Top Research Papers on Data Science

This article discusses data science from three perspectives: statistical, computational, and human, and argues that the effective combination of all three components is the essence of what data science is about.

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.

This report summarizes two talks that I gave at the Advanced Future Studies at Kyoto University in February of 2016, which provided an overview of an emerging research trend—the emergence of a new discipline called the Science of Science.

Learn about data science resources, analysis, communities and data management, and also learn about hte datasets openly available and dataset purchase program.

Data Science (DS) as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections, but it will take much more time to implement Open Science (OS) than the authors may have expected.

This discussion is about changes in the world, changes that affect us as academics, changes that affect us as empirical researchers (qualitative and quantitative), and changes that affect our students and our universities/ colleges I am an empirical social scientist, a political methodologist, and a statistician, so I will only discuss topics along these lines where I am qualified to make comments As of this re-rewriting, the country and the world are in various levels of lockdown and recovery because of Covid-19 During this difficult process it is clear that data and privacy issues are changi...

A VP of Engineering at a startup doing data mining and machine learning research explains how to get research into the hands of customers faster.

A new methodology for analysis of precipitate shapes using a segmentation-free approach based on the histogram of oriented gradients feature descriptor (HOG), a classic tool in image analysis, is demonstrated.

Lessons learned managing a data science research team are shared to help improve the quality of research and reduce the amount of uncertainty in the research process.

. The M3-Competition found that simple models outperform more complex ones for time series forecasting. As part of these competitions, several claims were made that statistical models exceeded machine learning (ML) techniques, such as recurrent neural networks (RNN), in prediction performance. These findings may over-generalize the capabilities of statistical models since the analysis measured the total forecasting accuracy across a wide range of industries and fields and with different interval lengths. This investigation aimed to assess how statistical and ML methods compared when individuat...

The activities of theBD2K TCC are described and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials.

These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field will catalyze significant advances in the understanding of society, health, and human behavior and increase the impact of the research.

The result of this study concludes that review helpfulness can be effectively predicted through the deployment of model features and enable strategies to moderate a user review post to improve the helpfulness quality of a review.

This paper researches application of Machine Learning approaches for memory element failure analysis which could mimic simulation-like accuracy and minimize the need for engineers to rely heavily on simulators for their validations.

A novel machine learning-based framework, the DARTH framework, that characterizes and combines multiple models, with one model for each composite feature, that enables the accurate identification of phishing emails is presented.

This paper explores in-depth the simulation model of Moving Average and Moving Average Convergence/Divergence (MACD) to come up with optimized parameters that will allow traders to profit from trading Dow Jones Industrial Index and Hang Seng Index.

Information system educators who can gain a better understanding of current trends in data science/analytics education and other information system researchers who are interested in how data science /analytics might impact the broader field of information systems and management education should find interest in this report.

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.

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.

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.

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.

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

Les information détaillées à propos de chaque cours sont disponibles en cliquant sur le code cours. En particulier, l’horaire précis, jour par jour, et les locaux correspondants sont accessibles via la rubrique “Horaire”. Detailed information about each course unit is available by clicking the course code. In particular, the detailed schedule, day by day, and the corresponding classrooms are provided under the “Schedule” sub-title.

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.

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.

Understanding published research results should be through one’s own eyes and include the raw diffraction data, an option that has recently become viable at various data archives.

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...

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.

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...

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%.

The Master of Data Science (Non-Thesis) program is designed to give candidates a foundation in statistics and computer science and also provide knowledge in a particular application domain of science or engineering. The balance between these three elements is a strength of the program and can prepare candidates for Data Science careers in industry, government, or for further study at the PhD level. Throughout is an emphasis on working in teams, creative problem solving, and professional development.

This book provides an overview of data science, lays out the groundwork for understanding data, and outlines the steps involved in the field’s evolution.

mathematics

Data collection, storage, and processing has never been simpler for businesses. Recent advances in high-performance computers, the proliferation of social media and big data, and the advent of cutting-edge methodologies for data analysis and modeling like deep learning have all contributed to the surge in demand for data scientists. Data science is a framework for discovering hidden insights in massive datasets by the use of certain concepts, issue descriptions, algorithms, and procedures. Although its reach is wider, it has strong ties to data mining and ML. This book provides an overview of ...

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.

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

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.

The Master of Data Science (Non-Thesis) program is designed to give candidates a foundation in statistics and computer science and also provide knowledge in a particular application domain of science or engineering. The balance between these three elements is a strength of the program and can prepare candidates for Data Science careers in industry, government, or for further study at the PhD level. Throughout is an emphasis on working in teams, creative problem solving, and professional development.

An increasing number of consequential decisions are made automatically by software that employs machine learning, data analytics, and artificial intelligence to discover decision rules using data to ensure good governance of these technologies and building accountable algorithms.

This panel will explore what DS education and CS education can learn from each other, how each can contribute and advance the goals of the other, and how these two intertwined disciplines can productively live alongside each other in K-12 settings.

This chapter investigates the limits and criticisms of the existing legal framework and the possible options to provide adequate answers to the new challenges of Big Data processing and suggests a broader approach that encompasses the collective dimension of data protection.

. A chasm exists between the active public equity investment management industry's fundamental, momentum, and quantitative styles. In this study, the researchers explore ways to bridge this gap by leveraging domain knowledge, fundamental analysis, momentum, crowdsourcing, and data science methods. This research also seeks to test the developed tools and strategies during the volatile time period of 2020 and 2021.

The study shows that assigning higher driving factors to certain aspects and genre result in the higher accuracy of the sentiment prediction models that utilized in this research.

Key features of ADS are the replacement of rudimentary data exploration and processing steps with automation and the augmentation of data scientist judgment with automatically-generated insights in a domain-agnostic way to facilitate the data science process.

This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features.

This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression and relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the PySR algorithm to determine the symbolic relationships.

Using the Jaccard similarity coefficient, in the knowledge graph, this study is able to identify and explore relationships between COVID-19 cases as well as predict the vulnerability of general population in a vicinity.

A novel application of Natural Language Processing techniques to classify unstructured text into toxic and non-toxic categories and showed a very promising accuracy of more than 70% performance by LSTM among all algorithms.

This study aims to benchmark this framework using a large dataset and a virtual machine (VM) to evaluate its viability in mitigating the computational load of SHAP calculations and preliminary results show promising improvements.

This paper examines the Finance and Banking industry, highlighting its issues and emphasizing the crucial role that Data Science plays in solving them.