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15 data viz events to watch for in 2015

15 data viz events to watch for in 2015 | Data Science | Scoop.it
December is here, and that means it’s time to look ahead at some of the main events that will take place next year.
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The Health Data Revolution: Improving Outcomes, Protecting Privacy

Will the next great medical insight come from a clinical trial, a laboratory study — or a database search? Today, health systems and insurers have access t
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The Promise and Prejudice of Big Data in Intelligence Community

The Promise and Prejudice of Big Data in Intelligence Community | Data Science | Scoop.it
Big data holds critical importance in the current generation of information technology, with applications ranging from financial, industrial, academic to defense sectors. With the exponential rise of open source data from social media and increasing government monitoring, big data is now also linked with national security, and subsequently to the intelligence community. In this study I review the scope of big data sciences in the functioning of intelligence community. The major part of my study focuses on the inherent limitations of big data, which affects the intelligence agencies from gathering of information to anticipating surprises. Photo: Wikimedia Commons at https://commons.wikimedia.org/wiki/File:A_24_hour_watch_center_at_the_Defense_Intelligence_Agency_(DIA).jpg
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Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data | Data Science | Scoop.it
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Photo: Chris Devers at https://www.flickr.com/photos/cdevers/8247782627
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Leveraging Big Data To Model An Ideal Buyer's Persona

Leveraging Big Data To Model An Ideal Buyer's Persona | Data Science | Scoop.it

When we develop a product, we should have a clear view of who our customers are. Conventional wisdom implies that we should include features that will satisfy a cross-section of our targeted customers. Cooper explains how applying this logic will result in a product that is an all-around compromise unable to excite anyone in the customer base..

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Big Data Comes to Dieting

Big Data Comes to Dieting | Data Science | Scoop.it

At this point in the resolution-heavy month, many of us may be trying to shed pounds, either the ones we added during the holidays or the ones we’ve accumulated stealthily with time. But by the end of the year, most of us won’t have succeeded — and there’s not much established science to tell us why. An ambitious new study published this month promises to shed some new light, enumerating for the first time the thousands of changes in genes and various biological systems that may occur after even a small amount of weight gain, and which may — or may not — be reversed if the weight is then dropped..

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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning | Data Science | Scoop.it
Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would revolutionize our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could transform many fields of biology, ecology, and zoology into "big data" sciences. Motion sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence.
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Data science for assessing possible tax income manipulation: The case of Italy

Data science for assessing possible tax income manipulation: The case of Italy | Data Science | Scoop.it
This paper explores a real-world fundamental theme under a data science perspective. It specifically discusses whether fraud or manipulation can be observed in and from municipality income tax size distributions, through their aggregation from citizen fiscal reports. The study case pertains to official data obtained from the Italian Ministry of Economics and Finance over the period 2007-2011. All Italian (20) regions are considered. The considered data science approach concretizes in the adoption of the Benford first digit law as quantitative tool.
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DOT wants to use data science to make roads safer

DOT wants to use data science to make roads safer | Data Science | Scoop.it
Earlier this month the Department of Transportation announced its intention to use data to make America’s highways safer. To begin with, this goal comes in the form of two pilot projects — both of which will integrate existing crash data sets with newer forms of “big data.” “Advances in data science have the potential to …
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Skynet it ain't: Deep learning will not evolve into true AI, says boffin

Skynet it ain't: Deep learning will not evolve into true AI, says boffin | Data Science | Scoop.it

Deep learning and neural networks may have benefited from the huge quantities of data and computing power, but they won't take us all the way to artificial general intelligence, according to a recent academic assessment. Gary Marcus, ex-director of Uber's AI labs and a psychology professor at the University of New York, argues that there are numerous challenges to deep learning systems that broadly fall into a series of categories.

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France may protect citizens' liberté with ban on foreigners buying local big data firms

France may protect citizens' liberté with ban on foreigners buying local big data firms | Data Science | Scoop.it

France is considering regulating foreign takeovers of businesses in the data protection and artificial intelligence sectors, minister for the economy Bruno Le Maire said on Friday. Le Maire made the comments while touring China with French president Emmanuel Macron. The investment restrictions would be added to the Montebourg decree that already regulates foreign takeovers of firms in the energy supply, water, transport, telecoms and public health sectors.

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A Knowledge Ecosystem for the Food, Energy, and Water System

A Knowledge Ecosystem for the Food, Energy, and Water System | Data Science | Scoop.it
Food, energy, and water (FEW) are key resources to sustain human life and economic growth. There is an increasing stress on these interconnected resources due to population growth, natural disasters, and human activities. New research is necessary to foster more efficient, more secure, and safer use of FEW resources in the U.S. and globally. In this position paper, we present the idea of a knowledge ecosystem for enabling the semantic data integration of heterogeneous datasets in the FEW system to promote knowledge discovery and superior decision making through semantic reasoning.
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Perspectives on Surgical Data Science

Perspectives on Surgical Data Science | Data Science | Scoop.it
The availability of large amounts of data together with advances in analytical techniques afford an opportunity to address difficult challenges in ensuring that healthcare is safe, effective, efficient, patient-centered, equitable, and timely. Surgical care and training stand to tremendously gain through surgical data science. Herein, we discuss a few perspectives on the scope and objectives for surgical data science. Photo: https://health.mil/News/Articles/2017/10/24/Keeping-surgical-instruments-sterile-safe
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From Sky to Earth: Data Science Methodology Transfer

From Sky to Earth: Data Science Methodology Transfer | Data Science | Scoop.it
We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.
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Scope for Machine Learning in Digital Manufacturing

Scope for Machine Learning in Digital Manufacturing | Data Science | Scoop.it
This provocation paper provides an overview of the underlying optimisation problem in the emerging field of Digital Manufacturing. Initially, this paper discusses how the notion of Digital Manufacturing is transforming from a term describing a suite of software tools for the integration of production and design functions towards a more general concept incorporating computerised manufacturing and supply chain processes, as well as information collection and utilisation across the product life cycle. Photo: Proto Labs at https://en.wikipedia.org/wiki/Proto_Labs#/media/File:Proto-Labs-Manufacturing-Injection-Molding.jpg
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Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences

Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences | Data Science | Scoop.it
As part of the Michigan Data Science Team (MDST), we partnered with the University Musical Society (UMS), a non-profit performing arts presenter housed in the University of Michigan, Ann Arbor. We are providing UMS with analysis and business intelligence, utilizing historical individual-level sales data. We built a recommendation system based on collaborative filtering, gaining insights into the artistic preferences of customers, along with the similarities between performances. To better understand audience behavior, we used statistical methods from customer-base analysis. Photo: Wikimedia Commons at https://commons.wikimedia.org/wiki/File:All_Time_Low_-_Saratoga_Performing_Arts_Center_September_4_2016.jpg
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Your Data Is Sound, But How’s Your Dashboard? 5 Aspects to Consider 

Your Data Is Sound, But How’s Your Dashboard? 5 Aspects to Consider  | Data Science | Scoop.it

One of the biggest problems in data management and data science is being able to obtain “good” data. You need to gather sufficient data from a substantial array of subjects who fit your study’s requirements, and ensure the accuracy of the data... otherwise, any conclusions you draw could be biased or skewed. But assume for a moment that your data is already solid. In addition to considering the quality of your data, consider the quality of your dashboard; it’s more important than you might assume.

Photo: Mederndepe at https://www.flickr.com/photos/jadendave/4985619920

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Three Perspectives on Learning Analytics in 2017 and Beyond 

Three Perspectives on Learning Analytics in 2017 and Beyond  | Data Science | Scoop.it
The emerging fields of learning analytics and educational data science have seen rapid change in recent years. As adoption increases and we in higher education grow in our understanding of how data collected by colleges and universities can be used to increase student success, it is helpful to look back at how far we have come in the past year as we also look forward to the year ahead. We recently asked three of our analytics experts to reflect on the field of learning analytics at the end of 2017. This is what they said.
 
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Key steps to model creation: data cleaning and data exploration

Key steps to model creation: data cleaning and data exploration | Data Science | Scoop.it

By following best practices and philosophies around these processes, an organization can enable successful collaboration and iteration between data science and IT teams. The explosion of data in the modern world has brought on many novel business problems when It comes to the applications of modeling and analysis. Businesses are starting to recognize the value that mature, robust analytics practice can bring to both their understanding of the industry, and their bottom line.

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Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors

Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors | Data Science | Scoop.it
As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company's user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance.
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Today's Deep Learning Frameworks Won't Change The Machine Learning Adoption Curve

Today's Deep Learning Frameworks Won't Change The Machine Learning Adoption Curve | Data Science | Scoop.it
Frameworks are only an intermediary step to the wider adoption of machine learning in applications. What’s needed are more visual products and those are still a couple of years away.
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Advanced Analytics Platforms – Big Changes in the Leaderboard 

Advanced Analytics Platforms – Big Changes in the Leaderboard  | Data Science | Scoop.it

The Gartner Magic Quadrant for Advanced Analytic and ML Platforms came out on February 22nd and there are some big changes in the leaderboard. Not only are there some surprising upgrades (Alteryx, KNIME, H20.ai) but some equally notable long falls for traditional players (IBM, Dataiku, and Teradata).

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Revealed: Two Secret Cogs In The FBI National Surveillance Machine

Revealed: Two Secret Cogs In The FBI National Surveillance Machine | Data Science | Scoop.it

After 9/11, federal law enforcement and intelligence agencies were roundly criticized for failing to coordinate information that, in the aggregate, might have allowed the government to stop the attacks before they happened. Since then, the pendulum has swung in the opposite direction. The FBI has built a secretive and guarded intelligence operation, the tentacles of which stretch beyond its core task of domestic law enforcement and into the construction of the great American panopticon..

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Data Science at Udemy: Agile Experimentation with Algorithms

Data Science at Udemy: Agile Experimentation with Algorithms | Data Science | Scoop.it
In this paper, we describe the data science framework at Udemy, which currently supports the recommender and search system. We explain the motivations behind the framework and review the approach, which allows multiple individual data scientists to all become 'full stack', taking control of their own destinies from the exploration and research phase, through algorithm development, experiment setup, and deep experiment analytics. We describe algorithms tested and deployed in 2015, as well as some key insights obtained from experiments leading to the launch of the new recommender system at Udemy.
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Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice'

Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice' | Data Science | Scoop.it
We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliche, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.
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Connecting Data Science and Qualitative Interview Insights through Sentiment Analysis to Assess Migrants' Emotion States Post-Settlement

Connecting Data Science and Qualitative Interview Insights through Sentiment Analysis to Assess Migrants' Emotion States Post-Settlement | Data Science | Scoop.it
Large-scale survey research by social scientists offers general understandings of migrants' challenges and provides assessments of post-migration benchmarks like employment, obtention of educational credentials, and home ownership. Minimal research, however, probes the realm of emotions or "feeling states" in migration and settlement processes, and it is often approached through closed-ended survey questions that superficially assess feeling states. The evaluation of emotions in migration and settlement has been largely left to qualitative researchers using in-depth, interpretive methods like semi-structured interviewing.
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