-: I would suggest people take a class.
One of the great things about college,
one of the things I really loved about it
was the opportunity to take little courses here and there
way outside of any discipline, like with zero connection
to what I wanted to do.
Take your computer science course, right?
Go take an energy systems course,
try and understand something way outside your comfort zone.
Like there's a lot of opportunities here to do that.
There are great teachers who can take
someone who's not an expert in this stuff
and really, in a semester, get them
a solid foundation there.
And then you can use those skills of learning
your technology domains to teach yourself this stuff
as it comes along if it was pertinent to whatever
your interests are.
So data science in general is a naysaid field
it is a multi-disciplinary field
it combines computer science, mathematics and statistics,
you know good kind of business sense, analytics,
instinct to understand the decisions that you're
trying to make, good communication skills,
so and just a real grounding in big real world data sets.
And there's lots of ways to pick that up, you know.
I have a diverse set of people that I work with
on a regular basis, getting a broad spectrum
of academic backgrounds.
I think the key piece there is they usually have some form
of empirically driven, statistically rigorous,
academic background.
Usually an advanced degree, right?
So my advice to them would be to take courses
that expose you to a wide variety of domains.
That have grounding in math, statistics,
spend some time doing computer programming
and getting familiar with how to leverage computers
to solve your problems at a big scale.
And then take courses that force you to communicate
complex ideas, because that's another key piece to this.
You can't do anything with a great model
if you can't communicate people, and help them
understand how to use it.
Do that, look into graduate programs
that will foster that type of empirical computer driven
exploration work, I think most of the people who do
social sciences, behavioral science, physical science,
come out with skills that are very transferable
into the data science realm
after they do some graduate work.