Row 6171

Row ID: 6171 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 6171 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I'm have technical interviews with a fintech company, and they (HR) have specifically told me that the interview will be on Problem Solving, SQL, and Python.

The position is for a Data Scientist, 2+ YOE.

I'm prepping by brushing up all my SQL, running through Ace the Data Science Interview for ML theory (and conceptual questions), and largely ignoring pure statistics/probabilities for now.

In a way, I'm thankful that it's not Leetcode because I suck ass at DS&A, but also I don't really know what to expect?

For the Python piece, I was thinking going over training models with sklearn (full pipeline, train-test-split, normalizatoin, scaling etc.), building some models from scratch (zzzz, linear regression, logistic regression), building some algorithms from scratch (cosine distance, bag of words, count vectorizer), pandas dataframe manipulation, numpy linear algebra.

Just wondering are there any ideas for what else I could expect? Is this list a good idea to prep?

Not sure if "it WONT be Leetcode" means, it will be DS&A just not problems from Leetcode, or it means nothing like DS&A at all.

HR interviewer said verbatim: "if you know how to dev, you will get it" which was new.

Thanks!

EDIT: title should say \*Problem Solving\* lol

FieldValue
text I'm have technical interviews with a fintech company, and they (HR) have specifically told me that the interview will be on Problem Solving, SQL, and Python. The position is for a Data Scientist, 2+ YOE. I'm prepping by brushing up all my SQL, running through Ace the Data Science Interview for ML theory (and conceptual questions), and largely ignoring pure statistics/probabilities for now. In a way, I'm thankful that it's not Leetcode because I suck ass at DS&A, but also I don't really know w…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-07
username_encoded Z0FBQUFBQm5LakwyNUJqUlBLay1LZ1RmMEFPLTB6T1l0cE16VWFGRWxaR2kzM3RsQjhrRC1PcVZRSC11NzhuU3NaV1dROUI2Sy1NejMzLURmWEFpdk9wcGdEb2pMc0tMbHc9PQ==
url_encoded Z0FBQUFBQm5Lak9HY2t6VUxuVFBwRmpUR1IzeXZOOUR6UHpoV21KLVVpaUtxY1JUQlFmR19RWHNjbjlzVlc3NWlUOElrZERHOVlhTU4wcHNoQ29YNnFXbzQ2OEZPVGNoaXd1aDVkZVI0RVp3LVMtal9KRF81X3dKSElOVklTM1hlRS1fSXYyOEhyY1pyWnBJWHpCMjAyLWhVRjJ6Tm5qMjVsX04yc3drUEpJSER0aTlkMzRMUWs0Q2N5TC1NQ1NZX0JXZTJwelRVc1drWWRITk9yS3d2VFBMWXVZNlVCbjNyUT09

Raw Record

{
  "text": "I'm have technical interviews with a fintech company, and they (HR) have specifically told me that the interview will be on Problem Solving, SQL, and Python.\n\nThe position is for a Data Scientist, 2+ YOE.\n\nI'm prepping by brushing up all my SQL, running through Ace the Data Science Interview for ML theory (and conceptual questions), and largely ignoring pure statistics/probabilities for now.\n\nIn a way, I'm thankful that it's not Leetcode because I suck ass at DS&A, but also I don't really know what to expect?\n\nFor the Python piece, I was thinking going over training models with sklearn (full pipeline, train-test-split, normalizatoin, scaling etc.), building some models from scratch (zzzz, linear regression, logistic regression), building some algorithms from scratch (cosine distance, bag of words, count vectorizer), pandas dataframe manipulation, numpy linear algebra.\n\nJust wondering are there any ideas for what else I could expect? Is this list a good idea to prep?\n\nNot sure if \"it WONT be Leetcode\" means, it will be DS&A just not problems from Leetcode, or it means nothing like DS&A at all.\n\nHR interviewer said verbatim: \"if you know how to dev, you will get it\" which was new.\n\nThanks!\n\nEDIT: title should say \\*Problem Solving\\* lol",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-07",
  "username_encoded": "Z0FBQUFBQm5LakwyNUJqUlBLay1LZ1RmMEFPLTB6T1l0cE16VWFGRWxaR2kzM3RsQjhrRC1PcVZRSC11NzhuU3NaV1dROUI2Sy1NejMzLURmWEFpdk9wcGdEb2pMc0tMbHc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9HY2t6VUxuVFBwRmpUR1IzeXZOOUR6UHpoV21KLVVpaUtxY1JUQlFmR19RWHNjbjlzVlc3NWlUOElrZERHOVlhTU4wcHNoQ29YNnFXbzQ2OEZPVGNoaXd1aDVkZVI0RVp3LVMtal9KRF81X3dKSElOVklTM1hlRS1fSXYyOEhyY1pyWnBJWHpCMjAyLWhVRjJ6Tm5qMjVsX04yc3drUEpJSER0aTlkMzRMUWs0Q2N5TC1NQ1NZX0JXZTJwelRVc1drWWRITk9yS3d2VFBMWXVZNlVCbjNyUT09"
}
Explore Dataset Explore Row

Entry Information