Statistical Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1, 124, 254, 297 History Corner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20, 179 Teacher’s Corner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26, 173, 263, 335 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 147, 211, 366 Statistical Computing and Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Statistical Computing and Software Reviews . . . . . . . . . . . . . . . . . . . . . . . . 75, 187 Reviews of Books and Teaching Materials . . . . . . . . . . . . . . . . . 92, 189, 281, 401 Brief Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100, 195, 292, 404 Letters to the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102, 197, 294, 406 Special Section: Statistical Training and Curricular Revision . . . . . . . . . . . . . 105 Errata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Special Section: Opportunities and Challenges for the Discipline . . . . . . . . . 201 Software Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

The American statistician

The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.

https://dl.acm.org/doi/pdf/10.1145/3076253

Data Science: A Comprehensive Overview

The twenty-first century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This paper provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons and thinking about data science and analytics.

Big Data and Data Science in Critical Care.

Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.

https://www.pnas.org/content/pnas/114/33/8689.full.pdf

Science and data science

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to use databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this article is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of und...

https://www.stat.purdue.edu/~mdw/papers/paper032.pdf

O. Hall-Holt

Papers

Data Science in Statistics Curricula: Preparing Students to “Think with Data”