# Personas, data science, k-means

DevFeed: [Personas, data science, k-means](<https://devfeed.tech/articles/personas-data-science-k-means-41169.md>)

Original publisher: [Read original article](<https://www.craigkerstiens.com/2014/05/08/Personas-data-science-k-means/>)

Author: Map

Published: 2014-05-08T20:55:56Z

Content type: opinion

Language: en

Sources: [Craig Kerstiens](<https://devfeed.tech/sources/craig-kerstiens.md>)

Topics: [Data Science](<https://devfeed.tech/topics/data-science.md>), [math](<https://devfeed.tech/topics/math.md>), [Statistics](<https://devfeed.tech/topics/statistics.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [data](<https://devfeed.tech/topics/data.md>), [Travis CI](<https://devfeed.tech/topics/travis-ci.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [math](<https://devfeed.tech/tags/math.md>), [statistics](<https://devfeed.tech/tags/statistics.md>), [technology](<https://devfeed.tech/tags/technology.md>), [travis-ci](<https://devfeed.tech/tags/travis-ci.md>)

## AI overview

The article discusses personas, data science, and k-means, arguing that personas can be understood as groupings of people or other entities with likely outcomes based on inputs. It defines data science as applying math or statistics and algorithms to learn actionable information about a business. The supplied text ends as the discussion of k-means begins.

## Source excerpt

If one of the industry lingo terms in title didn't make your skin crawl a little then I need to try harder. At the same time you've probably heard someone use one of them in a non-trolling way in the last month. All three of these can often actually mean the same or similar things, it's just people approach them differently from their world perspective. Personas don't have to be marketing only speak, and data science doesn't have to be only for stats people. My goal here is to simply set a context for the rest of the meat which talks about how you can simply look at your data and let it surface things you may not have known. Personas I most commonly hear this term from "business people". In fact not too long ago I recall interacting with someone that wanted to define personas for a company. They wanted to give them names, Joe and Mary. Joe is a father of 2, he works between 8 and 5, because he has to pick kids up from school, he's always worked at fortune 100 companies. Mary is single, she's a small business owner, she likes using tools instead of building things herself. If you think this is overly exaggerated on what you might expect that's fair. Lets take a company I'm fond of Travis CI, if someone were to do this for them it might look like: Enterprise QA developer Startup full stack engineer Open source contributor While this is all fine and good, a name and what they do doesn't help in the substantial way I'd like. Sure use personas if it helps you think about who you're building the product for, but don't expect customers to say yes I fit into only this bucket by trying to create classifications like this. Let's rephrase this to be super simple, groupings of people, no groupings of something that have a likely outcome based on some various inputs. Perhaps a better term for it is archtype Data science The application of math or statistics to learn something about your business. It doesn't have to be big data, or NoSQL, simply the application of an algorithm to