Why transition to Data Science from Economics?
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.
So, in this article, I’ll tell you how to transition to data science from economics.
I’ll examine the good, the bad, and the ugly; answer some of the most important questions running through your mind, like: “Can I”, “Should I” and “How can I” make this switch. And I’ll explain the pros and cons before finding the best way to transition to data science from economics.
How to Transition to Data Science From Economics
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 indeed 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 that 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 Computer Science or Data Science 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.
Should I transition to Data Science from Economics?
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 Master of Science will need 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.
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 machine learning 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.
As 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 Computer Science 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.
We answered the “can” and “should” parts of the discussion, so let’s dive into the “how-to” part.
How can I transition to Data Science from Economics?
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 Machine Learning approach should work best in each case.
In contrast, Data Science and Computer Science graduates often have a mindset of “How can I pre-process the data before I run a machine learning 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.
All things considered, 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.
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