Recreations in Randomness: Data Science After Hours

Editor-in-Chiefʼs Note: Since its debut in 2020, the Recreations in Randomness column has invited readers to discover data science in activities they already enjoy-or would like to explore-from cooking and sports to poetry and art.These familiar pursuits offer fertile ground for examining unfamiliar questions about data, uncertainty, prediction, and inference.Put simply, the aim is to take both learning and enjoyment seriously.I am delighted to welcome Stephanie Kovalchik as co-editor alongside Mark Glickman, my colleague, who gave this column both its fitting title and its life in 2020.As their invitation below makes clear, the possibilities continue to expand, especially as AI becomes a companion in our recreational lives.But a companion can also change the game, sometimes literally.If an algorithm helps us choose our next book, compose a song, or perfect a strategy, have we enriched the experience or outsourced part of what made it enjoyable?And who gets to decide what counts as ʻbetterʼ when the objective is pleasure?Such questions deserve both quantitative care and a playful spirit.Alongside Mark and Stephanie, I particularly encourage essays that introduce readers to the intellectual pleasures of recreational practices beyond their own cultural settings.A favorite pastime may be the starting point; the path of discovery may lead to a deeper appreciation of how we measure, reason, and create.Along the way, recreation may become creation: even a humanoid robotʼs comical running style, as seen during the 2026 World Humanoid Games in Beijing, might inspire us to ponder why such an awkward-looking stride could be a very efficient one.In a nutshell, I hope you will join Mark and Stephanie in showing how much there is to learn when we take pleasure seriously.

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Publication Details

Journal
Harvard Data Science Review
Published
2026-10-29
DOI
https://doi.org/10.1162/99608f92.5965bffd
Primary Topic
Statistics Education and Methodologies
Type
article
Field-Weighted Citation Impact
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article

Recreations in Randomness: Data Science After Hours

Mark E. Glickman, Stephanie Ann Kovalchik
1 links
Harvard Data Science Review
Statistics Education and Methodologies
article

Recreations in Randomness: Data Science After Hours

Mark E. Glickman, Stephanie Ann Kovalchik
article en

Abstract

Editor-in-Chiefʼs Note: Since its debut in 2020, the Recreations in Randomness column has invited readers to discover data science in activities they already enjoy-or would like to explore-from cooking and sports to poetry and art.These familiar pursuits offer fertile ground for examining unfamiliar questions about data, uncertainty, prediction, and inference.Put simply, the aim is to take both learning and enjoyment seriously.I am delighted to welcome Stephanie Kovalchik as co-editor alongside Mark Glickman, my colleague, who gave this column both its fitting title and its life in 2020.As their invitation below makes clear, the possibilities continue to expand, especially as AI becomes a companion in our recreational lives.But a companion can also change the game, sometimes literally.If an algorithm helps us choose our next book, compose a song, or perfect a strategy, have we enriched the experience or outsourced part of what made it enjoyable?And who gets to decide what counts as ʻbetterʼ when the objective is pleasure?Such questions deserve both quantitative care and a playful spirit.Alongside Mark and Stephanie, I particularly encourage essays that introduce readers to the intellectual pleasures of recreational practices beyond their own cultural settings.A favorite pastime may be the starting point; the path of discovery may lead to a deeper appreciation of how we measure, reason, and create.Along the way, recreation may become creation: even a humanoid robotʼs comical running style, as seen during the 2026 World Humanoid Games in Beijing, might inspire us to ponder why such an awkward-looking stride could be a very efficient one.In a nutshell, I hope you will join Mark and Stephanie in showing how much there is to learn when we take pleasure seriously.

Harvard Data Science ReviewVol. 8(4)
Harvard University (US)
Openalex Percentile: Top 12%
Statistics Education and Methodologies
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