# Data Science and Agile (What Works, and What Doesn't)

DevFeed: [Data Science and Agile (What Works, and What Doesn't)](<https://devfeed.tech/articles/data-science-and-agile-what-works-and-what-doesn-t-46426.md>)

Original publisher: [Read original article](<https://eugeneyan.com//writing/data-science-and-agile-what-works-and-what-doesnt/>)

Author: Eugene Yan

Published: 2019-01-26T00:00:00Z

Content type: article

Language: en

Sources: [Eugene Yan](<https://devfeed.tech/sources/eugene-yan.md>)

Topics: [Data Science](<https://devfeed.tech/topics/data-science.md>), [Agile](<https://devfeed.tech/topics/agile.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Kanban](<https://devfeed.tech/topics/kanban.md>), [meetings](<https://devfeed.tech/topics/meetings.md>)

Tags: [agile](<https://devfeed.tech/tags/agile.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datascience](<https://devfeed.tech/tags/datascience.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [meetings](<https://devfeed.tech/tags/meetings.md>), [productivity](<https://devfeed.tech/tags/productivity.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [team](<https://devfeed.tech/tags/team.md>)

## AI overview

This article examines which aspects of Agile work well for data science teams and which fit less well. It argues that engineering-oriented work aligns better with Agile than research-oriented work, and discusses short sprints, stakeholder prioritization, planning meetings, effort estimation, and the costs of changing priorities.

## Source excerpt

A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).