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{
"copyright_text": null,
"description": "*How do you know if your data science results are correct?* Robust\nsoftware usually has tests asserting that certain conditions hold, but\nas a data scientist it\u2019s often not straightforward or obvious how to\nintegrate these best practices. Our workflow includes exploration,\nstatistical models, and one-off analysis. This talk will give concrete\nexamples of when and how testing should play a role, and provide you\nwith enough introduction to get started writing your first data science\ntests using ``pytest`` & ``hypothesis``.\n",
"duration": 1860,
"language": "eng",
"recorded": "2019-05-05T13:50:00",
"related_urls": [
{
"label": "Conference schedule",
"url": "https://us.pycon.org/2019/schedule/talks/"
},
{
"label": "Conference slides (github)",
"url": "https://github.com/PyCon/2019-slides"
},
{
"label": "Conference slides (speakerdeck)",
"url": "https://speakerdeck.com/pycon2019"
},
{
"label": "Talk schedule",
"url": "https://us.pycon.org/2019/schedule/presentation/228/"
}
],
"speakers": [
"Jes Ford"
],
"tags": [
"talk"
],
"thumbnail_url": "https://i.ytimg.com/vi/0ysyWk-ox-8/maxresdefault.jpg",
"title": "Getting Started Testing in Data Science",
"videos": [
{
"type": "youtube",
"url": "https://www.youtube.com/watch?v=0ysyWk-ox-8"
}
]
}
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