DATA SCIENCE-2020


Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems
to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data.

The Belmont Report in the Age of Big Data : Ethics at the Intersection of Psychological Science and Data Science
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My thanks to Tom Griffiths for conversations about related issues during our work together on Data on the Mind; to Julia Blau for invaluable feedback on this chapter; to Aaron Culich for thoughtful discussions about securing computational pipelines; to audiences at U The original version of this book was published with an incorrect volume number, which has now been changed from SCI 871 to 871 The correction book has been updated with the change The updated version of the book can be found at https://doi.org/10.1007/978-3-030

Data Science
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Fulfilling this data science promise also means more accurate tracking, which results in better return on advertising investment (ROI), and ad spending (ROAS). With improved accuracy, hotel marketers can be more confident in their work. These stronger returns can

Practical Data Science for Actuarial Tasks
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The increased presence of data science in financial services will mean that many actuaries will have some level of familiarity with the basic concepts behind machine learning. However, it remains a challenge for actuaries to integrate these new techniques into their

refsplitr: Author name disambiguation, author georeferencing, and mapping of coauthorship networks with Web of Science data
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Summary The Science of Science (SciSci) is an emerging, trans-disciplinary approach for using large and disparate data -sets to study the emergence, dissemination, and impact of scientific research (Fortunato et al.). Bibliometric databases such as the Web of

Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community
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The capabilities of radar altimetry to measure inland water bodies are well established and several river altimetry 15 datasets are available. Here we produced a globally-distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean

Metadata for Administrative and Social Science Data
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Data are valuable but finding the right data is often difficult. This chapter reviews current approaches and issues for metadata about numeric data and data sets that facilitate the identification of relevant data . In addition, the chapter reviews how metadata support Online communities are now extremely numerous. Most of them being multifaceted, dynamic, and rapidly evolving, they are of the utmost interest for social science researchers. One of the special characteristics of these communities is the production of numerical traces

Data science : fundamental principles
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We live in a world where we collect huge amounts of data . Traditional methods and techniques are no longer sufficient to process them. In addition to the sophisticated development of computers, new ways of processing data are evolving. Data Science is a The Italian Research Conference on Digital Libraries (IRCDL) is an annual forum for the Italian research community to discuss the research topics pertaining to digital libraries and related technical, practical, and social issues both on the computer science and the Data science is the discipline which involves the study of information sources, representations, processing and finally conversion into valuable resources. This resource is an integral part development of business and other decision-making strategies. This

NumPy/SciPy Recipes for Data Science : Information Theoretic Vector Quantization
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Abstract In this note, we discuss how to implement the idea of information theoretic vector quantization using NumPy. Since our code is properly vectorized, it shows decent runtime performance I. INTRODUCTION The term vector quantization (VQ) commonly refers to the reasonable idea

Community detection and non-linear dimension reduction techniques in data science
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We studied two different approaches to interpreting high dimensional data sets: community detection and non-linear dimension reduction. Under community detection, we reviewed spectral clustering as presented in . We also examined the diffusion maps algorithm, a

Foundations for Private, Fair, and Robust Data Science
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Much of modern machine learning and statistics is based on the following paradigm: the algorithm designer specifies an objective function, and then optimizes it over some class of models. This is a powerful methodology, but while it generally results in a tool that is

Experimental Evidence Extraction System in Data Science with Hybrid Table Features and Ensemble Learning
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Data scientist was selected as the sexiest job of the 21st century and thus many higher education institutions have opened the new programs for data science training and research. Though data scientists are highly educated 88% have at least a Masters degree


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