data science 2018


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The professionalisation of data science
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Data science is establishing basic foundations to become a profession. Like the professionalisation of law and medicine in the past 100 years, the data science field is at the very beginning of becoming a profession with competency standards, a code of

Data science : big data , machine learning, and artificial intelligence
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A quick search on PubMed. gov for each of the terms that have been used to describe some aspect of data science exceeded 000 results per term with artificial intelligence (AI) returning 7 250 results. Moehrle introduces a future with a need for fewer radiologists with

Magic Quadrant for data science and machine-learning platforms
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This Magic Quadrant evaluates vendors of data science and machine-learning platforms. These are software products that data scientists use to help them develop and deploy their own data science and machine-learning solutions. More precisely, Gartner defines a data

Big Data science and its applications in health and medical research: Challenges and opportunities
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Abstract Recently, Big Data science has been a hot topic in the scientific, industrial and the business worlds. The healthcare and biomedical sciences have rapidly become data intensive as investigators are generating and using large, complex, high dimensional, andData science has been recognized as a science since 200 roughly. Its origin lies in technological advances that are generating nearly inconceivable volumes of data . The rate at which new data are being produced is not likely to slow for some time. As a society, we

Advances in Epilepsy: A data science perspective
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Epilepsy is the fourth most common neurological disorder, affecting more than 65 million people around the world It is estimated that 1 in 26 Americans will develop epilepsy in their lifetime. Here we look at how web resources like PubMed, social media data and news can

The Art of Data Science
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This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right Modern data science is related to massive data sets (BigData), machine learning, and cloud computing. There are multiple ways of understanding data science :(1) BigData with small cloud computational power, which requires very fast algorithms;(2) relatively small data sets

Process Mining: Data Science in Action
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At last decades people have to accumulate more and more data in different areas. Nowadays a lot of organizations are able to solve the problem with capacities of storage devices and therefore storing Big data . However they also often face to problem getting

2016 data science salary survey
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Media have collected survey data from data scientists, engineers, and others in the data space about their skills, tools, and salary. Some of the same patterns we saw last year are still present newer, scalable open source tools in general correlate with higher salariesWe live in an age where data is being collected at unprecedented levels. One can benefit from data only after converting it into useful and actionable knowledge. In the past few years, the methodology of extracting insights from data or data science has emerged as a This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical

Data Science Data , Tools Technologies
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If there was a time machine for real, I would like to take the readers 20 years back before explaining the title of the article. Theresa simple reason for it; to demonstrate how technology today has worked wonders for almost all the domains including finance

A survey on data science technologies big data analytics
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Data science is about dealing with large quality of data for the purpose of extracting meaningful and logical results/conclusions/patterns. Itsa newly emerging field that encompasses a number of activities, such as data mining and data analysis. It employs

Cognitive automation of data science
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This paper explores how an automated procedure leverage domain knowledge and reasoning to further automate Machine Learning (ML) and Data Science in a manner that be thought of as cognitive. To this end, we first describe key features that we believe a

Data science and data scientists: Whats in a name
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By Todd Saunders clarity for the term so that it is less likely to be confused with other terms such as business intelligence or data analysis. And why is that important Simply to be pragmatic. The point of a definition is efficient communication. The three terms that

LEXIA: A data science environment for Semantic analysis of german legal texts
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1Research Associate, b. waltl@ tum. de, Technical University of Munich, Department of Informatics, Software Engineering for Business Information Systems, Boltzmannstraße 85748 Garching bei München, DE 2Professor, matthes@ tum. de; https://wwwmatthes. in

Data science of the people, for the people, by the people: A viewpoint on an emerging dichotomy
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This paper presents a viewpoint on an emerging dichotomy in data science : applications in which predictions of datadriven algorithms are used to support people in making consequential decisions that can have a profound effect on other peoples lives and

Tempe: an interactive data science environment for exploration of temporal and streaming data
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Over the last two decades, data scientists performed increasingly sophisticated analyses on larger data sets, yet their tools and workflows remain low-level. A typical analysis involves different tools for different stages of the work, requiring file transfers and considerable care to

Learn2Mine: Data science practice and education through gameful experiences
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We introduce Learn2Mine, an education and analysis platform that integrates state-of-the-art data mining tools with effective feedback and training mechanisms in order to lower the barrier for domain experts and computer scientists to learn data science . Data science is the


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