Data Science: A Study from the Scientometric, Curricular, and Altmetric Perspectives

This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1.What are the scientometric features of the data science field? 2.What are the contributing fields to the establishment of data science? 3.What are the major research areas of the data science discipline? 4.What are the salient topics taught in the data science curriculum? 5.What topics appear in the Twitter-sphere regarding data science?6.What can be learned about data science from the scientometric, curricular, and altmetric analyses of the data collected? Using bibliometric data from the Scopus database for 1983-2021, the current study addresses the first three research questions. The fourth research question is answered with curricular data collected from U.S. educational institutions that offer data science programs. Altmetric data was gathered from Twitter for over 20 days to answer the fifth research question. All three sets of data are analyzed quantitatively and qualitatively. The scientometric portion of this study revealed a growing field, expanding beyond the borders of the United States and the United Kingdom into a more global undertaking. Computer Science and Statistics are foundational contributing fields with a host of additional fields contributing data sets for new data scientists to act, including, for example, the Biomedical and Information Science fields. When it comes to the question of salient topics across all three aspects of this research, it was revealed that a large degree of coherence between the three resulted in highlighting thirteen core topics of data science. However, it can be noted that Artificial Intelligence stood out among all the other groups with leading topics such as Machine Learning, Neural Networks, and Natural Language Processing. The findings of this study not only identify the major parameters of the data science field (e.g., leading researchers, the composition of the discipline) but also reveal its underlying intellectual structure and research fronts. They can help researchers to ascertain emerging topics and research fronts in the field. Educational programs in data science can learn from this study about how to update their curriculums and better prepare students for the rapidly growing field. Practitioners and other stakeholders of data science can also benefit from the present research to stay tuned and current in the field. Furthermore, the triple-pronged approach of this research provides a panoramic view of the data science field that no prior study has ever examined and will have a lasting impact on related investigations of an emerging discipline. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]