data science 2018

MLxtend: Providing machine learning and data science utilities and extensions to Pythons scientific computing stack
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MLxtend is a library that implements a variety of core algorithms and utilities for machine learning and data mining. The primary goal of MLxtend is to make commonly used tools accessible to researchers in academia and data scientists in industries focussing on

Data Science Challenges in Gravitational Wave Astronomy
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The Challenge of Big Data and Data Science
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Big data and data science are being used as buzzwords and are composites of many concepts says the US National Institute of Standards and Technology in a 2015 framework report on Big Data (NIST, 2015, 2). The phrase big data appears frequently in the press

Toward a System Building Agenda for Data Integration (and Data Science ).
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We argue that the data integration (DI) community should devote far more effort to building systems, in order to truly advance the field. We discuss the limitations of current DI systems, and point out that there is already an existing popular DI system out there, which is PyData The series Studies in Big Data (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of In the modern information age, data and decisions are more strongly linked than ever before. The recent emergence of data science as an interdisciplinary field that aims to distil insights from data and that informs data-driven decision-making bears witness to the

Data science and healthcare
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Summary The Big Data ecosystem consists of five components:(1) data creation,(2) data collection and management,(3) analysis and information extraction,(4) hypothesis and experiment and (5) decision-making and action. We propose to use data science in which

Soft Computing in Data Science
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Educational data mining has been widely used to predict student performance and establish intervention strategies to improve that performance. Most studies have implemented machine learning algorithms for interventions but the use of data mining in appraising

Semantic Representation of Data Science Programs.
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Your computer through which you are, in all likelihood, reading this paper is continuously, efficiently, and reliably executing computer programs, but does it really understand them Not in any meaningful sense. That burden falls upon human knowledge Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) on 17 21 September 2018 in Compiègne, France. The series of biannual International Conference

Microservice Architectures for Data Science Applications
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Page 1. Microservice Architectures for Data Science Applications Prof. Dr. Wilhelm (Willi) Hasselbring Software Engineering Group, Kiel University GEOMAR Data Science Symposium, April 19th, 2018 19.04.2018 W. Hasselbring 1 Page 2. Agenda 1. A

BBVA Fuels Digital Transformation Progress with a Data Science Center of Excellence
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employees served 72 million customers in the areas of

Learning health systems need to bridge thetwo cultures of clinical informatics and data science
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Background UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational Big Data. Learning health systems require not only data, but feedback loops of knowledge into changed

A Review on Data Science Technologies
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Data science is secure in with managing huge nature of information to extract significant and coherent outcomes/conclusions/designs. It is a recently rising field that incorporates various exercises, for example, data mining and information investigation. It utilizes procedures The increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings is overwhelming. The focus of this chapter is on how data with a large number of records, of

What is Data Science
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Data Science , a new discovery paradigm, is potentially one of the most significant advances of the early 21st century. Originating in scientific discovery, it is being applied to every human endeavor for which there is adequate data. While remarkable successes have been

Big Data: The Next Era of Informatics and Data Science in Medical Imaging: A Literature Review
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Big data is an evolving topic of research in recent years and provides much promise in all fields, including medicine and radiology. The purpose of this literature review is to define big data, and discuss its relevance to medical imaging informatics. Relevant imaging informatics

Data Science for Startups
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In 2017, I changed industries and joined a startup company where I was responsible for building up a data science discipline. While we already had a solid data pipeline in place when I joined, we didnt have processes in place for reproducible analysis, scaling up

data science 2018 IEEE PAPER

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