Graduate Education in Statistics and Data Science: The Why, When, Where, Who, and What-Reference-Cited by-同舟云学术

Graduate Education in Statistics and Data Science: The Why, When, Where, Who, and What

Published:2021-03-07 Issue:1 Volume:8 Page:25-39
ISSN:2326-8298
Container-title:Annual Review of Statistics and Its Application
language:en
Short-container-title:Annu. Rev. Stat. Appl.

Author:

Aerts Marc¹,Molenberghs Geert¹²,Thas Olivier¹³⁴

Affiliation:

1. Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, BE3590 Hasselt, Belgium;

2. Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), KU Leuven, 3000 Leuven, Belgium

3. National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, Keiraville, New South Wales 2500, Australia

4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium

Abstract

Organizing a graduate program in statistics and data science raises many questions, offering a variety of opportunities while presenting a multitude of choices. The call for graduate programs in statistics and data science is overwhelming. How does it align with other (future) study programs at the secondary and postsecondary levels? What could or should be the natural home for data science in academia? Who meets the entry criteria, and who does not? Which strategic choices inevitably play a prominent role when developing a curriculum? We share our views on the why, when, where, who and what.

Publisher

Annual Reviews

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-040620-032820

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Consultancy Style Dissertations in Statistics and Data Science: Why and How;The American Statistician;2023-02-02

2. Data science curriculum in theiField;Journal of the Association for Information Science and Technology;2022-07-30

3. Data Science in Information, Communication and Technology;Studies in Big Data;2021-08-22