Framing Big Data

2019-07-03
Framing Big Data

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  • Author: Maria Cristina Paganoni
  • Publisher: Springer
  • ISBN: 3030167887
  • Category : Language Arts & Disciplines
  • Languages : en
  • Pages : 116

This book addresses big data as a socio-technical construct with huge potential for innovation in key sectors such as healthcare, government and business. Big data and its increasingly widespread use in such influential spheres can generate ethically controversial decisions, including questions surrounding privacy, consent and accountability. This book attempts to unpack the epistemological implications of the term ‘big data’, as well as the opportunities and responsibilities which come with it. The author analyses the linguistic texture of the big data narrative in the news media, in healthcare and in EU law on data protection, in order to contribute to its understanding from the critical perspective of language studies. The result is a study which will be of interest to students and scholars working in the digital humanities, corpus linguistics, and discourse studies.

Thinking Big Data in Geography

2018-04
Thinking Big Data in Geography

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  • Author: Jim Thatcher
  • Publisher: U of Nebraska Press
  • ISBN: 1496205375
  • Category : Science
  • Languages : en
  • Pages : 318

Thinking Big Data in Geography offers a practical state-of-the-field overview of big data as both a means and an object of research, with essays from prominent and emerging scholars such as Rob Kitchin, Renee Sieber, and Mark Graham. Part 1 explores how the advent of geoweb technologies and big data sets has influenced some of geography’s major subdisciplines: urban politics and political economy, human-environment interactions, and geographic information sciences. Part 2 addresses how the geographic study of big data has implications for other disciplinary fields, notably the digital humanities and the study of social justice. The volume concludes with theoretical applications of the geoweb and big data as they pertain to society as a whole, examining the ways in which user-generated data come into the world and are complicit in its unfolding. The contributors raise caution regarding the use of spatial big data, citing issues of accuracy, surveillance, and privacy.

Big Data, Big Dupe

2018-02
Big Data, Big Dupe

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  • Author: Stephen Few
  • Publisher:
  • ISBN: 9781938377105
  • Category : Computers
  • Languages : en
  • Pages : 0

Argues against the value of big data, suggesting that it is a marketing campaign that distracts from the real and important work of deriving value from data.

R for Data Science

2016-12-12
R for Data Science

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  • Author: Hadley Wickham
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1491910364
  • Category : Computers
  • Languages : en
  • Pages : 521

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

Frame Theory in Data Science

Frame Theory in Data Science

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  • Author: Zhihua Zhang
  • Publisher: Springer Nature
  • ISBN: 3031494830
  • Category :
  • Languages : en
  • Pages : 262

Big Data?

2014-11-07
Big Data?

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  • Author: Martin Hand
  • Publisher: Emerald Group Publishing
  • ISBN: 1784410500
  • Category : Social Science
  • Languages : en
  • Pages : 250

This book examines and engages with the ambivalence of digitization, illuminating the diverse ways in which researchers approach, negotiate, understand and interpret objects and practices of digital research.

The Politics and Policies of Big Data

2018-05-08
The Politics and Policies of Big Data

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  • Author: Ann Rudinow Sætnan
  • Publisher: Routledge
  • ISBN: 1351866540
  • Category : Social Science
  • Languages : en
  • Pages : 358

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.

Framers

2022-05-12
Framers

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  • Author: Kenneth Cukier
  • Publisher: W H Allen
  • ISBN: 9780753555002
  • Category : Problem solving
  • Languages : en
  • Pages : 272

SQL Server Big Data Clusters

2020-05-23
SQL Server Big Data Clusters

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  • Author: Benjamin Weissman
  • Publisher: Apress
  • ISBN: 1484259858
  • Category : Computers
  • Languages : en
  • Pages : 272

Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments

Thick Big Data

2020-02-20
Thick Big Data

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  • Author: Dariusz Jemielniak
  • Publisher: Oxford University Press, USA
  • ISBN: 0198839707
  • Category : Business & Economics
  • Languages : en
  • Pages : 208

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.