Many data science tutorials give very clear outcomes on very clean data.
Good for learning. Not at all common in business.
Data is messy. Outcomes may be ambiguous, but even crystal clear ones don't always get supported.
DS reality != DS tutorials
(still love it though)
@tcarpenter216 @adamjnafa The original hard drives, you used to be able to add more platters to expand storage. Thus bad boy probably weighed 200lbs and stored 10MB
@HamelHusain And most hyping AI software/solutions have a vested financial interest in doing so. Reminds me of the crypto craze.
I keep waiting for some AI solutions to bubble up as leading contenders for value creation in the space, but instead new thing after new thing gets released.
@matsonj I was ready to nod along knowingly...but SQL and data cleaning?
Gotta push back against the idea that it's common to have pristine datasets delivered with no assembly required in most (any?) data science jobs.
Ok, so LLMs are a Thing.
How do they work? Embeddings.
WTF are embeddings?
I spent a year doing a deep dive. But when I was researching, I couldn't find anything that explained them in business, engineering, AND math contexts. So I wrote a thing.๐
https://t.co/iykVXIuzty
Time for my 6th Annual Holiday Toy Drive! If you're unemployed/underemployed & need help for your kids this holiday season, I have an awesome community that can help with toys, clothing, school supplies, etc. If you need help OR would like to help, check out the link in my bio!
@ArynnPost I've wondered about the move to preferring data science degrees for data roles and how that will affect an industry that originally brought in people from a variety of backgrounds.
@analyticshero@joshuastarmer@BecomingDataSci Thanks for writing it! I've referenced your book a couple of times when talking to my team already - both for how much better people recall a story vs a statistic, as well as why a data visualization is more persuasive than a data table. So many useful concepts!