Who can become an AI or ML engineer?
Well basically if you have some background
in software engineering like writing code,
be it in Java, Python or any programming language,
you can typically learn these skills because
what you need to be upskilled at is different
paradigm of how to apply algorithms to data
and make them learn from the data so that
it can improve over time and solve real-world
problems, be it trying to predict the stock
market tomorrow, trying to predict device
failures, anomaly detection, maybe even predicting
cancer, so scope is immense.
And let’ say even you don’t have a software
engineering background if you can upskill
yourself into writing some kind of programming
code with regard to python, java whatever
be it then you can pretty much simultaneously
dive into machine learning and maybe even
deep learning.
Who should learn data science?
It’s really somebody who is curious, has
a little bit of quantitative aptitude and
is willing to learn new things.
So if you enjoy learning, enjoy finding facts,
if you enjoy being a pseudo detective trying
to find answers using data, you should be
a data scientist.
So my own journey into data science has been
interesting and I found my way through all
kinds of different industries, exposures,
background, and roles that I took.
So it’s not true that you need to be a computer
scientist or a PhD in maths to really be a
part of data science conversation.
The fundamental need for you is really to
be curious.
I am an electrical engineer by education and
I did an MBA in marketing and strategy, does
that make me a computational scientist?
Not really!
As I keep saying that the core driving fact
is really the fact that you need to be a logical
thinker, you should be able to approach a
problem from first principles and really structure
that in a meaningful way.
Beyond that languages like Python making it
easier and easier for people with very little
background in programming to really go out
there and experience and try this.
Frankly speaking, there are other platforms
like RapidMiner that are kind of fought relatively
simpler pieces of analysis, even make the
language redundant.
So what really matters is how curious are
you and how interested are you in learning
data or the fact of the behavior you are addressed
with.
