Row 8746

Row ID: 8746 | Dataset Entry | Axioma AXP Content Repository

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I think it depends on what level you are. I think there are much more valuable skills than learning how to implement everything from scratch. Also you are not expected to build extremely advanced solutions until later in your career. You are required to have a solid understanding of the basics as a junior.

Things that I consider more valuable are knowing how and when to use each algorithm, knowing plenty techniques for feature engineering, always seeking business knowledge, knowing how implement and deploy models to production, like cloud/databricks and all that, and being a person that is always keeping up with new advancements. For me these are much more valuable and will take you further and faster to a higher position.

After having more knowledge and experience, knowing the ins and outs of algos becomes more valuable as well, and you'll have the maturity and skills to decide when to go for something more sophisticated or customized or not.

There was a similar discussion to this a while back, between A) spending years studying then seeking professional experience or B) going to the job market asap and learning while doing. A very famous data scientist that I don't recall the name was saying that option B was much better for a data scientist's career, as experience is equally or even more important than academic/in depth knowledge.

FieldValue
text I think it depends on what level you are. I think there are much more valuable skills than learning how to implement everything from scratch. Also you are not expected to build extremely advanced solutions until later in your career. You are required to have a solid understanding of the basics as a junior. Things that I consider more valuable are knowing how and when to use each algorithm, knowing plenty techniques for feature engineering, always seeking business knowledge, knowing how implemen…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-19
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Raw Record

{
  "text": "I think it depends on what level you are. I think there are much more valuable skills than learning how to implement everything from scratch. Also you are not expected to build extremely advanced solutions until later in your career. You are required to have a solid understanding of the basics as a junior.\n\nThings that I consider more valuable are knowing how and when to use each algorithm, knowing plenty techniques for feature engineering, always seeking business knowledge, knowing how implement and deploy models to production, like cloud/databricks and all that, and being a person that is always keeping up with new advancements. For me these are much more valuable and will take you further and faster to a higher position. \n\nAfter having more knowledge and experience, knowing the ins and outs of algos becomes more valuable as well, and you'll have the maturity and skills to decide when to go for something more sophisticated or customized or not.\n\nThere was a similar discussion to this a while back, between A) spending years studying then seeking professional experience or B) going to the job market asap and learning while doing. A very famous data scientist that I don't recall the name was saying that option B was much better for a data scientist's career, as experience is equally or even more important than academic/in depth knowledge.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-19",
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}
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