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Data Science beasts (failures) and where to find them
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Marks
Author(s):
Grishma Jena
Location
Bungalo East
Date
oct Sun 08
Days Raw Files
Start
13:15
First Raw Start
13:03
Duration
00:15:00
Offset
0:11:50
End
13:30
Last Raw End
14:03
Chapters
00:00
Total cuts_time
10 min.
https://pybay.com/speakers/#sz-speaker-56e08f39-fbea-4cf8-9af0-aba66372abc6
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The nature of the field of Data Science encourages trial and error, but we can do a better job of destigmatizing failure and learn from our collective experiences. Join me as I take us on an adventure to find the beasts i.e. the different ways Data Science projects can fail. I will be talking about 4 major reasons for failure (data, infrastructure, implementation, and culture), their different aspects, and supplementing it with my experiences and case studies. I will also share how to control these beasts and recommend actions to be taken to ensure a successful end-to-end Data Science project.
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production notes
2023-10-08/13_03_10.ts
Apply:
13:03:10 - 13:16:13 ( 00:13:03 )
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13:33:09
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13:03:10
seconds: 0.0
Wall: 13:03:10
Duration
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13:33:09
seconds: 783.0
Wall: 13:16:13
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13:16:13 - 13:27:03 ( 00:10:50 )
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13:03:10
seconds: 783.0
Wall: 13:16:13
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00:29:59
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seconds: 1712.0
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seconds: 1183.0
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13:33:10
seconds: 1688.0
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