Once you complete your degree program in Economics, you’ll probably wonder, ‘what next?’
Well, lucky for you, millions of other graduates are also planning their next step. But here’s the thing: there are a million many more options for you than the ones your family and friends are telling you.
What if I told you, you could enter the wondrous world of data science with your economics degree? Unsure? Well, keep scrolling. We’re going to tell you how to jump into the world of data science. Ready? Let’s start at the top.
First and foremost, let’s make sure we’re all on the same page.
Data science refers to the combination of domain expertise, programming skills, and knowledge of mathematics and statistics necessary to find meaningful insight from the given data.
While this is true, data science is a much broader term now. In simple words, you need to know machine learning and coding to become a part of this world.
Now that you know what data science means and you already know economics let’s learn how you can transition from one to the other.
When it comes to being a data scientist, there’s a lot to unpack. From assessing published papers to examining case studies, there’s an array of different jobs related to what you’ve studied.
So, first of all, make sure you know what role you want to play. Next, demonstrate your prowess in that role. Showcase your analytical skills and your well-thought-out insights. Lastly, wait for your employer to get blown away!
You’ll find several undergraduates in data sciences and computer science and data science majors working in the office. Here’s a little secret for you economic majors: your social studies gives you a huge advantage.
When it comes to displaying your ideas and insights, presentations and open discussions are the way to go. And who better to think beyond the numbers than economic majors?
Your economics degree allows you to approach data sciences uniquely, embrace it!
When you start working in an office, you’ll have to look out for other things too.
From confusing neural network algorithms to discovering patterns in casual data, there will be a lot for you to work with. Therefore, you’ll have to make sure you can adapt to the type of work assigned.
Be flexible and open to learning and demonstrating newer skills.
It’s no secret: data science needs you to have at least a little know-how of programming and codes.
If you’ve never done any, don’t worry, you can start with Python or R. Both are super easy. Plus, once you’re fluent in them, you can jump to others as well.
If you’re still saying, ‘well, that doesn’t answer my question. Should I or should I not?’ Here’s a straight answer: yes!