Row 86490

Row ID: 86490 | Dataset Entry | Axioma AXP Content Repository

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Fraud Analysis Courses and other tips

Hello y'all!

I'm currently working as a data analyst for an auditing company that is starting to be proactive in their audits. The plan is to start using ML or any automated process to detect frauds or unusual transactions within the clients financial reports.

Researching I found different ways to deal with data to cluster them. KMeans, KMode, KPrototype. My data is all over the place and mostly mixed data, that's why k-prototypes might be a thing. Additionally, my data has millions of rows, but not necessarily highly dimensional, less than 50 columns/features.

I'm also aware of PyOC, which uses different methods to find outliers, mostly using only numerical fields. Whomp whomp.

I guess what I'm looking for are tips on courses or resources that I can consume to better prepare when the requests start coming. Any tips are appreciated!

Thanks!

FieldValue
text Fraud Analysis Courses and other tips Hello y'all! I'm currently working as a data analyst for an auditing company that is starting to be proactive in their audits. The plan is to start using ML or any automated process to detect frauds or unusual transactions within the clients financial reports. Researching I found different ways to deal with data to cluster them. KMeans, KMode, KPrototype. My data is all over the place and mostly mixed data, that's why k-prototypes might be a thing. Addit…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-24
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{
  "text": "Fraud Analysis Courses and other tips\n\nHello y'all!\n\nI'm currently working as a data analyst for an auditing company that is starting to be proactive in their audits. The plan is to start using ML or any automated process to detect frauds or unusual transactions within the clients financial reports. \n\nResearching I found different ways to deal with data to cluster them. KMeans, KMode, KPrototype. My data is all over the place and mostly mixed data, that's why k-prototypes might be a thing. Additionally, my data has millions of rows, but not necessarily highly dimensional, less than 50 columns/features.\n\nI'm also aware of PyOC, which uses different methods to find outliers, mostly using only numerical fields. Whomp whomp. \n\nI guess what I'm looking for are tips on courses or resources that I can consume to better prepare when the requests start coming. Any tips are appreciated!\n\nThanks!",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-24",
  "username_encoded": "Z0FBQUFBQm5Lak1wTnkxTmZjQml5S0ZYUVdHRmh0aUhaVDdreGVUWlJQNnBmZnhxaTdyTVlhUHRPN21LTzc4NExMVW5ubFpoWUh1aWUxa1B3dVEzek1BVy05YXlKM0MzT1E9PQ==",
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}
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