and processes impact all offices and organizations that include Airmen and responsibilities and relationships change regularly. To ensure the readiness of Airmen to fulfill the mission of the Air Force, strategic approaches are developed and issued through guidance and actions of the Office of the Deputy Chief of Staff for Manpower, Personnel and Services and the Office of the Assistant Secretary of the Air Force for Manpower and Reserve Affairs. Strengthening US Air Force Human Capital Management assesses and strengthens the various U.S. Air Force initiatives and programs working to improve person-job match and human capital management in coordinated support of optimal mission capability. This report considers the opportunities and challenges associated with related interests and needs across the USAF HCM system as a whole, and makes recommendations to inform improvements to USAF personnel selection and classification and other critical system components across career trajectories. Strengthening US Air Force Human Capital Management offers the Air Force a strategic approach, across a connected HCM system, to develop 21st century human capital capabilities essential for the success of 21st century Airmen. |
| data science air force: Technology Innovation and the Future of Air Force Intelligence Analysis LANCE. METHE, Dahlia Anne Goldfeld, Abbie Tingstad, Sherrill Lingel, Edward Geist, 2021-01-27 There is growing demand for the Air Force Distributed Common Ground System (AF DCGS) to analyze sensor data. The authors assessed how new tools and technologies, including artificial intelligence and machine learning (AI/ML), can help meet these demands. The authors assessed AF DCGS tools and processes, surveyed the state of the art in AI/ML methods, and examined best practices to encourage innovation and to incorporate new tools. |
| data science air force: Autonomous Horizons Greg Zacharias, 2019-04-05 Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology. |
| data science air force: Data Science Gyanendra K. Verma, Badal Soni, Salah Bourennane, Alexandre C. B. Ramos, 2021-08-19 This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries. |
| data science air force: Leveraging Big Data Analytics to Improve Military Recruiting Nelson Lim, Bruce R. Orvis, Kimberly Curry Hall, 2019-11-30 The authors identified current, desired, and prospective data-enabled practices that the U.S. Department of Defense and the services might be able to deploy in their outreach and recruiting processes. |
| data science air force: Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions, 2017-02-06 The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R. |
| data science air force: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science air force: Data Science in Engineering, Volume 10 Ramin Madarshahian, François Hemez, 2023-12-07 Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection High-rate Structural Monitoring and Prognostics |
| data science air force: The Air Force Budget United States. Air Force. Office of Comptroller, 1977 |
| data science air force: Assessing Department of Defense Use of Data Analytics and Enabling Data Management to Improve Acquisition Outcomes Philip S. Antón, 2019 In the conference report accompanying the National Defense Authorization Act for Fiscal Year 2017, Congress expressed concern that the U.S. Department of Defense (DoD) does not sufficiently incorporate data into its acquisition-related learning and decision-making and asked six questions about the use of data analysis, measurement, and other evaluation-related methods in DoD acquisition programs. In this report, the authors decompose and measure acquisition functions, data governance, and training to assess how data and associated analytics support DoD acquisition decision making. The authors found that the DoD is applying a breadth of data analytics to acquisition. Capabilities range from simple data archives and plotting to archives integrated with commercial analytic tools. The DoD has implemented an array of data governance and management practices, but major challenges remain, including a culture against data sharing and concerns about security and oversight burden. Some commercial breakthroughs in advanced analytics sound promising for DoD acquisition, but some might not be applicable; research is ongoing. Advancement should include developing a data analytics strategy across acquisition domains, expanding data governance and data sharing, and continuing to expand and mature data collection, access, and analytic layers. Also, mechanisms are needed to authorize and ensure protected access to data for both the DoD and external analysts. Improved incentives and understanding of data analytics could encourage decision makers to make better use of capabilities -- Publisher's description. |
| data science air force: Becoming a Data Head Alex J. Gutman, Jordan Goldmeier, 2021-04-13 Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful. Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You’ve heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You’ll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what’s really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you’re a business professional, engineer, executive, or aspiring data scientist, this book is for you. |
| data science air force: Data Science in Engineering Vol. 10 Thomas Matarazzo, |
| data science air force: Analytics and Data Science Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones, 2017-10-05 This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science. |
| data science air force: Secure Data Science Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan, 2022-04-27 Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science. |
| data science air force: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| data science air force: The United States Air Force and the Culture of Innovation, 1945-1965 Stephen B. Johnson, 2002 |
| data science air force: Leadership in Statistics and Data Science Amanda L. Golbeck, 2021-03-22 This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture. |
| data science air force: Data Science And Knowledge Engineering For Sensing Decision Support - Proceedings Of The 13th International Flins Conference Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, 2018-07-30 FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions of the FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, with special focuses on data science and knowledge engineering for sensing decision support, both from the foundations and the applications points-of-view. |
| data science air force: The U.S. Air Force in Space, 1945 to the Twenty-First Century: Proceedings Air Force Historical Foundation. Symposium, 1998-09-02 Contains papers presented at the Air Force Historical Foundation Symposium, held at Andrews Air Force Base, Maryland, on September 21-22, 1995. Topics addressed are: Pt. 1, The Formative Years, 1945-1961; Pt. 2, Mission Development and Exploitation Since 1961; and Pt. 3, Military Space Today and Tomorrow. Includes notes, abbreviations & acronyms, an index, and photographs. |
| data science air force: Data Science for Nano Image Analysis Chiwoo Park, Yu Ding, 2021-07-31 This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies. |
| data science air force: Data Science in Engineering, Volume 9 Ramin Madarshahian, Francois Hemez, 2021-10-04 Data Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Data Science in Engineering Applications Engineering Mathematics Computational Methods in Engineering |
| data science air force: Air Force and Space Digest , 2015-07 |
| data science air force: Frontiers in Data Science Matthias Dehmer, Frank Emmert-Streib, 2017-10-16 Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone. |
| data science air force: Data Science and Analytics Usha Batra, Nihar Ranjan Roy, Brajendra Panda, 2020-05-27 This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics. |
| data science air force: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| data science air force: Monthly Catalog of United States Government Publications , 1965 |
| data science air force: Enabling AI Applications in Data Science Aboul-Ella Hassanien, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa, 2020-09-23 This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science. |
| data science air force: Learning to Love Data Science Mike Barlow, 2015-10-27 Until recently, many people thought big data was a passing fad. Data science was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you’ll find out how far data science reaches. With this anthology, you’ll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries. |
| data science air force: 2019 Missile Defense Review Department Of Defense, 2019-01-19 2019 Missile Defense Review - January 2019 According to a senior administration official, a number of new technologies are highlighted in the report. The review looks at the comprehensive environment the United States faces, and our allies and partners face. It does posture forces to be prepared for capabilities that currently exist and that we anticipate in the future. The report calls for major investments from both new technologies and existing systems. This is a very important and insightful report because many of the cost assessments for these technologies in the past, which concluded they were too expensive, are no longer applicable. Why buy a book you can download for free? We print this book so you don't have to. First you gotta find a good clean (legible) copy and make sure it's the latest version (not always easy). Some documents found on the web are missing some pages or the image quality is so poor, they are difficult to read. We look over each document carefully and replace poor quality images by going back to the original source document. We proof each document to make sure it's all there - including all changes. If you find a good copy, you could print it using a network printer you share with 100 other people (typically its either out of paper or toner). If it's just a 10-page document, no problem, but if it's 250-pages, you will need to punch 3 holes in all those pages and put it in a 3-ring binder. Takes at least an hour. It's much more cost-effective to just order the latest version from Amazon.com This book includes original commentary which is copyright material. Note that government documents are in the public domain. We print these large documents as a service so you don't have to. The books are compact, tightly-bound, full-size (8 1/2 by 11 inches), with large text and glossy covers. 4th Watch Publishing Co. is a HUBZONE SDVOSB. https: //usgovpub.com |
| data science air force: Recent Advances in Data Science Henry Han, Tie Wei, Wenbin Liu, Fei Han, 2020-09-28 This book constitutes selected papers of the Third International Conference on Data Science, Medicine and Bioinformatics, IDMB 2019, held in Nanning, China, in June 2019. The 19 full papers and 1 short paper were carefully reviewed and selected from 93 submissions. The papers are organized according to the following topical sections: business data science: fintech, management, and analytics.- health and biological data science.- novel data science theory and applications. |
| data science air force: Data Science Beiji Zou, Min Li, Hongzhi Wang, Xianhua Song, Wei Xie, Zeguang Lu, 2017-09-15 This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mining and Knowledge Management, Infrastructure for Data Science, Machine Learning for Data Science, Data Security and Privacy, Applications of Data Science, Case Study of Data Science, Multimedia Data Management and Analysis, Data-driven Scientific Research, Data-driven Bioinformatics, Data-driven Healthcare, Data-driven Management, Data-driven eGovernment, Data-driven Smart City/Planet, Data Marketing and Economics, Social Media and Recommendation Systems, Data-driven Security, Data-driven Business Model Innovation, Social and/or organizational impacts of Data Science. |
| data science air force: Federal Data Science Feras A. Batarseh, Ruixin Yang, 2017-09-21 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 |
| data science air force: Source Hierarchy List: E through N , 1990 |
| data science air force: Artificial Intelligence for Data Science in Theory and Practice Mohamed Alloghani, Christopher Thron, Saad Subair, 2022-04-05 This book provides valuable information on effective, state-of-the-art techniques and approaches for governments, students, researchers, practitioners, entrepreneurs and teachers in the field of artificial intelligence (AI). The book explains the data and AI, types and properties of data, the relation between AI algorithms and data, what makes data AI ready, steps of data pre-processing, data quality, data storage and data platforms. Therefore, this book will be interested by AI practitioners, academics, researchers, and lecturers in computer science, artificial intelligence, machine learning and data sciences. |
| data science air force: Why Data Science Projects Fail Douglas Gray, Evan Shellshear, 2024-09-05 The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether. For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics. |
| data science air force: Data Smart Jordan Goldmeier, 2023-09-22 A straightforward and engaging approach to data science that skips the jargon and focuses on the essentials In the newly revised second edition of Data Smart: Using Data Science to Transform Information into Insight, accomplished data scientist and speaker Jordan Goldmeier delivers an approachable and conversational approach to data science using Microsoft Excel’s easily understood features. The author also walks readers through the fundamentals of statistics, machine learning and powerful artificial intelligence concepts, focusing on how to learn by doing. You’ll also find: Four-color data visualizations that highlight and illustrate the concepts discussed in the book Tutorials explaining complicated data science using just Microsoft Excel How to take what you’ve learned and apply it to everyday problems at work and life A must-read guide to data science for every day, non-technical professionals, Data Smart will earn a place on the bookshelves of students, analysts, data-driven managers, marketers, consultants, business intelligence analysts, demand forecasters, and revenue managers. |
| data science air force: Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition) Graham L Giller, 2022-06-27 This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went 'viral' before anybody knew what that meant, on being the person who forgot to hit 'enter' to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality.The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as 'The Pleasure of Finding Things Out.' |
| data science air force: Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track Yuxiao Dong, Dunja Mladenić, Craig Saunders, 2021-02-24 The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. |
| data science air force: U.S. Government Research Reports , 1962 |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
