Row 6714

Row ID: 6714 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 6714 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I enjoy auditing university courses on data science topics. At least in my experience, the stats courses tend to explain-- or even prove-- theoretical properties of different methods (e.g., "This estimator is consistent and asymptotically normal because ...").

On the other hand, the machine learning courses I see tend to focus on intuitions and implementation mechanics. And they get a bit hand-wavy when it comes to justifying an approach (e.g., "The models in the ensemble balance each other out, leading to better predictive performance").

Have you observed this difference? Any thoughts why it occurs?

FieldValue
text I enjoy auditing university courses on data science topics. At least in my experience, the stats courses tend to explain-- or even prove-- theoretical properties of different methods (e.g., "This estimator is consistent and asymptotically normal because ..."). On the other hand, the machine learning courses I see tend to focus on intuitions and implementation mechanics. And they get a bit hand-wavy when it comes to justifying an approach (e.g., "The models in the ensemble balance each other out…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-13
username_encoded Z0FBQUFBQm5LakwzZUF6RDdwX0FCWVM3T044U3E2cEo5dDlHUEgzVHNDV1kxN2pMbFBESjQzdUJJVkZTUkNseEJzckNxanFheXpmR2ZwdldXTG81VEpEdnJCZC1ER1JGZ0E9PQ==
url_encoded Z0FBQUFBQm5Lak9HVWlacTZXWW14cDRqRUpoZmJZMmJiWkNuRVZaRmFFZ2lBUXFSMlFzZk1KQTBzeHpzWGJwYm5JM2RNRWRfZzJWOUQycTFaVURHWG1WMG1mOUVSbHJiaDNHSDg5UjZRNmlqOGxxVHlhYW0wMjNVX0l6cF9hTzRWZERKUm1ueFplbmF6cWptVE9CZUhDZmY2Y0l6NnFfeXdIdzdLZDBOeEhGMDFvU1lONFc5TXc4PQ==

Raw Record

{
  "text": "I enjoy auditing university courses on data science topics. At least in my experience, the stats courses tend to explain-- or even prove-- theoretical properties of different methods (e.g., \"This estimator is consistent and asymptotically normal because ...\").\n\nOn the other hand, the machine learning courses I see tend to focus on intuitions and implementation mechanics. And they get a bit hand-wavy when it comes to justifying an approach (e.g., \"The models in the ensemble balance each other out, leading to better predictive performance\").\n\nHave you observed this difference? Any thoughts why it occurs?",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-13",
  "username_encoded": "Z0FBQUFBQm5LakwzZUF6RDdwX0FCWVM3T044U3E2cEo5dDlHUEgzVHNDV1kxN2pMbFBESjQzdUJJVkZTUkNseEJzckNxanFheXpmR2ZwdldXTG81VEpEdnJCZC1ER1JGZ0E9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9HVWlacTZXWW14cDRqRUpoZmJZMmJiWkNuRVZaRmFFZ2lBUXFSMlFzZk1KQTBzeHpzWGJwYm5JM2RNRWRfZzJWOUQycTFaVURHWG1WMG1mOUVSbHJiaDNHSDg5UjZRNmlqOGxxVHlhYW0wMjNVX0l6cF9hTzRWZERKUm1ueFplbmF6cWptVE9CZUhDZmY2Y0l6NnFfeXdIdzdLZDBOeEhGMDFvU1lONFc5TXc4PQ=="
}
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