Top Research Papers on Data Science

Koby Mike and Orit Hazzan consider why multiple definitions are needed to pin down data science.

Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets and takes up other challenges, such as the capturing, cleaning, and transforming of unstructured social media and web data; the use of big-data technologies to store and process big, unstructuring data sets; and questions related to data ethics and regulation.

Among the most popular integrators such as Runge–Kutta methods, time-splitting, exponential integrators and Lawson methods, exponential Lawson multistep methods with one predictor–corrector step provide the best stability and accuracy at the least effort.

mathematics

The main aim of Data Science search out turn big sets of two together unorganized and organized data into valuable news that can help organisations to create strong data-compelled resolutions.

The author wants to create a diabetes prediction system independently through a website-based application system using the XGBoost algorithm, which has an accuracy of 74.67%, a precision value of 57.40%, a recall value of 65.94% and a specificity value of 78.50%.

This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of dataScience, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of datascience.

The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree and there are four specializations: Analytics and Modeling, Analytics Management, Artificial Intelligence, Data Engineering and Technology Entrepreneurship.

A review of the impact of information security on the government and companies is described in terms of threats and types of information safety, including application security, cloud security, cryptography, security infrastructure, incident response, and vulnerability management.

Design, development, evaluation 3D user interfaces, Symbolic, menu, gestural, and multimodal interaction, interaction techniques metaphors, immersive.

While it may not be possible to build a data brain identical to a human, data science can still aspire to imaginative machine thinking.

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The Bachelor of Science in Data Science studies the collection, manipulation, storage, retrieval, and computational analysis of data in its various forms, including numeric, textual, image, and video data from small to large volumes. The program combines computer science, information science, mathematics, statistics, and probability theory into an integrated curriculum that prepares students for careers or graduate studies in big data analysis, data science, and data analytics. The coursework covers exploratory data analysis, data manipulation in a variety of programming languages, large-scale...

This paper aims to reveal the obstacle and limitations of other science into a data science completely, on that basis the definition of data sciences needs to be elaborated, then confirm data science as new science and not depend directly on several other sciences.

The Bachelor of Science in Data Science studies the collection, manipulation, storage, retrieval, and computational analysis of data in its various forms, including numeric, textual, image, and video data from small to large volumes. The program combines computer science, information science, mathematics, statistics, and probability theory into an integrated curriculum that prepares students for careers or graduate studies in big data analysis, data science, and data analytics. The coursework covers exploratory data analysis, data manipulation in a variety of programming languages, large-scale...

mathematics

I came into data science from the industrial side, and when I saw that Harvard Business Review already in 2012 had declared “Data Scientist” to be “The Sexiest Job of the 21st Century” [3], I wanted to become one too.

Broad discussion of data management in the sciences, and how libraries and librarians can embed themselves in the data lifecycle are presented, along with specific examples of how libraries have become involved with research data services.

The technological revolution has led to an explosion of data in domains of knowledge, and new methodologies have emerged to power intelligent systems, make more accurate predictions, and gain new insight using the large volumes of data generated by scientists, entrepreneurs, and analysts.

Machine learning is a highly influential field that has made major contributions to the increased effectiveness of artificial intelligence by utilizing different methods, four of which have been particularly effective.