Editing 2341: Scientist Tech Help

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In the first panel Randall, presents an idealized view of the tasks of tech people. A group of scientists have run their experiments and compiled their data, but find that the data is simply too complicated for humans, even advanced scientists such as themselves; the tech people resolve in heroic statements to decipher the data with their most advanced algorithms. Large portions of {{w|machine learning}} and {{w|data science}} hinge around finding a pattern (either regression or classification) in a given data set, but the more common, real-world problem is in {{w|data cleaning}} and preparation. For the most part, the rest can be done with preexisting implementations. These types of tasks are those that tech people both expect to perform, and hope to expand upon.
 
In the first panel Randall, presents an idealized view of the tasks of tech people. A group of scientists have run their experiments and compiled their data, but find that the data is simply too complicated for humans, even advanced scientists such as themselves; the tech people resolve in heroic statements to decipher the data with their most advanced algorithms. Large portions of {{w|machine learning}} and {{w|data science}} hinge around finding a pattern (either regression or classification) in a given data set, but the more common, real-world problem is in {{w|data cleaning}} and preparation. For the most part, the rest can be done with preexisting implementations. These types of tasks are those that tech people both expect to perform, and hope to expand upon.
  
βˆ’
The second panel presents a different reality. The scientists are fully confident they can interpret the data on their own, provided they can access it, because the methods of ''recording'' their data are incredibly sub-par. Apparently wasps had infested the lab, and the scientists had to take photos of their equipment through the window. This created a much more fundamental problem of data format than normal (image vs spreadsheet, as opposed to something more normal like pixel-wise vs vertex-based segmentation).  The joke is that the scientists' questions for their tech specialists are very mundane in nature; it presents not a chance to test and prove their machine learning systems, but a simple and tedious process of untangling digital paperwork. [https://old.reddit.com/r/talesfromtechsupport/comments/hxt0fw/my_whole_computer_went_black_what_did_you_do/ This is true in real life] — experts' expertise is usually deep, but not broad, and helping them with issues outside their comfort zone is rarely glamorous.
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The second panel, presents a different reality. The scientists are fully confident they can interpret the data on their own, provided they can access it, because the methods of ''recording'' their data are incredibly sub-par. Apparently wasps had infested the lab, and the scientists had to take photos of their equipment through the window. This created a much more fundamental problem of data format than normal (image vs spreadsheet, as opposed to something more normal like pixel-wise vs vertex-based segmentation).  The joke is that the scientists' questions for their tech specialists are very mundane in nature; it presents not a chance to test and prove their machine learning systems, but a simple and tedious process of untangling digital paperwork. [https://old.reddit.com/r/talesfromtechsupport/comments/hxt0fw/my_whole_computer_went_black_what_did_you_do/ This is true in real life] — experts' expertise is usually deep, but not broad, and helping them with issues outside their comfort zone is rarely glamorous.
  
 
{{w|Polaroid Corporation|Polaroid}} is a brand of {{w|instant camera}}, though "Polaroid" is often used to refer to instant cameras in general. Excel is referring to {{w|Microsoft Excel}}, a spreadsheet management program.
 
{{w|Polaroid Corporation|Polaroid}} is a brand of {{w|instant camera}}, though "Polaroid" is often used to refer to instant cameras in general. Excel is referring to {{w|Microsoft Excel}}, a spreadsheet management program.

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