Hi, and welcome to another 365 Data Science
Special.
This video is not here to give an overview
of data science as a super smart career decision.
Instead, we’ll look into what makes a data
scientist successful in 2019!
First things first – what is a data scientist?
Well, the term was first used by DJ Patil
and Jeff Hammerbacher in 2008, around the
same time that the words swag and chai latte
made it to the Oxford English Dictionary.
It’s safe to say that while these two are
fairly easy to define, data science is a little
bit harder to understand.
Essentially, the field deals primarily with
data, no joke.
A data scientist can take the data – small
or big – and start developing, implementing,
and deploying machine learning algorithms.
They use advanced statistical methods to do
predictive analytics and get meaningful insight
from the data.
Often, the data scientist will also dabble
in deep learning making use of the latest
tech developments, such as neural networks
and the likes.
Obviously, all of this requires a fairly mathematically
inclined thinking style, and a lot of programming
skills.
But is this all?
What about their education, work history,
formal qualifications, and… of course, which
industries hire data scientists in 2019 and
pay them a base salary of $117,000?
Let’s reverse-engineer the data scientist
and find out!
If you want to find what makes a data scientist
– think like one.
We conducted a study – methodology in the
description – to find out more about the
typical data scientist of 2019?
At a glance, there’s a certain type of professionals
who should definitely stay on board.
The domain is still strongly dominated by
men (69%), who can hold a conversation in
at least two languages.
They have been in the workforce for 8 years,
but only working as data scientists for 2.3
of them.
They can proudly frame up a second-cycle academic
degree (74% hold a Master’s or a PhD), and
do a lot more than program “Hello World”
in Python or R (73%), often both.
Luckily for those, who are female, or have
not yet earned our Doctorates, the segmentation
of the data tells a richer and truer story.
So, you might be wondering does data scientist
imply Doctor of Philosophy?
And the answer is simple.
Just as the field is not impregnable by women,
so is having a PhD not a prerequisite for
the position.
In fact, less than a third of the data scientists
in the cohort hold a Doctorate degree (28%).
This is a comparable number to last year’s
27%, which seems to entail that industry does
not intentionally introduce an unattainable
degree of academic prowess.
So far, so good for 2019!
On the other hand, if Master’s degree is
something into which the aspiring data scientist
is willing to invest time and effort, it seems
to be the golden standard for academic qualifications
(46% of the sample hold a Master).
In fact, we can speculate that in the future
the requirement for a second-cycle academic
degree will decrease, being evened out by
data scientists penetrating the field with
only a Bachelor’s.
And there is already a 4% increase compared
to last year in the number of data scientists
with only a Bachelor’s degree.
Actually, unless you’re coming straight
from academia, having a PhD or master’s
is not essential.
Especially if you can land an internship (8%)
or come from an IT background (9%).
What’s interesting, though is that there
a lot of other gateways to a job as a data
scientist.
For example, academic researchers (9%), data
analysts (13%), and consultants (6%) all have
pretty respectable chances of being data scientists
in 2019!
And if you are to take a look at what you
need to study to become a data scientist,
you’d notice that the gateways to data science
are very many.
From Economics and social sciences (21%),
through natural sciences (11%), statistics
and mathematics (16%), computer science (22%),
engineering (9%), and of course, data science
and analysis (12%).
There was even one person who studied Law
in our cohort!
That’s all super interesting but do you
still have to go to an Ivy league college
or…?
Actually, not at all.
University ranking doesn’t seem to influence
your chances of becoming a data scientist
in 2019.
While a high number of the data scientists
in our research indeed come from the Top 50
universities (31%; according to the Times
Higher Education Ranking for 2019), almost
as many come from universities ranked above
1001+ (24%).
So, Data Science is definitely not a private
playing field for Ivy league graduates.
However, one thing almost half of our data
scientists have in common is online courses.
43% of them said they have gained at least
1 certificate from an online course, with
3 being the average.
E-learning is definitely a resource many data
scientists take advantage of.
And it checks out – some of the best online
programs out there are dedicated to help you
master various programming and data handling
skills, which, after all is the bread and
butter of the data scientist.
But we still haven’t talked about that,
have we?
Alright, so Python (54%) is definitely leading
the pack when it comes to programming skills
the 2019 data scientist needs, and that is
universal all around the world.
R (45%) comes second but in UK it’s definitely
lagging behind in popularity.
SQL (36%) and MATLAB (19%) also prove to be
widely considered handy, so you might want
to invest some time brushing up on these,
too, if you’re considering joining the data
science race this year.
And in which industry should you expect to
be spending your glorious days as a data scientist?
This was definitely easier to answer last
year, because the data was heavily pointing
to the Tech industry as the titan hiring the
most data scientist.
2019, however, is introducing a lot more diversity
as is the trend with a lot of other things.
That said, the Tech/IT industry (43%) is still
a major employer but the industrial sector
is rapidly catching up (39%).
What’s even more interesting is that in
the UK more scientists work there (38%), instead
of in Tech/IT (35%).
The financial sector is claiming 16% of the
data scientists worldwide, with UK again leading
the pack (24%).
No surprises here – London is (or used to
be, kh, kh) Europe’s financial capital and
plenty of financial, trading, and brokerage
firms reside there.
In that respect, the US stands at the humble
9%.
The job market for data scientists there mostly
employs data scientists in the Tech/IT sector
(47%).
But perhaps the most important question we
can answer here is which location leads to
the fastest career progression?
Based on our data, India and the UK are the
two locations where a data scientist can thrive
even with little to no experience.
The States are the least accommodating to
new data science enthusiasts and prefers professionals
with 3 to 5 years of experience.
Well, now you know where to look if you want
to get promoted from already being in a data
scientist position!
So, what’s the key take-home message here?
Hopefully, this video does not make you doubt
whether the data scientist profession is something
you could realistically pursue.
Instead, we hope to have lent a reassuring
hand.
One of the main messages we extracted from
our study both last and this year, is that
if you have the skill base that makes a data
scientist, you can be a data scientist.
It will be interesting to see how the data
science profession changes in the next 2-5
years, but right now, a universal data scientist
profile appears to be taking shape: a unique
programming language toolbox desired across
industries and locations; preferably a Master’s
degree, or a Bachelor’s and proof of practical
abilities; and a confident learning-on-the-go
attitude are the currencies of the field.
If you are interested in a solid data science
preparation starting from scratch, make sure
you visit our website 365datascience.com where
you can start your preparation and see if
this is the career path for you.
