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Federal Data Science

Federal Data Science Book
Author : Feras A. Batarseh,Ruixin Yang
Publisher : Academic Press
Release : 2017-09-21
ISBN : 012812444X
Language : En, Es, Fr & De

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Book Description :

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

Exam Prep for Federal Data Science

Exam Prep for  Federal Data Science Book
Author : Anonim
Publisher : Unknown
Release : 2021-04-15
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Exam Prep for Federal Data Science book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

97 Things About Ethics Everyone in Data Science Should Know

97 Things About Ethics Everyone in Data Science Should Know Book
Author : Bill Franks
Publisher : O'Reilly Media
Release : 2020-08-06
ISBN : 149207263X
Language : En, Es, Fr & De

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Book Description :

Most of the high-profile cases of real or perceived unethical activity in data science aren’t matters of bad intent. Rather, they occur because the ethics simply aren’t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Concept—Tim Wilson How to Approach Ethical Transparency—Rado Kotorov Unbiased ≠ Fair—Doug Hague Rules and Rationality—Christof Wolf Brenner The Truth About AI Bias—Cassie Kozyrkov Cautionary Ethics Tales—Sherrill Hayes Fairness in the Age of Algorithms—Anna Jacobson The Ethical Data Storyteller—Brent Dykes Introducing Ethicize™, the Fully AI-Driven Cloud-Based Ethics Solution!—Brian O’Neill Be Careful with "Decisions of the Heart"—Hugh Watson Understanding Passive Versus Proactive Ethics—Bill Schmarzo

Data Science for Librarians

Data Science for Librarians Book
Author : Yunfei Du,Hammad Rauf Khan
Publisher : ABC-CLIO
Release : 2020-03-26
ISBN : 1440871221
Language : En, Es, Fr & De

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Book Description :

This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Skills such as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design. Reviews fundamental concepts and principles of data science Offers a practical overview of tools and software Highlights skills and services needed in the 21st-century academic library Covers the entire research data life cycle and the librarian's role at each stage Provides insight into how library science and data science intersect

Data Science in the Public Interest Improving Government Performance in the Workforce

Data Science in the Public Interest  Improving Government Performance in the Workforce Book
Author : Joshua D. Hawley
Publisher : W.E. Upjohn Institute
Release : 2020-07-22
ISBN : 0880996749
Language : En, Es, Fr & De

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Book Description :

This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making.

Roundtable on Data Science Postsecondary Education

Roundtable on Data Science Postsecondary Education Book
Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics
Publisher : National Academies Press
Release : 2020-10-02
ISBN : 030967770X
Language : En, Es, Fr & De

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Book Description :

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Data Science for Business and Decision Making

Data Science for Business and Decision Making Book
Author : Luiz Paulo Fávero,Patrícia Belfiore
Publisher : Academic Press
Release : 2019-03-08
ISBN : 9780128112168
Language : En, Es, Fr & De

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Book Description :

Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Unicorn Data Scientist the Rarest of Breeds

Unicorn Data Scientist  the Rarest of Breeds Book
Author : Anonim
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Abstract : Purpose: Many organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors' pursuit of these elusive mythical creatures. Design/methodology/approach: Qualitative data were collected through semi-structured interviews with managers/directors from nine Australian state and federal government agencies with relatively mature data science functions. Findings: Although the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities. Research limitations/implications: Given that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms. Originality/value: The skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.

Data Science Tools

Data Science Tools Book
Author : Christopher Greco
Publisher : Stylus Publishing, LLC
Release : 2020-05-30
ISBN : 1683925823
Language : En, Es, Fr & De

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Book Description :

In the world of data science there are myriad tools available to analyze data. This book describes some of the popular software application tools along with the processes for downloading and using them in the most optimum fashion. The content includes data analysis using Microsoft Excel, KNIME, R, and OpenOffice (Spreadsheet). Each of these tools will be used to apply statistical concepts including confidence intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysis using real data from Federal Government sources. Features: Analyzes data using popular applications such as Excel, R, KNIME, and OpenOffice Covers statistical concepts including confidence intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysis Capstone exercises analyze data using the different software packages

Analytics Data Science and Artificial Intelligence

Analytics  Data Science  and Artificial Intelligence Book
Author : Ramesh Sharda,Dursun Delen,Efraim Turban
Publisher : Unknown
Release : 2020-03-06
ISBN : 9781292341552
Language : En, Es, Fr & De

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Book Description :

For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications Book
Author : Philipp Kats,David Katz
Publisher : Packt Publishing Ltd
Release : 2019-08-30
ISBN : 1789533066
Language : En, Es, Fr & De

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Book Description :

Understand the constructs of the Python programming language and use them to build data science projects Key Features Learn the basics of developing applications with Python and deploy your first data application Take your first steps in Python programming by understanding and using data structures, variables, and loops Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python Book Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learn Code in Python using Jupyter and VS Code Explore the basics of coding – loops, variables, functions, and classes Deploy continuous integration with Git, Bash, and DVC Get to grips with Pandas, NumPy, and scikit-learn Perform data visualization with Matplotlib, Altair, and Datashader Create a package out of your code using poetry and test it with PyTest Make your machine learning model accessible to anyone with the web API Who this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.

Game Theory for Data Science

Game Theory for Data Science Book
Author : Boi Faltings,Goran Radanovic
Publisher : Morgan & Claypool Publishers
Release : 2017-09-19
ISBN : 1681731959
Language : En, Es, Fr & De

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Book Description :

Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.

Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare Book
Author : Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Publisher : Academic Press
Release : 2020-10-23
ISBN : 0128193158
Language : En, Es, Fr & De

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Book Description :

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Exam Prep for Handbook of Big Data Analytics in Life

Exam Prep for  Handbook of Big Data Analytics in Life     Book
Author : Anonim
Publisher : Unknown
Release : 2021-04-15
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Exam Prep for Handbook of Big Data Analytics in Life book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Business Intelligence

Business Intelligence Book
Author : Ramesh Sharda,Dursun Delen,Efraim Turban
Publisher : Pearson
Release : 2017-01-13
ISBN : 9780134633282
Language : En, Es, Fr & De

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Book Description :

For courses on Business Intelligence or Decision Support Systems. A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.

Soft Computing in Data Science

Soft Computing in Data Science Book
Author : Michael W. Berry,Azlinah Hj. Mohamed,Bee Wah Yap
Publisher : Springer
Release : 2016-09-17
ISBN : 9811027773
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.

IoT Based Data Analytics for the Healthcare Industry

IoT Based Data Analytics for the Healthcare Industry Book
Author : Sanjay Kumar Singh,Ravi Shankar Singh,Anil Kumar Pandey,Sandeep S Udmale,Ankit Chaudhary
Publisher : Academic Press
Release : 2020-12-01
ISBN : 0128214767
Language : En, Es, Fr & De

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Book Description :

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization. The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand. Provides state-of-art methods and current trends in data analytics for the healthcare industry Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques Discusses several potential AI techniques developed using IoT for the healthcare industry Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages

Intelligent Data Analytics for Decision Support Systems in Hazard Mitigation

Intelligent Data Analytics for Decision Support Systems in Hazard Mitigation Book
Author : Ravinesh C. Deo,Pijush Samui,Ozgur Kisi,Zaher Mundher Yaseen
Publisher : Springer Nature
Release : 2020-09-09
ISBN : 9811557721
Language : En, Es, Fr & De

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Book Description :

This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and environmental sciences.

Statistics for Data Science and Policy Analysis

Statistics for Data Science and Policy Analysis Book
Author : Azizur Rahman
Publisher : Springer Nature
Release : 2020-03-31
ISBN : 9811517355
Language : En, Es, Fr & De

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Book Description :

This book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019. Written by leading international experts in the field of statistics, data science and policy evaluation. This book explores the theme of effective policy methods through the use of big data, accurate estimates and modern computing tools and statistical modelling.

Data Science for COVID 19

Data Science for COVID 19 Book
Author : Utku Kose,Deepak Gupta,Victor Hugo Costa de Albuquerque,Ashish Khanna
Publisher : Academic Press
Release : 2021-04-01
ISBN : 0128245379
Language : En, Es, Fr & De

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Book Description :

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation. Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and treatment of the COVID-19 virus Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including both positive and negative research findings Provides insights into innovative data-oriented modeling and predictive techniques from COVID-19 researchers Includes real-world feedback and user experiences from physicians and medical staff from around the world on the effectiveness of applied Data Science solutions