Data Analytics For The Social Sciences
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Data Analytics For The Social Sciences
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Author : G. David Garson
language : en
Publisher: Routledge
Release Date : 2021-11-29
Data Analytics For The Social Sciences written by G. David Garson and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Psychology categories.
Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.
Data Science And Social Research
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Author : N. Carlo Lauro
language : en
Publisher: Springer
Release Date : 2017-11-17
Data Science And Social Research written by N. Carlo Lauro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-17 with Social Science categories.
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
Grey Data Analytics For Management And Social Sciences
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Author : RAJESH, R.
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2025-04-14
Grey Data Analytics For Management And Social Sciences written by RAJESH, R. and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
This book covers details of various grey-based models and methodologies and the practical applications of the same, with specific focus on the management and the social sciences domains. The step-by-step implementation aspects, which is followed in this book help readers to implement the methodologies with ease. Undergraduate and postgraduate students of business management and social sciences domains can be benefited through the practical and case-based approach charted in the book. Also, the Excel-based implementation aspects with screenshots of the calculations and the formulas, along with the downloadable Excel files can help the users to replicate the results as given. Apart from this, the implementation aspects can assist them in advocating these methodologies to diverse research problems. A wide variety of applications of the grey system-based methods, such as the grey relational analysis, grey prediction, grey clustering, grey programming, grey target decision-making, grey incidence analysis, and other combined grey-based models are elaborated and detailed in this book. KEY FEATURES • Several grey-based models and methodologies are uncovered • Practical and case-based approach is followed • Excel-based implementation with demonstration can benefit readers • Calculation sheets are made available and downloadable for users • Specific focus to quantitative social sciences and management research TARGET AUDIENCE • MBA (Analytics) • MA/MSc Statistics • MA Sociology
Handbook Of Computational Social Science Volume 1
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Author : Uwe Engel
language : en
Publisher: Routledge
Release Date : 2021-11-10
Handbook Of Computational Social Science Volume 1 written by Uwe Engel and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-10 with Computers categories.
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.
Data Mining For The Social Sciences
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Author : Paul Attewell
language : en
Publisher: Univ of California Press
Release Date : 2015-05-01
Data Mining For The Social Sciences written by Paul Attewell and has been published by Univ of California Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-01 with Social Science categories.
"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.
Big Data Research For Social Sciences And Social Impact
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Author : Miltiadis D. Lytras
language : en
Publisher: MDPI
Release Date : 2020-03-19
Big Data Research For Social Sciences And Social Impact written by Miltiadis D. Lytras and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-19 with Technology & Engineering categories.
A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.
The Routledge Social Science Handbook Of Ai
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Author : Anthony Elliott
language : en
Publisher: Routledge
Release Date : 2021-07-12
The Routledge Social Science Handbook Of Ai written by Anthony Elliott and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-12 with Social Science categories.
The Routledge Social Science Handbook of AI is a landmark volume providing students and teachers with a comprehensive and accessible guide to the major topics and trends of research in the social sciences of artificial intelligence (AI), as well as surveying how the digital revolution – from supercomputers and social media to advanced automation and robotics – is transforming society, culture, politics and economy. The Handbook provides representative coverage of the full range of social science engagements with the AI revolution, from employment and jobs to education and new digital skills to automated technologies of military warfare and the future of ethics. The reference work is introduced by editor Anthony Elliott, who addresses the question of relationship of social sciences to artificial intelligence, and who surveys various convergences and divergences between contemporary social theory and the digital revolution. The Handbook is exceptionally wide-ranging in span, covering topics all the way from AI technologies in everyday life to single-purpose robots throughout home and work life, and from the mainstreaming of human-machine interfaces to the latest advances in AI, such as the ability to mimic (and improve on) many aspects of human brain function. A unique integration of social science on the one hand and new technologies of artificial intelligence on the other, this Handbook offers readers new ways of understanding the rise of AI and its associated global transformations. Written in a clear and direct style, the Handbook will appeal to a wide undergraduate audience.
Role Of Emerging Technologies In Social Science
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Author : Hitesh Mohapatra
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2024-08-16
Role Of Emerging Technologies In Social Science written by Hitesh Mohapatra and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-16 with Computers categories.
In today’s world, technology has seamlessly woven itself into the fabric of our social existence, leaving an indelible mark. This book aims to illuminate the far-reaching impact of technology across various aspects of our lives, including business, commerce, lifestyle, sentiment analysis, and transportation. It delves into both the advantages and drawbacks of technology, emphasizing the need for a delicate balance between our social interactions and its pervasive influence. In today’s interconnected world, technology profoundly influences our social fabric. This book explores its impact across diverse domains—business, commerce, lifestyle, sentiment analysis, and transportation. It delves into both advantages and drawbacks, emphasizing the delicate balance between social interactions and technology, and guides aspiring researchers through cutting-edge topics like blockchain, the Internet of Things, AI, and machine learning. Key takeaways include understanding tech’s role, evaluating pros and cons, and exploring future research. The book caters to universities, graduate colleges, and research centers.
Introduction To Python Programming For Business And Social Science Applications
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Author : Frederick Kaefer
language : en
Publisher: SAGE Publications
Release Date : 2020-08-06
Introduction To Python Programming For Business And Social Science Applications written by Frederick Kaefer and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-06 with Business & Economics categories.
Would you like to gather big datasets, analyze them, and visualize the results, all in one program? If this describes you, then Introduction to Python Programming for Business and Social Science Applications is the book for you. Authors Frederick Kaefer and Paul Kaefer walk you through each step of the Python package installation and analysis process, with frequent exercises throughout so you can immediately try out the functions you’ve learned. Written in straightforward language for those with no programming background, this book will teach you how to use Python for your research and data analysis. Instead of teaching you the principles and practices of programming as a whole, this application-oriented text focuses on only what you need to know to research and answer social science questions. The text features two types of examples, one set from the General Social Survey and one set from a large taxi trip dataset from a major metropolitan area, to help readers understand the possibilities of working with Python. Chapters on installing and working within a programming environment, basic skills, and necessary commands will get you up and running quickly, while chapters on programming logic, data input and output, and data frames help you establish the basic framework for conducting analyses. Further chapters on web scraping, statistical analysis, machine learning, and data visualization help you apply your skills to your research. More advanced information on developing graphical user interfaces (GUIs) help you create functional data products using Python to inform general users of data who don’t work within Python. First there was IBM® SPSS®, then there was R, and now there′s Python. Statistical software is getting more aggressive - let authors Frederick Kaefer and Paul Kaefer help you tame it with Introduction to Python Programming for Business and Social Science Applications.
Handbook Of Computational Social Science For Policy
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Author : Eleonora Bertoni
language : en
Publisher: Springer Nature
Release Date : 2023-01-23
Handbook Of Computational Social Science For Policy written by Eleonora Bertoni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-23 with Computers categories.
This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holdingdata that can be used to study social sciences and are interested in achieving a policy impact.