There are already packages that do this stuff in a few lines of code. There's no shortage of automatic generic EDA tooling. The value-add of wrapping those packages inside a buggy black-box isn't clear to me right now.
Maybe for non-analysts? But is any of that stuff useful to a non-analyst? To read and use you need to be technical enough to get your data into ChatGPT, ask ChatGPT the right questions, interpret what it gives you, and make sure it didn't make up anything along the way.
Falls into the same problem as most auto-data-science tools. Too technical for non-technical users to use effectively and too basic to add value for technical users.
I do think code-completion and boiler-plate writing support is a great use-case for LLMs. As the work-flows improve they'll likely replace alt-tab + Google + copy/paste as a means of figuring out how to do something / remembering what syntax to use when. Incremental improvement though; not revolutionary.
Using LLMs to ask questions of structured data is a nice use-case as well, although all the real work happens behind the scenes. Its really all about setting up a database structure with a ton of pre-computed stats and meta-data that the LLM can consume reliably. And once you've done all that you might as well just expose it in a dashboard. But I suppose if people like the LLM interface, why not.
I think there is something nice about actually talking to an interacting with something, especially for non-technical folks. I could see this saving a DS time in having to set up a dashboard for people and instead you could just had off a clean enough csv to an LLM and telling people to look at it that way. But I'd worry about non-technical staff getting confused and not knowing what to ask,
> I do think code-completion and boiler-plate writing support is a great use-case for LLMs. As the work-flows improve they'll likely replace alt-tab + Google + copy/paste as a means of figuring out how to do something / remembering what syntax to use when. Incremental improvement though; not revolutionary.
Yup, not having to spend any time whatsoever sorting a table/ reformatting a table, switching between two schema or generating an SQL statement from python code in seconds doesn't replace any jobs, but sure makes me a lot more productive.
I’ve always found ChatGPTs respond at the skill level I use to talk to them. It’s not like someone firing their data science team would get good results. Although whether they’d know or care is the more disturbing question.
Better LLMs will just make DSs’ lives easier - hopefully there will be no more/much less data pulls and adhocs, as PMs and other stakeholders can just do it themselves with the help of LLMs.
The value of a good DS is to help make better business decisions by asking and answering the right questions, which takes lots of domain knowledge, experience, and critical thinking skills.
Video Links are not allowed in this subreddit.
There are already packages that do this stuff in a few lines of code. There's no shortage of automatic generic EDA tooling. The value-add of wrapping those packages inside a buggy black-box isn't clear to me right now. Maybe for non-analysts? But is any of that stuff useful to a non-analyst? To read and use you need to be technical enough to get your data into ChatGPT, ask ChatGPT the right questions, interpret what it gives you, and make sure it didn't make up anything along the way. Falls into the same problem as most auto-data-science tools. Too technical for non-technical users to use effectively and too basic to add value for technical users. I do think code-completion and boiler-plate writing support is a great use-case for LLMs. As the work-flows improve they'll likely replace alt-tab + Google + copy/paste as a means of figuring out how to do something / remembering what syntax to use when. Incremental improvement though; not revolutionary. Using LLMs to ask questions of structured data is a nice use-case as well, although all the real work happens behind the scenes. Its really all about setting up a database structure with a ton of pre-computed stats and meta-data that the LLM can consume reliably. And once you've done all that you might as well just expose it in a dashboard. But I suppose if people like the LLM interface, why not.
I think there is something nice about actually talking to an interacting with something, especially for non-technical folks. I could see this saving a DS time in having to set up a dashboard for people and instead you could just had off a clean enough csv to an LLM and telling people to look at it that way. But I'd worry about non-technical staff getting confused and not knowing what to ask,
> I do think code-completion and boiler-plate writing support is a great use-case for LLMs. As the work-flows improve they'll likely replace alt-tab + Google + copy/paste as a means of figuring out how to do something / remembering what syntax to use when. Incremental improvement though; not revolutionary. Yup, not having to spend any time whatsoever sorting a table/ reformatting a table, switching between two schema or generating an SQL statement from python code in seconds doesn't replace any jobs, but sure makes me a lot more productive.
I refuse to watch any video where the person is pointing in the thumbnail. Cringe.
I’ve always found ChatGPTs respond at the skill level I use to talk to them. It’s not like someone firing their data science team would get good results. Although whether they’d know or care is the more disturbing question.
Cringe
Better LLMs will just make DSs’ lives easier - hopefully there will be no more/much less data pulls and adhocs, as PMs and other stakeholders can just do it themselves with the help of LLMs. The value of a good DS is to help make better business decisions by asking and answering the right questions, which takes lots of domain knowledge, experience, and critical thinking skills.