Looking for the best ways to transition into
data science?
Well, some degrees can give you a massive
advantage.
And a degree in C-S certainly qualifies you
for this rewarding and challenging career.
Welcome to this 3-6-5 Data Science series
of videos where we discuss how to transition
into data science.
Today, we’ll be making the switch from Computer
Science and explore the steps you need to
take to enter one of the hottest career fields.
We’ll answer some of the most important
questions that go through your head, like:
“Can I”, “Should I” and “How can
I” make this switch.
We’ll discuss the pros and cons and give
you some tried-and-tested tips to transition
into Data Science.
Let’s start with “Can I make the switch?”
Well, if you can’t, then no one else can.
A degree in C-S prepares you to be a code-savvy
professional with strong analytical thinking,
and a knack for creative tech solutions - which
makes you the top choice of data science employers.
Professionals with that degree are skilled
in mathematics and problem solving.
Not to mention they are already proficient
in several programming languages and tools.
No wonder 18.3% of current data scientists
have majored namely in Computer Science!
So, let’s explore in detail the major points
Computer Science helps you score :
The first and the most important advantage
a C-S background gives you is spectacular
problem-solving skills.
Computer Scientists thrive in challenging
situations.
And solving complex issues is just a regular
part of their lifestyle!
Basically, what they do on a daily basis is
identifying a problem, translating it to the
computer, and finding the smartest way to
deal with it.
Over and over again.
A C-S graduate rushes in and finds solutions
where others fear to tread which makes them
a leading figure in any data science team.
Second - writing a code that’s reusable
and understandable by others.
This is one of the most precious skills for
everyone working in data science.
Why is that?
For one thing, it saves a lot of time for
everyone involved.
If your code is very hard to follow, no one
will want to use it.
Especially in a fast-paced business environment
where data science teammates should work like
a well-oiled machine.
On the other hand, writing readable code that
complies with the best practices speaks volumes.
It shows you’re good at explaining your
way of thinking to others, which is undeniably
crucial for a data scientist working within
a cross-functional team.
As a C-S person, you obviously know how to
do that, so this box is ticked!
And third – having a super-versatile toolbox.
Data scientists rarely fly solo.
That said, your ability to work with TTD or
version control systems, like Git, for example,
is indispensable to managing the code: including
past changes, speed of execution, and development
of the project.
A data science team needs someone who knows
how to monitor timelines or check if the code
is labeled properly.
Not many people are highly skilled at that,
but a C-S graduate has the know-how that certainly
gives them an edge.
Alright.
We believe now you know transitioning into
data science from Computer Science is not
a question of “Can I?”
rather than “Should I?”
Well, every person is different and so are
their career choices.
Data Science has been recently “discovered”
and giving it a worldwide meaning seems to
be a problem.
Because of that, understanding the data science
industry is a tough job.
We might say that in most places being a Data
Scientist will require you to work in a chaotic,
continuously developing and challenging environment.
And, yes, 20 years ago, there wasn’t a Data
Science job…
And you may ask “Why?”
The main reason is that there wasn’t that
much data to work with.
But this is not the case now.
There are 2.5 quintillion bytes of data created
daily and businesses are in dire need of people
working on it to improve our lifestyle, health
and more…
In fact, the demand for data science professionals
is so high that it will be hard for the supply
to catch up for many years to come!
That also explains the $100,000+ median base
salary and why reports like Glassdoor’s
50 Best Jobs have consistently named Data
Science the winner for the past few years.
Consider this – data science today is very
close to how computer science was perceived
back in 2005.
Actually, D-S and C-S are very similar in
that they are following the same demand and
supply laws…
But only with a 20-year difference.
So, you might as well take advantage of that
before the market gets overcrowded with highly
trained data scientists and salaries start
to plateau.
So, how to do that?
Knowing how to code has already put you on
the fast track to the DS role.
What you might miss in terms of knowledge
is:
Statistics –Computer Scientists boast a
deterministic mindset.
This compels them to want to have all possibilities
covered.
And that’s great, but, to be a data scientist,
you need to shift to a statistical or even
better – a probabilistic mindset.
Why?
Well, because of how data science works - events
follow distributions and there are probabilities
associated with each possibility.
So, that’s a whole new way of thinking to
adapt to.
Machine and deep learning – you guessed
right -usually, these aren’t covered in
the C-S curriculum.
But it is namely sharp predictive modeling
skills and advanced deep learning techniques
that will give you a huge competitive edge.
Fortunately, there are plenty of post-graduate
qualifications and online trainings that will
help you get there.
Reading research papers –Math, Statistics,
and D-S majors are very science-oriented.
so reading, understanding and applying the
technical methods in said paper is no challenge
for them.
But these don’t come naturally to a C-S
graduate.
That said, being able to apply concepts from
papers is the number 1 skill demanded in top
companies, so adding research to your reading
list is certainly worth the effort.
Data Visualization – representing a whole
data research on just a few graphs and tables
is a major component of a data scientist work.
And it’s not an easy task.
So, while you may prefer to code, adding software
tools like Tableau, Power BI, and Excel are
a must for any data scientist.
Overlooking these could be the biggest mistake
of C-S graduates.
Remember – in the business world, sometimes
it is about completing a task in 5 minutes
and not about writing the most parameterized
code.
Okay.
So, if you’ve set your sight on making the
switch, we’ve got you covered.
We developed the ‘3-6-5 Data Science Program’
to help people of all backgrounds enter the
field of data science.
We have trained more than 450,000 people around
the world and are committed to continue doing
so.
If you are interested to learn more, you can
find a link in the description that will also
give you 20% off all plans if you’re looking
to start learning from an all-around data
science training.
But even with these skills under your belt,
data science is no easy street.
In fact, one of the biggest challenges you’ll
face is working efficiently with both C-level
executives and team members with various backgrounds
and fields of expertise.
So, if you think that employers are only looking
for top technical talent – you’re wrong.
A data scientist should also be a great team
player.
According to an internal study ran by Google,
the most inventive and effective teams within
the corporation weren’t the ones full of
top scientists.
Instead, their best performers were interdisciplinary
groups with employees who brought strong soft
skills to the table and enhanced the collaborative
process…
Which brings us to Leadership.
As a data scientist, you will not only plan
projects, and build analytic systems and predictive
models.
You will also be the leader of a data science
team.
And managing a team of other data scientists,
machine learning engineers, and big data specialists
requires more than drive and vision.
In a data science team, you can always teach
others or be taught yourself, regardless of
their level in the hierarchy.
So, keeping an open mind to new and challenging
ideas is a must.
But don’t worry if you don’t feel you’re
cut out to be a leader just yet– as long
as you have empathy, integrity, and the desire
to listen to your team’s needs and concerns,
you can grow to become an outstanding Lead
Data Scientist.
Alright!
In this video, we discussed that Computer
Science majors can, and should, try to pursue
a career in data science because they have
the necessary skills and there is high market
demand.
Surely, programming skills are mandatory for
any data scientist.
Thus, there is no doubt that you, dear C-S
major, could be a successful one.
Good luck!
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Thanks for watching!
