What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

Ley, Christophe; Bordas, Stéphane

doi:10.1007/s41060-017-0090-x

Cited by 36 publications

(15 citation statements)

References 63 publications

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“…This article presents a deep neural network (DNN) model, which is a subset of machine learning (ML), for the prediction and understanding of foamed concrete strength. ML and data science has shown great potential for predicting, designing, and discovering materials (Ley & Bordas, ). In civil engineering and construction, ML has been extensively used in a variety of applications such as structural heal monitoring (Gao & Mosalam, ; Rafiei & Adeli, , ; Xue & Li, ), reliability analysis (Dai & Cao, ; Grande, Castillo, Mora, & Lo, ; Nabian & Meidani, ), transportation (Dharia & Adeli, ; García‐Ródenas, López‐García, & Sánchez‐Rico, ; Yu, Wang, Shan, & Yao, ; Zhang & Ge, ), and prediction and estimation (Adeli & Wu, ; Chou & Pham, ; Rafiei, Khushefati, Demirboga, & Adeli, ; Zhao & Ren, ).…”

Section: Introductionmentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

196

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The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

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Section: Introductionmentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

196

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“…Data science centres on the notion of multidisciplinary and interdisciplinary approaches to extracting knowledge or insights from large quantities of complex data for use in a broad range of applications [1]. It incorporates knowledge from Statistics, Computer Science and Mathematics and hence can deal with challenging application domains which had remained out of reach because of a combined lack of data and computer power [27]. Data scientists are very much in demand as companies grapple with the challenge of making valuable discoveries from Big Data.…”

Section: Resultsmentioning

confidence: 99%

Information Retrieval in Data Science Curricula

Bukhari¹

2020

7th International Conference on Artificial Intelligence and Applications

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Data scientists are very much in demand as companies grapple with the challenge of making valuable discoveries from Big Data. Therefore, academic institutions have started to offer different kind of data science DS programs and they strive to prepare students to be data scientists who are capable to face the challenge of the new age. As an interdisciplinary field, DS programs should represent a combination of subject areas from several disciplines. Consequently, schools that host data science programs are diverse. Until now few studies have investigated data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that explore DS programs and investigate its curriculum structure across disciplines. This study conducted an exploratory content analysis of 30 United States' DS programs from a variety of disciplines. The present study seeks to depict the current state of DS education in the U.S. to explore what discipline DS programs covers at the graduate level. The analysis was conducted on course titles and course descriptions. The results show that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. It can be said that all DS programs requires the basic knowledge of database design, representation, extraction and management. DS programs delivered information skills through their core courses. Results indicates that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Required courses also addressed communication visualization and mathematics skills.

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“…According to Diggle (2015), Statistics is the Data Science of our modern times. In the same vein, Ley and Bordas (2018) claim that Data Science is actually Statistics 2.0. What may set Data Science apart from the more "classical" Statistics (probability model building, data description, inference, and prediction) is the incorporation of machine learning (supervised, unsupervised, and reinforcement learning) and computer science besides these classical statistics.…”

Section: What Does Data Science Actually Mean?mentioning

confidence: 99%

Data-Centric Engineering in modern science from the perspective of a statistician, an engineer, and a software developer

Ley

Tibolt

Fromme

2020

DCE

Self Cite

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Data-Centric Engineering is an emerging branch of science that certainly will take on a leading role in data-driven research. We live in the Big Data era with huge amounts of available data and unseen computing power, and therefore a crafty combination of Statistics (or, in more modern terms, Data Science), Computer Science and Engineering is required to filter out the most important information, master the ever more difficult challenges of a changing world and open new paths. In this paper, we will highlight some of these aspects from a combined perspective of a statistician, an engineer and a software developer. In particular, we will focus on sound data handling and analysis, computational science in Structural Engineering, data care, security and monitoring, and conclude with an outlook on future developments.

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What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

Cited by 36 publications

References 63 publications

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Information Retrieval in Data Science Curricula

Data-Centric Engineering in modern science from the perspective of a statistician, an engineer, and a software developer

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