Row 99792

Row ID: 99792 | Dataset Entry | Axioma AXP Content Repository

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Bit late but I come from a scientific background (biotech), and I think this has really helped me think about problems in a different way to other people on the teams ive been on. The more i progressed, the less I used the normal libraries that youd typically use as a data scientist and went more into putting things into production (SWE). at the end of the day, data science is an iterative process, just like being at the lab. Try a model based on some intuition, if it works, keep it and move on. I suppose anyone can do this, and it doesnt require understanding how to multiply two matrices. I have since forgotten all the math involved because Im not in research. When I was in biology, I worked on bacteria. I didnt need to know every bacteria on earth to do research, and i certainly knew nothing about the cellular processes of the bacteria I was working with. If the bacteria turns green -- good, no color -- change approach. Likewise, I view data science in the same way. As you progress you get a certain intuition for when to use boosting or how many layers you need to make something happen.

FieldValue
text Bit late but I come from a scientific background (biotech), and I think this has really helped me think about problems in a different way to other people on the teams ive been on. The more i progressed, the less I used the normal libraries that youd typically use as a data scientist and went more into putting things into production (SWE). at the end of the day, data science is an iterative process, just like being at the lab. Try a model based on some intuition, if it works, keep it and move on.…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-25
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Raw Record

{
  "text": "Bit late but I come from a scientific background (biotech), and I think this has really helped me think about problems in a different way to other people on the teams ive been on. The more i progressed, the less I used the normal libraries that youd typically use as a data scientist and went more into putting things into production (SWE). at the end of the day, data science is an iterative process, just like being at the lab. Try a model based on some intuition, if it works, keep it and move on. I suppose anyone can do this, and it doesnt require understanding how to multiply two matrices. I have since forgotten all the math involved because Im not in research. When I was in biology, I worked on bacteria. I didnt need to know every bacteria on earth to do research, and i certainly knew nothing about the cellular processes of the bacteria I was working with. If the bacteria turns green -- good, no color -- change approach. Likewise, I view data science in the same way. As you progress you get a certain intuition for when to use boosting or how many layers you need to make something happen.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-25",
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}
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