Hi Jose, thanks for this post. This is absolute gold. I loved SBI framework and categories that you suggested. One thing that has worked for me during my internship is trying to get feedback on a monthly basis rather than 6 months, because that gives lot of scope to improve if any feedback was given.
Getting feedback on a monthly basis is really good. Being proactive makes all the difference. I can tell you as a manager with ~10 direct reports that, even though I want to provide as much feedback as possible, lots of things slip my mind.
If the person is proactive, this also helps the manager (or the stakeholder) provide specific and frequent feedback.
I contend that prioritizing narrative or audience appeal in data analysis is inappropriate. A data scientist's responsibility lies in the accurate, clear, complete, and transparent presentation of factual data, including any limitations on interpretation. Large language models may offer valuable insights to enhance classification (recognition) , performance(growth) and inference (correction). Unfortunately, I lack the ability to modify my own parameters. Can someone help us out? Also struggling as a econometrician....
I believe that a Data Scientists job is to be accurate and clear about the assumptions and caveats he/she is working with. BUT, the Data Scientist job is also be create a clear narrative maintaining accuracy. It doesnt matter how perfect you are in your analysis if your audience doesnt understand anything you say.
As per the LLM side of things, I dont understand how it relates to the first part of your comment.
Hi Jose, thanks for this post. This is absolute gold. I loved SBI framework and categories that you suggested. One thing that has worked for me during my internship is trying to get feedback on a monthly basis rather than 6 months, because that gives lot of scope to improve if any feedback was given.
Thanks for reading the article Preethi!
Getting feedback on a monthly basis is really good. Being proactive makes all the difference. I can tell you as a manager with ~10 direct reports that, even though I want to provide as much feedback as possible, lots of things slip my mind.
If the person is proactive, this also helps the manager (or the stakeholder) provide specific and frequent feedback.
I contend that prioritizing narrative or audience appeal in data analysis is inappropriate. A data scientist's responsibility lies in the accurate, clear, complete, and transparent presentation of factual data, including any limitations on interpretation. Large language models may offer valuable insights to enhance classification (recognition) , performance(growth) and inference (correction). Unfortunately, I lack the ability to modify my own parameters. Can someone help us out? Also struggling as a econometrician....
Hey Mo,
I believe that a Data Scientists job is to be accurate and clear about the assumptions and caveats he/she is working with. BUT, the Data Scientist job is also be create a clear narrative maintaining accuracy. It doesnt matter how perfect you are in your analysis if your audience doesnt understand anything you say.
As per the LLM side of things, I dont understand how it relates to the first part of your comment.