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The Big Data Age

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Elements in Quantitative and Computational Methods for the S: Modern Dimension Reduction (Paperback)

In the big data age, this Element offers many modern methods and opensource code for learning from high dimensional data. Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.

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How new tech can help lawyers rethink their jobs in the big data age

The legal profession has undergone a lot of unpleasant changes since the Great Recession struck in 2008. New data-analysis technologies and a new approach to thinking about data could help firms operate leaner, meaner and better.

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Scalable Efficient Big Data Pipeline Architecture

Scalable and efficient data pipelines are as important for the success of analytics, data science, and machine learning as reliable supply lines are for winning a war.

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Big Data and the Internet of Things - by Robert Stackowiak & Art Licht & Venu Mantha & Louis Nagode (Paperback)

About the Book "Your guide to defining an information architecture for emerging trends like Big Data and the Internet of Things"--Page 1 of cover. Book Synopsis Enterprise Information Architecture for a New Age: Big Data and The Internet of Things, provides guidance in designing an information architecture to accommodate increasingly large amounts of data, massively large amounts of data, not only from traditional sources, but also from novel sources such everyday objects that are fast bing wired into global Internet. No business can afford to be caught out by missing the value to be mined from the increasingly large amounts of available data generated by everyday devices. The text provides background as to how analytical solutions and enterprise architecture methodologies and concepts have evolved (including the roles of data warehouses, business intelligence tools, predictive analytics, data discovery, Big Data, and the impact of the Internet of Things). Then you're taken through a series of steps by which to define a future state architecture and create a plan for how to reach that future state. Enterprise Information Architecture for a New Age: Big Data and The Internet of Things helps you gain an understanding of the following: Implications of Big Data from a variety of new data sources (including data from sensors that are part of the Internet of Things) upon an information architecture How establishing a vision for data usage by defining a roadmap that aligns IT with line-of-business needs is a key early step The importance and details of taking a step-by-step approach when dealing with shifting business challenges and changing technology capabilities How to mitigate risk when evaluating existing infrastructure and designing and deploying new infrastructure Enterprise Information Architecture for a New Age: Big Data and The Internet of Things combines practical advice with technical considerations. Author Robert Stackowiak and his team are recognized worldwide for their expertise in large data solutions, including analytics. Don't miss your chance to read this book and gain the benefit of their advice as you look forward in thinking through your own choices and designing your own architecture to accommodate the burgeoning explosion in data that can be analyzed and converted into valuable information to drive your business forward toward success. About the Author Robert Stackowiak is Vice President of Information Architecture and Big Data at Oracle in North America. His team of architects and experts focuses on Big Data (including Hadoop and NoSQL databases), predictive analytics, data warehousing, business intelligence, and information discovery. The team engages with companies that are implementing these technologies and exploring new solutions such as those enabled by the Internet of Things. Bob has spoken at conferences around the world and co-authored many books on data management and business intelligence including five editions of Oracle Essentials (O'Reilly Media), Oracle Big Data Handbook (Oracle Press), Achieving Extreme Performance with Oracle Exadata (Oracle Press), and Oracle Data Warehousing and Business Intelligence Solutions (Wiley). Follow him on Twitter @rstackow.

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Growing Depth Of Background Checks In The Big Data Age

ALT Datum #BigData #SmartDataCollectiveExclusive #backgroundchecks Growing Depth Of Background Checks In The Big Data Age Data Analytics

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Compromised Data - by Greg Elmer (Hardcover)

Book Synopsis There has been a data rush in the past decade brought about by online communication and, in particular, social media (Facebook, Twitter, Youtube, among others), which promises a new age of digital enlightenment. But social data is compromised: it is being seized by specific economic interests, it leads to a fundamental shift in the relationship between research and the public good, and it fosters new forms of control and surveillance. Compromised Data: From Social Media to Big Data explores how we perform critical research within a compromised social data framework. The expert, international lineup of contributors explores the limits and challenges of social data research in order to invent and develop new modes of doing public research. At its core, this collection argues that we are witnessing a fundamental reshaping of the social through social data mining. Review Quotes "Big data promise a more efficient understanding of citizen needs, wants, and desires and cast a wide net of harnessing these tendencies in the hope of bringing about more direct forms of democracy. At the same time, however, they do so at the cost of compromising personal data, in effect sacrificing the personal for the political. Compromised Data features contributions from leading scholars who examine the immediacy and complexity of data, big or small, and is a must read for all interested in these issues." --Zizi Papacharissi, Professor and Head of Communication, University of Illinois at Chicago, USA, and editor of the Journal of Broadcasting and Electronic Media and Social Media and Society "Social theorists and champions of civil rights have long been concerned with the politics and practices of dataveillance, forms of surveillance accomplished through computation instead of by visual inspection. Yet, Compromised Data may be the first book that is so very clear about the threats and dangers of surveillance after the computational turn; after when, that is, the concern is not so much who sees whom, but whose data is captured by whom and what calculations are performed on that data. You have been instrumented -- through all your Internet-enabled devices -- to divulge where you are and when, what you say and write, with whom you associate, and what you are interested in. This book asks in very specific terms how we are compromised by big data." --Warren Sack, Professor of Film and Digital Media, University of California, Santa Cruz, USA "[CC] The digital age provides society with many advantages in the areas of communication and information on a personal, social, or political level. Here, more then ten international scholars who have examined the importance and complexity of social data and big data issues address the dangers that may occur in surveillance of online sites, including how and by whom this social data may be transformed and eventually controlled. Under the best scenario, data collection and analysis help society further the common good. However, the results of social data analysis can often be compromised. Avenues such as Facebook and Twitter allow users to share personal information, but often these sites are compromised by marketers who may use this information for profit. Clicking on a link frequently gives researchers worthwhile information, but even an innocuous click can be problematic. In addition, privacy is a major issue. Individuals innocently list information about their age, where they work and live, what their interests and concerns are, where they travel, etc., not realizing the potential adverse effects of their actions. Selected information from data collected online can lead to issues related to social profiling and guilt or innocence, as well as flag persons of interest. Overall, a thought-provoking book for all online users. Summing Up: Rmended. All readership levels." -D. J. Gougeon, University of Scranton, USA, CHOICE About the Author Ganaele Langlois is Assistant Professor at York University, Canada, and Associate Director of the Infoscape Centre for the Study of Social Media, Ryerson University, Canada. Her research focuses on new media theory, software studies and technoculture. Joanna Redden is Assistant Professor of Critical Media Studies at the University of Calgary, Canada. Her work investigates how digital technologies influence political, media, and protest practices specifically as related to poverty, inequality, and governance. Greg Elmer is Bell Globemedia Research Chair and Director of the Infoscape Centre for the Study of Social Media at Ryerson University, Canada. His research and teaching focus on new media and politics, theories and methods in social media studies, surveillance theory, and media globalization.

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(Ebook PDF) - Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data

(Ebook PDF) - Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data

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C Suite Podcast Season 2 Episode 3: Data Dilemma

#Didyouknow 🤔 97.2% of organizations are investing in big data and AI. (Source: NewVantage) In this episode, we feature two guests: Alexandra Ebert, the Chief Trust Officer of Mostly.ai, and Doug Merritt, the CEO of Splunk, to explore: ✅ Examples of how #data can be used incorrectly ✅ Tips for companies to maximize the use of data and protect their customers Listen now

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The Big V's of Big Data

Big Data is unmistakenly a key trend that businesses must acclimate with proper computing infrastructures and storage tools. However, in order to avoi

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Machine Learning Audits in the 'Big Data Age' | CIO Insight

By Andrew Clark Machine learning—essentially a computer that recognizes patterns without having to be explicitly programmed—is revolutionizing

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