Have you ever wondered “So, what’s next for me?”
Well, you’re not alone! Many graduates aren’t too sure what they want to do after graduation.
That’s especially true for Econ majors. Trust me – I am one.
And one of the often-overlooked options is data science.
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 economics and examine the
good, the bad and the ugly. We’ll answer some of the most important
questions running through your mind, like: “Can I”, “Should I” and “How can
I” make this switch. And we’ll discuss the pros and cons before finding the best
way to transition into Data Science.
Let’s start with “Can I make the switch?”. The answer here is a resounding “yes!”.
Roughly 13% of current data scientists have an Economics degree. For comparison, the most
well-represented discipline is data science and analysis, which takes up 21% of the pie.
Therefore, Economics is a competitive discipline when it comes to data science.
This isn’t at all surprising for several reasons.
First, unlike STEM disciplines, social studies help develop great presentational skills which
are essential for any data scientist. Through presentations and open discussions, students
learn how to present a topic, as well as argue for or against a given statement. These activities
result in developing a confident and credible way of showcasing actionable insights. Moreover,
most econ majors deeply care about human behavior and response to different stimuli.
Hence, social-studies majors can capably serve as mediators between the team and management.
Second, economists often have a different approach than C.S. or D.S. majors. Due to
their superior understanding of causal relations, social-studies graduates can add another perspective
when looking at the data and the results. This is extremely important because their
casual inference allows them to think beyond the numbers and extract actionable insights.
Furthermore, Economics frequently intertwines with Mathematics, Finance, Psychology, and
Politics. Therefore, an economist’s approach is always meant to be interdisciplinary.
Finally, the technical capabilities of an economist are often quite impressive. An average
economist has a good understanding of Machine Learning without really referring to it as
such. Linear regressions and logistic regressions are studied in almost all Economics degrees.
I think we are pretty convinced about the “Can I” part. So, let’s move to the
“Should I” part. Well, the answer here is “Yes” – with
a very small asterisk next to it. Now, any Economics graduate possesses many
of the required skills to transition into Data Science, but that doesn’t necessarily
suggest they should do it… They might be more suited for something else.
For example, an Economics graduate with an affinity for Political science will most likely
thrive better in a policy advisory role in a bank or hedge fund or even in a government
position. Similarly, less-coding-savvy social-studies graduates are a finer fit for data analyst
positions, where machine learning algorithms are relied upon less frequently. It’s not
that either one wouldn’t be able to succeed as a data scientist, but their skills are
better suited for different career paths.
So, let’s look at the question like an economist would – through the lens of incentives.
Where does one find the incentives? That’s right - in a job ad.
The main components of a job ad are the level of education, years of experience and indispensable
skills. We already discussed how popular Economics
is compared to STEM degrees, so you know it’s a good choice for a potential career as a
Data Scientist. When it comes to economics degrees, 43% of the job ads in our research
require a BA and an additional 40% a Master’s. Hence, due to the interdisciplinary nature
of social sciences, you don’t need to get a doctorate to be successful in the field.
As for years of experience, if you’re transitioning from another position in business, you’ve
probably had to do some analytical thinking already. Usually, 3 to 4 years in such a setting
are enough to ensure a smooth transition, but this is tightly related to your level
of education. A candidate with an M-S will require 2 fewer-years of experience in a business
setting due to their additional academic qualifications. However, if you’re trying to make a transition
straight out of college, you might want to go for an entry-level job in the field. We’ve
got a special video on that one, by the way! When it comes to skills, one of the key parts
is understanding statistical results and their implications. Luckily, economics degrees are
often based on statistical study cases and experiments, so you should feel comfortable
interpreting the results. Of course, this expands to understanding the intuition behind
M-L algorithms and their limitations. As we already stated, Econometrics incorporates
linear and logistic regressions, so Economics graduates have a great grasp of the intuition
behind Machine Learning models. Additional skills listed in such job ads include
problem solving and strong analytical thinking. A lot of Economics degrees heavily rely on
examining study cases, solving practical examples and analyzing published papers, so you probably
possess these qualities already. Of course, communication skills are essential
when working in a team and as we mentioned earlier – economics graduates often serve
as a bridge between the data science team and higher management.
Lastly, anybody making the switch to Data Science needs a certain coding pedigree. Whether
it’s R, Python or both, knowing how to use such software is a must if you want to succeed
in the field.
If you’re an Economist in your 20s, we can assume you have seen some Python or R code.
Hence, you only need to gather more work experience in a business setting.
If you are above 30 and you aren’t a CS graduate, you most probably didn’t use the
computer in your university classes. So, you may think your main challenge is the lack
of programming skills. But that shouldn’t be the case.
Just focus on the technical part – programming and the latest software technologies.
Coding has never been easier, and anyone can learn. Especially a person from an economics
background. We all know you have seen some very complicated stuff.
After all, a significant part of our team is coming from an Econ background but transitioned
into data science. In fact, 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.
After answering the “can” and “should” parts of the discussion, let’s dive into
the “how-to” part. There are generally 4 crucial things you need
to do to make the switch. The first one is picking your spot.
As discussed, there is plenty of room for Economics graduates in Data Science. All you
need to make sure you’re ready to fit exactly that role and demonstrate your strengths.
Employers value your understanding of causal inference, so you need to highlight that in
your application. Showcase the analytical part of your work.
Mention insights you gained through research or academic work and quote their measurable
impact. These bring credibility and provide recruiters with a glimpse of what they’ll
be getting once they hire you. Second - use your social science advantage.
By knowing how surveys and experiments are constructed, you know where to look when examining
the results. You see beyond the data and understand which M-L approach should work best in each
case. In contrast, D-S and C-S graduates often have
a mindset of “How can I pre-process the data before I run an M-L algorithm?”, instead
of looking at the way the data was gathered. Your understanding of collinearity, reverse
causality and biases can help you accurately quantify interdependence within the data.
Thus, you can have great synergy with the rest of the members on your team.
The third and most crucial change you need to make is to adapt your way of thinking.
Even though the cause & effect mentality will help you settle in your career, you need to
be able to look for other things as well. The findings of Neural Networks algorithms
can be confusing because they discover patterns rather than causal links. Hence, you need
to be ready to demonstrate flexibility in your thinking and adjust accordingly.
Of course, this isn’t a change that can happen overnight, but rather one that happens
gradually with experience.
Last but not least, you’ll need to learn a programming language or BI software.
Lucky for you, programming languages such as Python and R aren’t that hard to learn.
And once you’re fluent in one programming language, you can easily master another one,
despite coming from an Economics background. This also falls into the “learn as we go”
area, so just make sure to be proficient in at least one of either Python or R, and your
transition into the field should be smooth as butter.
Alright! In this video, we discussed that Economics
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, economics skills are mandatory for any data
science team. Thus, there is no doubt that you, dear Econ major, could be that person.
Good luck! If you liked this video, don’t forget to
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Thanks for watching!