Data science jobs are in. Fresh grads, here’s what you need to know

To the fresh grads and incoming freshmen, we can feel your apprehension — deciding on the field or industry that might determine your career path is crazy, especially if that field involves numbers, terabytes of information, and the esoteric art of coding. With these tips though, you don’t have to go into this wild world empty-handed.

According to the 2019 Emerging Jobs in the Philippines Report by LinkedIn, one skill that the job market is sniffing around for is digital competency. (And no, we don’t mean “proficient in Microsoft Word,” so y’all can stop putting that on your CVs.) In a world governed by data and algorithms, you’ve automatically got a leg up if you know your way around technology, whether as an engineer, developer, or a sales representative who absolutely crushes at Microsoft Excel. But let’s talk about one specific profession: data scientists.

Data science is a huge, multi-disciplinary field, as we’ve learned in our roundtable interview with Mark Toledo, an app developer at CirroLytix, and Leona Lao, product manager at Cobena Business Analytics. Data science involves programming, computing, communication and other skills that involve producing insights from unstructured information.

And as the data industry matures and refines its demands, we thought we’d do a little insight-producing of our own, and let you guys in on some things to consider, if you’re interested in pursuing a career in data science. We look at three common misconceptions about the data scientist profession, and bust ‘em the way a hacker busts through a firewall. (Zuckerberg, if you’re reading this, let us know if we missed anything.)

Myth 1: You need to be good at math to work with data.

For Real: You don’t need to be a math whiz, because there’s more to data science than just that.

People think that because data science requires the crunching of numbers, you need to be good at long division, exponents, PEMDAS (PEMDAS!!!) and whatnot to even qualify for the profession. Not entirely true.

“It’s also a lot of creative problem solving, being able to communicate,” Leona says. She gives the example of coding. A line of code doesn’t have to be just correct — it can also be elegant. That sort of thing requires creativity



Myth 2: Automated labor will eventually replace the profession.

For Real: Some jobs still need that human touch.

Automation is a tricky issue that many have attempted to unpack, but we can all agree that automated labor is changing the job market. There is the sensible concern that jobs will be harder to find the more we trust automatons to do those jobs, so it stands to reason that the work of computing is better taken care of by computers, right?

Not exactly. Mark’s line of work involves working with other people in retail, applying data science to shelf economics (measuring products on a shelf, measuring stock and supply, etc). Using data is a pretty efficient way to solve those problems, the kind of issues that would otherwise take a lot of strenuous manual labor. Ideally, automation makes things easier for everybody, and can even make you more deeply aware of the limits of both man and machine. “Programming, particularly, I think trains a mindset of learning what questions can or can’t (be solved), and being more cognizant of what computers can do,” Leona says.

Fact vs fiction: Mark Toledo and Leona Lao share truths about the field of data science that we might not know.

Myth 3: It’s a geeky, isolating profession, and making friends is hard in that line of work.

For Real: In a field like data science? You’re gonna get around.

According to Mark, the industries that are hungriest for data scientists are, well, industries that work with lots of data! So there’s retail, as mentioned above, but also telcos, banks, and even marketing agencies. Imagine data science as one circle overlapping with a bunch of other Venns.

Even without considering overlapping industries, building a network is entirely possible. “I started this career because of LinkedIn,” Mark says. He was able to find his current line of work and mentor because of the platform, and how it allowed him to present his best professional self to a community he wanted to be a part of.

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