big data and social science

Download Book Big Data And Social Science in PDF format. You can Read Online Big Data And Social Science here in PDF, EPUB, Mobi or Docx formats.

Big Data And Social Science

Author : Ian Foster
ISBN : 9781498751438
Genre : Mathematics
File Size : 57. 5 MB
Format : PDF
Download : 528
Read : 1129

Get This Book


Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Big Data Research For Social Sciences And Social Impact

Author : Miltiadis D. Lytras
ISBN : 9783039282203
Genre : Technology & Engineering
File Size : 80. 2 MB
Format : PDF
Download : 552
Read : 726

Get This Book


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.

Computational Social Science In The Age Of Big Data

Author : Martin Welker
ISBN : 9783869622682
Genre : Business & Economics
File Size : 72. 11 MB
Format : PDF, ePub
Download : 212
Read : 335

Get This Book


Der Sammelband Computational Social Science in the Age of Big Data beschäftigt sich mit Konzepten, Methoden, Tools und Anwendungen (automatisierter) datengetriebener Forschung mit sozialwissenschaftlichem Hintergrund. Der Fokus des Bandes liegt auf der Etablierung der Computational Social Science (CSS) als aufkommendes Forschungs- und Anwendungsfeld. Es werden Beiträge international namhafter Autoren präsentiert, die forschungs- und praxisrelevante Themen dieses Bereiches besprechen. Die Herausgeber forcieren dabei einen interdisziplinären Zugang zum Feld, der sowohl Online-Forschern aus der Wissenschaft wie auch aus der angewandten Marktforschung einen Einstieg bietet.

Humanizing Big Data

Author : Colin Strong
ISBN : 9780749472122
Genre : Business & Economics
File Size : 72. 54 MB
Format : PDF, ePub, Mobi
Download : 312
Read : 241

Get This Book


Big data raises more questions than it answers, particularly for those organizations struggling to deal with what has become an overwhelming deluge of data. It can offer marketers more than simple tactical predictive analytics, but organizations need a bigger picture, one that generates some real insight into human behaviour, to drive consumer strategy rather than just better targeting techniques. Humanizing Big Data guides marketing managers, brand managers, strategists and senior executives on how to use big data strategically to redefine customer relationships for better customer engagement and an improved bottom line. Humanizing Big Data provides a detailed understanding of the way to approach and think about the challenges and opportunities of big data, enabling any brand to realize the value of their current and future data assets. First it explores the 'nuts and bolts' of data analytics and the way in which the current big data agenda is in danger of losing credibility by paying insufficient attention to what are often fundamental tenets in any form of analysis. Next it sets out a manifesto for a smart data approach, drawing on an intelligent and big picture view of data analytics that addresses the strategic business challenges that businesses face. Finally it explores the way in which datafication is changing the nature of the relationship between brands and consumers and why this calls for new forms of analytics to support rapidly emerging new business models. After reading this book, any brand should be in a position to make a step change in the value they derive from their data assets.

Big Data And Social Science

Author : Sudha Menon
ISBN : 1774072696
Genre : Social Science
File Size : 37. 60 MB
Format : PDF, ePub
Download : 952
Read : 786

Get This Book


Big Data and Social Science discusses about how big data and social science can be employed in a system. This book comprises topics and explanations about types of big data, different big data systems and type of datasets that are considered big data. This book also sheds light on social science in aspect of big data, the web and data as well as computational social science. Readers are also given insights about topics such as necessities of computational social sciences, necessary skills for computational social science and the importance of computational social science. Discussions about text analysis in the world of big data and information visualization are also laid down in a simple manner.

Thick Big Data

Author : Dariusz Jemielniak
ISBN : 9780198839705
Genre : Business & Economics
File Size : 35. 91 MB
Format : PDF, Mobi
Download : 996
Read : 465

Get This Book


The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.

Big Data Analysis Using Machine Learning For Social Scientists And Criminologists

Author : Juyoung Song
ISBN : 9781527536791
Genre : Social Science
File Size : 52. 64 MB
Format : PDF
Download : 788
Read : 312

Get This Book


This book provides a detailed description of the entire study process concerning gathering and analysing big data and making observations to develop a crime-prediction model that utilizes its findings. It offers an in-depth discussion of several processes, including text mining, which extracts useful information from online documents; opinion mining, which analyses the emotions contained in documents; machine learning for crime prediction; and visualization analysis. To accurately predict crimes using machine learning, it is necessary to procure high-quality training data. Machine learning combined with high-quality data can be used to develop excellent crime-prediction artificial intelligences. As such, the book will serve to be a practical guide to anyone wishing to predict rapidly-changing social phenomena and draw creative conclusions using big-data analysis.

The Use Of Big Data For Social Sciences Research

Author : Mihály Fazekas
ISBN : 147395035X
Genre : Big data
File Size : 68. 93 MB
Format : PDF, ePub
Download : 607
Read : 1131

Get This Book


With the advent of 'big data', that is, the online availability of large volumes of electronic data characterising all aspects of social life, new avenues of research have opened up both with its promises and caveats. Working with 'big data' requires a new research mindset in terms of obtaining data and analysing them. This methodology case study uses the example of an ongoing research project looking into high-level corruption in public procurement in Central and Eastern Europe.

Data Science And Social Research

Author : N. Carlo Lauro
ISBN : 9783319554778
Genre : Social Science
File Size : 34. 55 MB
Format : PDF, ePub, Mobi
Download : 660
Read : 1244

Get This Book


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.

Introduction To Data Science For Social And Policy Research

Author : Jose Manuel Magallanes Reyes
ISBN : 9781108364119
Genre : Social Science
File Size : 59. 96 MB
Format : PDF, ePub, Docs
Download : 635
Read : 1075

Get This Book


Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Top Download:

Best Books