Multidimensional Data Visualization

Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions,

An integrated introduction to data visualization, strategic communication, and delivery best practices.Persuading with Data provides an integrated instructional guide to data visualization, strategic communication, and delivery best practices. Most books on data visualization focus on creating good graphs. This is the first book that combines both explanatory visualization and communication strategy, showing how to use visuals to create effective communications that convince an audience to accept and act on the data. In four parts that proceed from macro to micro, the book explains how our brains make sense of graphs; how to design effective graphs and slides that support your ideas; how to organize those ideas into a compelling presentation; and how to deliver and defend data to an audience. Persuading with Data is for anyone who has to explain analytical results to others. It synthesizes a wide range of skills needed by modern data professionals, providing a complete toolkit for cr

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

In the age of Big Data, the tools of information visualization offer us a macroscopeto help us make sense of the avalanche of data available on every subject. This book offers a gentleintroduction to

Scholars from a range of disciplines interrogate terms relevant to critical studies of big data, from abuse and aggregate to visualization and vulnerability.This pathbreaking work offers an interdisci

Digital images are an integral part of all media, including television, film, photography, animation, video games, data visualization, and the Internet. In the digital world, spectators become navigat

Interactive visualization is emerging as a vibrant new form of communication,providing compelling presentations that allow viewers to interact directly with information in orderto construct their own

How to use data as a tool for empowerment rather than oppression.Big data can be used for good, from tracking disease to exposing human rights violations, and for bad, implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action, Sarah Williams provides a guide for working with data in more ethical and responsible ways. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. The approach generates policy debates, influences civic decisions, and informs design to help ensure that the voices of people represented in the data are neither marginalized nor left unheard.

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deplo

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States--Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow--Loukissas shows us how to analyze data settings rather than data sets.Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogen

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow―and show how a human-centered approach can enhance each one, by making the process more transparent, aski

An examination of the datafication of family life--in particular, the construction of our children into data subjects. Our families are being turned into data, as the digital traces we leave are share

How to use data as a tool for empowerment rather than oppression. Big data can be used for good--from tracking disease to exposing human rights violations--and for bad--implementing surveillance and c

How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data.Trusted Data describes a data architecture that pla

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local.In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Lo

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identi

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identi

We live in the era of Big Data, with storage and transmission capacity measured notjust in terabytes but in petabytes (where peta- denotes a quadrillion, or athousand trillion). Data collection is con

In the eighteenth century, chemistry was the science of materials. Chemists treated mundane raw materials and chemical substances as multidimensional objects of inquiry that could be investigated in b

An interdisciplinary study of the basic human activities of accessing and browsing. This book contends that accessing and browsing information and communication are multidimensional and consequentia

A wake-up call for America to create a new framework for democratizing data.Public data are foundational to our democratic system. People need consistently high-quality information from trustworthy so

A guide to all aspects of experimental design and data analysis for fMRI experiments, completely revised and updated for the second edition. Functional magnetic resonance imaging (fMRI), which allows

The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.Today, the volume, velocity, and variety of data are increasing rapid

If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?Machine learning-programming computers to learn from data-has spread across scientific d

Machine learning -- programming computers to learn from data -- has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transactio

MATLAB is one of the most popular programming languages for neuroscience and psychology research. Its balance of usability, visualization, and widespread use makes it one of the most powerful tools in

"Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. B

As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural

"Big Data" is on the covers of Science, Nature, the Economist, andWired magazines, on the front pages of the Wall Street Journal and theNew York Times. But despite the media hyperbol

Big Data is made up of lots of little data: numbers entered into cell phones,addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and anyother activity that leav

Representation in Scientific Practice, published by the MIT Pressin 1990, helped coalesce a long-standing interest in scientific visualization among historians,philosophers, and sociologists of scienc

Biomedical signal analysis has become one of the most important visualization and interpretation methods in biology and medicine. Many new and powerful instruments for detecting, storing, transmittin

All organizations today confront data quality problems, both systemic and structural. Neither ad hoc approaches nor fixes at the systems level—installing the latest software or developing an ex

Over the past twenty years, the technologies of simulation and visualization have changed our ways of looking at the world. In Simulation and Its Discontents, Sherry Turkle examines the now dominant

This definitive study uses theory, history, and data to analyze the evolution of the US brewing industry from a fragmented market to an emerging oligopoly. Drawing on a rich and extensive data set an

This definitive study uses theory, history, and data to analyze the evolution of the US brewing industry from a fragmented market to an emerging oligopoly. Drawing on a rich and extensive data set and

This book focuses on recent developments in representational and processing aspects of complex data-intensive applications. Until recently, information systems have been designed around different bu

Data-Parallel Programming demonstrates that architecture-independent parallel programming is possible by describing in detail how programs written in a high-level SIMD programming language may be comp

The assembly of an archive of longitudinal data on the world's nation-states was undertaken by the Center for Comparative Political Research in 1968, with computer-facilitated procedures for the stora