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# 데이터 과학의 생애주기 소개
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|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)|
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|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/14-DataScience-Lifecycle.png)|
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|:---:|
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| 데이터 과학의 생애주기 소개 - [@nitya](https://twitter.com/nitya)의 이미지 |
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이번 강의에서는 생애 주기의 세 부분 : 데이터 포획, 데이터 처리 그리고 유지에 집중합니다.
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![Diagram of the data science lifecycle](./images/data-science-lifecycle.jpg)
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![Diagram of the data science lifecycle](.././images/data-science-lifecycle.jpg)
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> [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) 의 이미지
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## 데이터 포획
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|Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)|
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|--|--|
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|![Team Data Science Lifecycle](./images/tdsp-lifecycle2.png) | ![Data Science Process Alliance Image](./images/CRISP-DM.png) |
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|![Team Data Science Lifecycle](.././images/tdsp-lifecycle2.png) | ![Data Science Process Alliance Image](.././images/CRISP-DM.png) |
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| [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle)의 이미지 | [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/)의 이미지 |
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## [이전 강의 퀴즈](https://red-water-0103e7a0f.azurestaticapps.net/quiz/27)
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* [데이터 과학 작업 실행: 탐색, 모델링 및 배치](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/execute-data-science-tasks)
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## 과제
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[데이터 세트 ](assignment.md)
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[데이터 세트](assignment.md)

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