
What is going to be the future of Computer Science? - askari01
I see tons of people shifting towards AI, Machine Learning (ML), Big Data &amp; Mixed Reality(MR). But what I am failing to understand is decide which one is that I should choose, like there are plenty of options I like MR, ML &amp; Big Data. But I can&#x27;t do all. I want to evaluate them before going down a path. 
Comments, suggestions, opinions, criticism &amp; advice are all welcomed.
feel free to comment.<p>thanks
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mbrock
I don't think you'll find a single branch that's "the" future. Science is
always branching and there's always many things going on.

And what if you find the biggest and most important branch, and go all in, and
then it turns out that everyone else also had the same idea? You'll probably
still be useful, but maybe you could be even more useful with another
specialty.

It's why I think the cliché "follow your bliss" is a pretty good heuristic.
Another way of putting it: follow the gradient of your own intrinsic
motivation, and try to have fun along the way.

And make good friends!

My own prognosis is that open source development is going to play a huge role
in the future, and that participating in the open source commons is _the_ way
to "network" in the software world. If an employer won't let me work openly,
I'll consider that a major downside for many reasons. So I would bet on open
source involvement as an important career investment.

(That's part of why I think cryptocurrencies are really exciting, but that's a
longer and more tenuous argument...)

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askari01
thank you for your kind advice. I am very motivated in following my heart. But
sometimes I feel low comparing my work with my friends and seniors. I have
embarked a journey less travelled by developers. There is very few demand of
the work which I do which worries me. Which causes problems for me financially
but I love my work. This is the very main reason I am looking to explore other
options. But I fear I may end up jack of all trades but master of none. I will
be waiting for your reply, need your honest opinion n guidance

thanks

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meric
Don’t compare ... imagine if you had the option to swap “souls” with those
other persons - your job, your work, your family, your face, your friends,
your spouse, and the other person won’t even notice, would you take it?!

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AnimalMuppet
Choosing a path just means choosing a path for now - maybe for the next 5 to
10 years. Assume that you will have at least four such paths over the course
of your career. So you don't have to get it "right" \- you don't have to pick
a path that you will enjoy for the next 40 years, or pick a path that will
still be a viable career in 40 years.

Pick what looks best to you right now, and for the next 5 years. As you walk
down that path, learn as much as you can, not just about that path, but about
neighboring things. Sometimes learn them in a targeted way, because you think
they may be things you need to learn to keep your career viable, but always
learn something about whatever you're coming into contact with.

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apohn
Keep in mind that many of the areas you state will mature and you will not
need to be a subject matter expert to use them.

Take for example Hadoop. 5 years ago you had to be able to code to use Hadoop
effectively and many people were convinced their big data careers would
collapse if they didn't immediately learn to code in the Hadoop ecosystem.
Today people are starting to use GUI tools (e.g. Pentaho) for ETL and SQL is
replacing writing map-reduce code for many people. Proper Data Engineers with
solid technical skills are still heavily in demand, but many of the pseudo-
technical people are finding their jobs are changing.

You see something similar with AI and ML. 10 years ago AI was "dead" and
ML/Stats was for expert CS and Stats people. Now you can do a lot with a few
lines of code or API calls. What makes a good Data Scientist/Machine Learning
person is not so much understanding every aspect of an algorithm, but being
able to understand what is takes to quantify a business problem into a form
that is suitable for ML/AI. That requires a blend of technical and business
skills.

I'd echo what mbrock says. Look at these areas and others (example - The
Blockchain: the next thing that is supposedly going to solve all the problems
in the universe). Get a solid foundation in CS, find an area that interests
you and pursue that with focus. Realize that every 5-10 years you'll need to
update your skillset to the next big thing in your area of interest.

I'd say that out of all the areas you've listed MR is the newest. There's
going to be a lot of hype around that for a few years, and hype = companies
want to spend money = jobs.

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askari01
thank you for taking out time and commenting. I love AR & VR. I believe they
are the future and will become more mature with time. I am very interested in
autonomous driving cars and want to explore them. But it is not possible and
it discourages me to not being able to work on things you like.

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tugberkk
The question of the post and the post itself are not the same imho. I think
Post-quantum cryptography has a great role in the future of computer science.

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askari01
thanks for the comment, I agree question and comment don't match. But I am
confused when comparing my skillset to others. Main reason is too few
opportunities with the current skill set I have. So, I wanted some guidance.

