Hi ,
Seth Godin recently wrote
that:
"To win a Nobel prize a hundred years ago, you might only need a legal pad and a few pencils.
Today, it takes millions of dollars, scores of people and many years of
effort."
Similar things can also be said about data science.
The linear regression
algorithm was first published in the early 1800's, almost two centuries before computers became commonplace.
Yet, few organisations would possess the resources to develop and train a model like GPT-4, from scratch, today.
Here's the thing...
There is a place in the world for data scientists capable of building "the next ChatGPT".
But not every data scientist needs to be a cutting-edge researcher to be in high demand. You just need to create business value.
And with the majority of organisations still new to the world of data, creating a path from nothing to something is sometimes all it takes.
Unlike the physical sciences, where "big science" is now the norm, in data science it's still possible to make a difference with techniques that are over
200 years old.
Talk again soon,
Dr Genevieve Hayes.
