Data Science In Context

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Public Policy Analytics

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Author : Ken Steif
language : en
Publisher: Chapman & Hall/CRC Data Science Series
Release Date : 2021

Public Policy Analytics written by Ken Steif and has been published by Chapman & Hall/CRC Data Science Series this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Political planning categories.

Public Policy Analytics teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. This book will appeal to researchers and data scientists at the intersection of data analytics and public policy, and readers who wish to understand how algorithms will affect the future of government.

Data Science In Context

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Author : Alfred Z. Spector
language : en
Publisher: Cambridge University Press
Release Date : 2022-10-20

Data Science In Context written by Alfred Z. Spector and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Computers categories.

Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.

Context Aware Machine Learning And Mobile Data Analytics

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Author : Iqbal Sarker
language : en
Publisher:
Release Date : 2021

Context Aware Machine Learning And Mobile Data Analytics written by Iqbal Sarker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.

Foundations Of Data Science

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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23

Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Leveraging Data Science For Global Health

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Author : Leo Anthony Celi
language : en
Publisher: Springer Nature
Release Date : 2020-07-31

Leveraging Data Science For Global Health written by Leo Anthony Celi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Medical categories.

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

R For Data Science

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12

R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Science In The Context Of Application

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Author : Martin Carrier
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-12

Science In The Context Of Application written by Martin Carrier and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-12 with Science categories.

We increasingly view the world around us as a product of science and technology. Accordingly, we have begun to appreciate that science does not take its problems only from nature and then produces technological applications, but that the very problems of scientific research themselves are generated by science and technology. Simultaneously, problems like global warming, the toxicology of nanoparticles, or the use of renewable energies are constituted by many factors that interact with great complexity. Science in the context of application is challenged to gain new understanding and control of such complexity—it cannot seek shelter in the ivory tower or simply pursue its internal quest for understanding and gradual improvement of grand theories. Science in the Context of Application will identify, explore and assess these changes. Part I considers the "Changing Conditions of Scientific Research" and part II "Science, Values, and Society". Examples are drawn from pharmaceutical research, the information sciences, simulation modelling, nanotechnology, cancer research, the effects of commercialization, and many other fields. The book assembles papers from well-known European and American Science Studies scholars like Bernadette Bensaude-Vincent, Janet Kourany, Michael Mahoney, Margaret Morrison, Hans-Jörg Rheinberger, Arie Rip, Dan Sarewitz, Peter Weingart, and others. The individual chapters are written to address anyone who is concerned about the role of contemporary science in society, including scientists, philosophers, and policy makers.

Developing Focus Group Research

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Author : Jenny Kitzinger
language : en
Publisher: SAGE
Release Date : 1999-02-22

Developing Focus Group Research written by Jenny Kitzinger and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-02-22 with Social Science categories.

This book critically examines the potential of, and suggests ways forward in, harnessing a versatile and powerful method of research - focus groups. The book challenges some of the emerging orthodoxies and presents accessible, insightful and reflective discussions about the issues around focus group work. The contributors, an impressive group of experienced researchers from a range of disciplines and traditions, discuss different ways of designing, conducting and analyzing focus group research. They examine sampling strategies; the implications of combining focus groups with other methods; accessing views of `minority' groups; their contribution to participatory or feminist research; use of software packages; discourse anal

Sql For Data Science

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Author : Antonio Badia
language : en
Publisher: Springer Nature
Release Date : 2020-11-09

Sql For Data Science written by Antonio Badia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-09 with Computers categories.

This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.

Foundations Of Data Science For Engineering Problem Solving

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Author : Parikshit Narendra Mahalle
language : en
Publisher:
Release Date : 2022

Foundations Of Data Science For Engineering Problem Solving written by Parikshit Narendra Mahalle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.