Modern data and applications pose very different challenges from those of the
1950s or even the 1980s. Students contemplating a career in statistics or data
science need to have the tools to tackle problems involving massive,
heavy-tailed data, often interacting with live, complex systems. However,
despite the deepening connections between engineering and modern data science,
we argue that training in classical statistical concepts plays a central role
in preparing students to solve Google-scale problems. To this end, we present
three industrial applications where significant modern data challenges were
overcome by statistical thinking.
%0 Generic
%1 chamandy2015teaching
%A Chamandy, Nicholas
%A Muralidharan, Omkar
%A Wager, Stefan
%D 2015
%K statistics
%T Teaching Statistics at Google Scale
%U http://arxiv.org/abs/1508.01278
%X Modern data and applications pose very different challenges from those of the
1950s or even the 1980s. Students contemplating a career in statistics or data
science need to have the tools to tackle problems involving massive,
heavy-tailed data, often interacting with live, complex systems. However,
despite the deepening connections between engineering and modern data science,
we argue that training in classical statistical concepts plays a central role
in preparing students to solve Google-scale problems. To this end, we present
three industrial applications where significant modern data challenges were
overcome by statistical thinking.
@misc{chamandy2015teaching,
abstract = {Modern data and applications pose very different challenges from those of the
1950s or even the 1980s. Students contemplating a career in statistics or data
science need to have the tools to tackle problems involving massive,
heavy-tailed data, often interacting with live, complex systems. However,
despite the deepening connections between engineering and modern data science,
we argue that training in classical statistical concepts plays a central role
in preparing students to solve Google-scale problems. To this end, we present
three industrial applications where significant modern data challenges were
overcome by statistical thinking.},
added-at = {2020-06-02T18:50:54.000+0200},
author = {Chamandy, Nicholas and Muralidharan, Omkar and Wager, Stefan},
biburl = {https://www.bibsonomy.org/bibtex/250890d321ff3f4a44c26183371eb0b5c/cpankow},
description = {[1508.01278] Teaching Statistics at Google Scale},
interhash = {5de962b8a7f3312b4c758105997bb760},
intrahash = {50890d321ff3f4a44c26183371eb0b5c},
keywords = {statistics},
note = {cite arxiv:1508.01278Comment: To appear in The American Statistician},
timestamp = {2020-06-02T18:50:54.000+0200},
title = {Teaching Statistics at Google Scale},
url = {http://arxiv.org/abs/1508.01278},
year = 2015
}