Relevant bibliographies by topics / Big Data Science / Journal articles
To see the other types of publications on this topic, follow the link: Big Data Science.

Journal articles on the topic 'Big Data Science'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Big Data Science.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wright, Alex. "Big data meets big science." Communications of the ACM 57, no. 7 (July 2014): 13–15. http://dx.doi.org/10.1145/2617660.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Broome, Marion E. "Big data, data science, and big contributions." Nursing Outlook 64, no. 2 (March 2016): 113–14. http://dx.doi.org/10.1016/j.outlook.2016.02.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

McCartney, Patricia R. "Big Data Science." MCN, The American Journal of Maternal/Child Nursing 40, no. 2 (2015): 130. http://dx.doi.org/10.1097/nmc.0000000000000118.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Morik, Katharina, Christian Bockermann, and Sebastian Buschjäger. "Big Data Science." KI - Künstliche Intelligenz 32, no. 1 (December 20, 2017): 27–36. http://dx.doi.org/10.1007/s13218-017-0522-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tonidandel, Scott, Eden B. King, and Jose M. Cortina. "Big Data Methods." Organizational Research Methods 21, no. 3 (November 16, 2016): 525–47. http://dx.doi.org/10.1177/1094428116677299.

Full text
Abstract:
Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.
APA, Harvard, Vancouver, ISO, and other styles
6

Mathias, Dr Elton, Dr Roveena Goveas, and Manish Rajak. "Clinical Research - A Big Data Science Approach." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 1075–78. http://dx.doi.org/10.31142/ijtsrd9547.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Chen, Yong, Hong Chen, Anjee Gorkhali, Yang Lu, Yiqian Ma, and Ling Li. "Big data analytics and big data science: a survey." Journal of Management Analytics 3, no. 1 (January 2, 2016): 1–42. http://dx.doi.org/10.1080/23270012.2016.1141332.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wang, Chunpeng, Ullrich Steiner, and Alessandro Sepe. "Synchrotron Big Data Science." Small 14, no. 46 (September 17, 2018): 1802291. http://dx.doi.org/10.1002/smll.201802291.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Delaney, Connie White, Jane Englebright, and Thomas Clancy. "Nursing Big Data Science." Journal of Nursing Scholarship 53, no. 3 (May 2021): 259–61. http://dx.doi.org/10.1111/jnu.12664.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Saez-Rodriguez, Julio, Markus M. Rinschen, Jürgen Floege, and Rafael Kramann. "Big science and big data in nephrology." Kidney International 95, no. 6 (June 2019): 1326–37. http://dx.doi.org/10.1016/j.kint.2018.11.048.

Full text
APA, Harvard, Vancouver, ISO, and other styles
11

Kauermann, Göran, and Helmut Küchenhoff. "Statistik, Data Science und Big Data." AStA Wirtschafts- und Sozialstatistisches Archiv 10, no. 2-3 (July 14, 2016): 141–50. http://dx.doi.org/10.1007/s11943-016-0188-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
12

Galeano, Pedro, and Daniel Peña. "Data science, big data and statistics." TEST 28, no. 2 (April 8, 2019): 289–329. http://dx.doi.org/10.1007/s11749-019-00651-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
13

Jifa, Gu, and Zhang Lingling. "Data, DIKW, Big Data and Data Science." Procedia Computer Science 31 (2014): 814–21. http://dx.doi.org/10.1016/j.procs.2014.05.332.

Full text
APA, Harvard, Vancouver, ISO, and other styles
14

Fox, Peter, and James Hendler. "The Science of Data Science." Big Data 2, no. 2 (June 2014): 68–70. http://dx.doi.org/10.1089/big.2014.0011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

Petrovic, Jelisaveta. "Big data - a big deal for sociology?" Sociologija 60, no. 3 (2018): 557–82. http://dx.doi.org/10.2298/soc1803557p.

Full text
Abstract:
The paper critically examines the attitude of the mainstream sociology towards the study of big data in social sciences. Content analysis of the scientific papers published in the top-tier sociological journals ranked on the Thomson Reuters Impact Factor ssci list (2000-2017) shows that, in the observed period, the issue of big data was largely neglected. This topic is still rather invisible in the mainstream sociological thought, although it draws a lot of attention outside the academia. The analysis of big data within mainstream sociology is dominated by a critical perspective, while the application of the big data analytics is quite rare. In the concluding section, the importance of the big data study for sociology is emphasised. Moreover, it is pointed out at the risk of auto-marginalization in case of neglecting the ?tectonic? changes induced by the big data analytics in the space once dominated by the social sciences. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 179035: Izazovi nove drustvene integracije u Srbiji: koncepti i akteri]
APA, Harvard, Vancouver, ISO, and other styles
16

Vignieri, Sacha. "Ecological “big data”." Science 370, no. 6517 (November 5, 2020): 677.6–678. http://dx.doi.org/10.1126/science.370.6517.677-f.

Full text
APA, Harvard, Vancouver, ISO, and other styles
17

Hack, James J., and Michael E. Papka. "Big Data: Next-Generation Machines for Big Science." Computing in Science & Engineering 17, no. 4 (July 2015): 63–65. http://dx.doi.org/10.1109/mcse.2015.78.

Full text
APA, Harvard, Vancouver, ISO, and other styles
18

Hassani, Hossein, Stephan Unger, and Christina Beneki. "Big Data and Actuarial Science." Big Data and Cognitive Computing 4, no. 4 (December 19, 2020): 40. http://dx.doi.org/10.3390/bdcc4040040.

Full text
Abstract:
This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.
APA, Harvard, Vancouver, ISO, and other styles
19

PINKELMAN, Jim, and Tony HEY. "Big Data Is Transforming Science." TRENDS IN THE SCIENCES 18, no. 9 (2013): 9_45–9_51. http://dx.doi.org/10.5363/tits.18.9_45.

Full text
APA, Harvard, Vancouver, ISO, and other styles
20

Baranyi, J. "Big data and food science." Acta Alimentaria 49, no. 1 (March 2020): 1–3. http://dx.doi.org/10.1556/066.2020.49.1.1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
21

Shneiderman, B. "The Big Picture for Big Data: Visualization." Science 343, no. 6172 (February 13, 2014): 730. http://dx.doi.org/10.1126/science.343.6172.730-a.

Full text
APA, Harvard, Vancouver, ISO, and other styles
22

May, Mike. "Big data, big picture: Metabolomics meets systems biology." Science 356, no. 6338 (May 11, 2017): 646–48. http://dx.doi.org/10.1126/science.356.6338.646.

Full text
APA, Harvard, Vancouver, ISO, and other styles
23

Panesar, Sandip S., and Juan Fernandez-Miranda. "Big Data, Big Impact: The Potential for Data Science in Neurosurgery." World Neurosurgery 138 (June 2020): 558–59. http://dx.doi.org/10.1016/j.wneu.2020.03.182.

Full text
APA, Harvard, Vancouver, ISO, and other styles
24

Cate, F. H. "The big data debate." Science 346, no. 6211 (November 13, 2014): 818. http://dx.doi.org/10.1126/science.1261092.

Full text
APA, Harvard, Vancouver, ISO, and other styles
25

Pal, Sankar K., Saroj K. Meher, and Andrzej Skowron. "Data science, big data and granular mining." Pattern Recognition Letters 67 (December 2015): 109–12. http://dx.doi.org/10.1016/j.patrec.2015.08.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
26

Ch. Sai Krishna Manohar, Ch Sai Krishna Manohar. "Analytics of Data Science using Big Data." IOSR Journal of Computer Engineering 10, no. 2 (2013): 19–21. http://dx.doi.org/10.9790/0661-01021921.

Full text
APA, Harvard, Vancouver, ISO, and other styles
27

Ohno-Machado, L. "Big science, big data, and a big role for biomedical informatics." Journal of the American Medical Informatics Association 19, e1 (June 1, 2012): e1-e1. http://dx.doi.org/10.1136/amiajnl-2012-001052.

Full text
APA, Harvard, Vancouver, ISO, and other styles
28

Waldherr, Annie, Daniel Maier, Peter Miltner, and Enrico Günther. "Big Data, Big Noise." Social Science Computer Review 35, no. 4 (May 9, 2016): 427–43. http://dx.doi.org/10.1177/0894439316643050.

Full text
Abstract:
In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case.
APA, Harvard, Vancouver, ISO, and other styles
29

May, M. "LIFE SCIENCE TECHNOLOGIES: Big biological impacts from big data." Science 344, no. 6189 (June 12, 2014): 1298–300. http://dx.doi.org/10.1126/science.344.6189.1298.

Full text
APA, Harvard, Vancouver, ISO, and other styles
30

Coveney, Peter V., Edward R. Dougherty, and Roger R. Highfield. "Big data need big theory too." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2080 (November 13, 2016): 20160153. http://dx.doi.org/10.1098/rsta.2016.0153.

Full text
Abstract:
The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.
APA, Harvard, Vancouver, ISO, and other styles
31

Chin, G. "Biting into Big Data in the Big Apple." Science 345, no. 6203 (September 18, 2014): 1464. http://dx.doi.org/10.1126/science.345.6203.1464-a.

Full text
APA, Harvard, Vancouver, ISO, and other styles
32

Scaife, Anna M. M., and Sally E. Cooper. "The DARA Big Data Project." Proceedings of the International Astronomical Union 14, A30 (August 2018): 569. http://dx.doi.org/10.1017/s174392131900543x.

Full text
Abstract:
AbstractThe DARA Big Data project is a flagship UK Newton Fund & GCRF program in partnership with the South African Department of Science & Technology (DST). DARA Big Data provides bursaries for students from the partner countries of the African VLBI Network (AVN), namely Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia and Zambia, to study for MSc(R) and PhD degrees at universities in South Africa and the UK. These degrees are in the three data intensive DARA Big Data focus areas of astrophysics, health data and sustainable agriculture. The project also provides training courses in machine learning, big data techniques and data intensive methodologies as part of the Big Data Africa initiative.
APA, Harvard, Vancouver, ISO, and other styles
33

Broniatowski, D. A., M. J. Paul, and M. Dredze. "Twitter: Big data opportunities." Science 345, no. 6193 (July 10, 2014): 148. http://dx.doi.org/10.1126/science.345.6193.148-a.

Full text
APA, Harvard, Vancouver, ISO, and other styles
34

Hines, Pamela J. "Insights from big data." Science 369, no. 6510 (September 17, 2020): 1443.10–1445. http://dx.doi.org/10.1126/science.369.6510.1443-j.

Full text
APA, Harvard, Vancouver, ISO, and other styles
35

Osborne, I. S. "Dealing with big data." Science 349, no. 6247 (July 30, 2015): 491–92. http://dx.doi.org/10.1126/science.349.6247.491-g.

Full text
APA, Harvard, Vancouver, ISO, and other styles
36

Bail, Christopher A. "Taming Big Data." Sociological Methods & Research 46, no. 2 (July 9, 2016): 189–217. http://dx.doi.org/10.1177/0049124115587825.

Full text
Abstract:
Social media websites such as Facebook and Twitter provide an unprecedented amount of qualitative data about organizations and collective behavior. Yet these new data sources lack critical information about the broader social context of collective behavior—or protect it behind strict privacy barriers. In this article, I introduce social media survey apps (SMSAs) that adjoin computational social science methods with conventional survey techniques in order to enable more comprehensive analysis of collective behavior online. SMSAs (1) request large amounts of public and non-public data from organizations that maintain social media pages, (2) survey these organizations to collect additional data of interest to a researcher, and (3) return the results of a scholarly analysis back to these organizations as incentive for them to participate in social science research. SMSAs thus provide a highly efficient, cost-effective, and secure method for extracting detailed data from very large samples of organizations that use social media sites. This article describes how to design and implement SMSAs and discusses an application of this new method to study how nonprofit organizations attract public attention to their cause on Facebook. I conclude by evaluating the quality of the sample derived from this application of SMSAs and discussing the potential of this new method to study non-organizational populations on social media sites as well.
APA, Harvard, Vancouver, ISO, and other styles
37

Pardee, R. "PS1-6: Big Data, Data Science and You: Demystifying Some Big Ideas." Clinical Medicine & Research 12, no. 1-2 (September 1, 2014): 111. http://dx.doi.org/10.3121/cmr.2014.1250.ps1-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
38

Wrobel, Stefan, Hans Voss, Joachim Köhler, Uwe Beyer, and Sören Auer. "Big Data, Big Opportunities." Informatik-Spektrum 38, no. 5 (June 24, 2014): 370–78. http://dx.doi.org/10.1007/s00287-014-0806-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
39

Wax, Amy, Raquel Asencio, and Dorothy R. Carter. "Thinking Big About Big Data." Industrial and Organizational Psychology 8, no. 4 (December 2015): 545–50. http://dx.doi.org/10.1017/iop.2015.81.

Full text
Abstract:
Guzzo, Fink, King, Tonidandel, and Landis (2015) review important issues—privacy, informed consent, and data/data analysis integrity—that are critical logistical considerations in any program of research with human subjects, including studies utilizing big data. Overall, we agree with the cautionary sentiment conveyed throughout the focal article; industrial and organizational (I-O) psychology researchers and practitioners should not assume that big data is a panacea, and many of our established disciplinary approaches for ensuring ethical and accurate research are applicable—or modifiable—in big data contexts. However, we believe that the conversation about big data in I-O psychology is broader than that reviewed by Guzzo et al., and we would like to further elaborate on the focal article. We present this commentary from our perspective as junior scholars entering the field at a critical time—a time when I-O psychology is becoming increasingly intertwined with big data science.
APA, Harvard, Vancouver, ISO, and other styles
40

Aalst, Wil van der, and Ernesto Damiani. "Processes Meet Big Data: Connecting Data Science with Process Science." IEEE Transactions on Services Computing 8, no. 6 (November 1, 2015): 810–19. http://dx.doi.org/10.1109/tsc.2015.2493732.

Full text
APA, Harvard, Vancouver, ISO, and other styles
41

Serik, M., G. Nurbekova, and J. Kultan. "Big data technology in education." Bulletin of the Karaganda University. Pedagogy series 100, no. 4 (December 28, 2020): 8–15. http://dx.doi.org/10.31489/2020ped4/8-15.

Full text
Abstract:
The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.
APA, Harvard, Vancouver, ISO, and other styles
42

Cass, T. "SOFTWARE:A Handler for Big Data." Science 282, no. 5389 (October 23, 1998): 636. http://dx.doi.org/10.1126/science.282.5389.636.

Full text
APA, Harvard, Vancouver, ISO, and other styles
43

Concolato, Claude E., and Li M. Chen. "Data Science: A New Paradigm in the Age of Big-Data Science and Analytics." New Mathematics and Natural Computation 13, no. 02 (July 2017): 119–43. http://dx.doi.org/10.1142/s1793005717400038.

Full text
Abstract:
As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.
APA, Harvard, Vancouver, ISO, and other styles
44

Martínez-Álvarez, Francisco, Alicia Troncoso, and José Riquelme. "Data Science and Big Data in Energy Forecasting." Energies 11, no. 11 (November 21, 2018): 3224. http://dx.doi.org/10.3390/en11113224.

Full text
Abstract:
This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.
APA, Harvard, Vancouver, ISO, and other styles
45

Brady, Henry E. "The Challenge of Big Data and Data Science." Annual Review of Political Science 22, no. 1 (May 11, 2019): 297–323. http://dx.doi.org/10.1146/annurev-polisci-090216-023229.

Full text
Abstract:
Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about society by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences, and to generate predictions. They also pose challenges as social scientists must grasp the meaning of concepts and predictions generated by convoluted algorithms, weigh the relative value of prediction versus causal inference, and cope with ethical challenges as their methods, such as algorithms for mobilizing voters or determining bail, are adopted by policy makers.
APA, Harvard, Vancouver, ISO, and other styles
46

Chaturvedi, Shubhankar, and Shwetank Kanava. "Big Data: Data Science Applications and Present Scenario." International Journal of Engineering Trends and Technology 67, no. 1 (January 25, 2019): 57–59. http://dx.doi.org/10.14445/22315381/ijett-v67i1p210.

Full text
APA, Harvard, Vancouver, ISO, and other styles
47

Delicado, Pedro. "Comments on: Data science, big data and statistics." TEST 28, no. 2 (April 8, 2019): 334–37. http://dx.doi.org/10.1007/s11749-019-00639-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
48

Shi, Jian Qing, and Shane Halloran. "Comments on: Data science, big data and statistics." TEST 28, no. 2 (April 8, 2019): 353–56. http://dx.doi.org/10.1007/s11749-019-00640-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
49

Tsay, Ruey S. "Comments on: Data science, big data and statistics." TEST 28, no. 2 (April 8, 2019): 357–59. http://dx.doi.org/10.1007/s11749-019-00641-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
50

Genton, Marc G., and Ying Sun. "Comments on: Data science, big data and statistics." TEST 28, no. 2 (April 8, 2019): 338–41. http://dx.doi.org/10.1007/s11749-019-00642-w.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the bibliographies on the topic 'Big Data Science' for other source types:
Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography