جستجوی مقالات مرتبط با کلیدواژه "data science" در نشریات گروه "ریاضی"

The study of the relation between data science as an emergent phenomenon in 21st century and statistics, an original and applicable science, is one the most challenged topics by different researchers. Data science is dependent to some theoretical basics such as linear models, clustering and etc, which are from statistics. Some of the scientists believe that data science is begotten from statistics and consider it as statistics’ offspring. Some others consider data science as a branch of statistics using modern computational techniques. Also, there is an uncommon idea that considers data science as a separated scientific field from statistics. In this paper, we recall theoretical basics of data science and bring up comments and interpretations of some well-known scientists about the fact that data science is not separate from statistics. Finally, we consider Statistics+ as another name of data science. Additionally, this study aims to explore the practical implications of this relatio ship by examining how advancements in data science continue to reshape and augment statistical methodologies. Through a comprehensive review of contemporary applications, we aim to illustrate the dynamic interplay between data science and statistics, illuminating their evolving synergy.

More than 50 years ago, J. Tukey called for a reformation of academic statistics. In “The ‎f‎uture of dataanalysis,” he pointed to the existence of an as-yet unrecognized science, whose subject of interest waslearning from data, or “data analysis.” A recent and growing phenomenon has been the emergence of “data science” programs atmajor universities. This article reviews some ingredients of the current “data science moment,” including recent commentary about data science in the popular media, and about how/whether data science is really different from statistics. The now-contemplated field of data science amounts to a superset of the fields of statistics and machine learning, which adds sometechnology for “scaling up” to “big data.” Because all of science itself will soon become data that can be mined, the imminentrevolution in data science is not about mere “scaling up,” but instead the emergence of scientific studies ofdata analysis science-wide. I present a vision of data science based on the activities of people who are “learning from data,” and I describe an academic field dedicated to improving that activity in an evidence-basedmanner. This new field is a better academic enlargement of statistics and machine learning than today’s data science initiatives, while being able to accommodate thesame short-term goals.

The COVID-19 global pandemic has required everyone to make sense of data about community spread, levels of risk, and vaccine efficacy. Yet research shows that students are underprepared in data literacy. Tanya LaMar and Jo Boaler argue that data science education provides an opportunity to address this problem while providing much needed updates to the current mathematics curriculum. The integration of data science can provide a more equitable mathematics pipeline than the calculus-focused pathway that has excluded most students from a future in mathematics. Through data science, students can learn to answer questions that are relevant to their lives and communities, to be critical consumers of the data that surround them every day, and to wield the power of data analysis.