Row 99872

Row ID: 99872 | Dataset Entry | Axioma AXP Content Repository

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This is what copilot had to say about science vs. engineering. It summed it up pretty well, IMO. Scientific method: state question, do background investigation, formulate hypothesis, design and execute experiments, analyze results and draw conclusions, publicize results. Engineering method: define problem, constrain/scope problem, propose possible solutions, build a prototype, test/refine, productionize. Based on that definition, most people outside of academia and FAANG are doing engineering. Copilot’s final thoughts: 1) scientists focus on understanding nature; engineers focus on practical solutions 2) many projects fall in a gray area between science and engineering. That last certainly seems to apply to data science, hence the endless debate over terminology.

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text This is what copilot had to say about science vs. engineering. It summed it up pretty well, IMO. Scientific method: state question, do background investigation, formulate hypothesis, design and execute experiments, analyze results and draw conclusions, publicize results. Engineering method: define problem, constrain/scope problem, propose possible solutions, build a prototype, test/refine, productionize. Based on that definition, most people outside of academia and FAANG are doing engineering. C…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-25
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{
  "text": "This is what copilot had to say about science vs. engineering. It summed it up pretty well, IMO. Scientific method: state question, do background investigation, formulate hypothesis, design and execute experiments, analyze results and draw conclusions, publicize results. Engineering method: define problem, constrain/scope problem, propose possible solutions, build a prototype, test/refine, productionize. Based on that definition, most people outside of academia and FAANG are doing engineering. Copilot’s final thoughts: 1) scientists focus on understanding nature; engineers focus on practical solutions 2) many projects fall in a gray area between science and engineering. That last certainly seems to apply to data science, hence the endless debate over terminology.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-25",
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