Latest / Career Skills with Fexingo: Hard Skills, Soft Skills, and Future-Proofing Your Career / How to Know If a Skill Is Worth Learning
Transcript
- Lucas: So Luna, I was talking to a friend last week, a marketing manager, and she told me she's thinking about dropping two thousand dollars on a data analytics certificate. And my first thought wasn't 'great idea' — it was 'how do you know that's worth it?' Luna: That's a big question. I mean, everyone's telling us to upskill, learn data, learn AI, but nobody really walks you through the cost-benefit of it. Lucas: Right. And that's exactly what I want to drill into today. Because there's this idea that any skill is better than no skill — that learning something new is always a net positive. But that's not true. Every hour you spend learning one thing is an hour you're not spending on something else, or on your actual job, or on rest. Luna: Opportunity cost. That's the phrase. Lucas: Exactly. So I want to propose a simple test, something I call the 'if it was worth it' test. It's not about whether the skill is useful in some abstract sense, but whether it's worth it for you, right now, given your goals. Luna: Okay, I'm already curious. How does the test work? Lucas: Well, first you have to define what 'worth it' even means. For most of us, it's a mix of career advancement, salary upside, job security, and personal satisfaction. But you have to weight those differently depending on where you are in your career. Luna: So for someone early on, maybe salary and job security are heavier. Later, maybe it's more about satisfaction or flexibility. Lucas: Exactly. And once you know your weights, you can estimate the potential upside. Let's use my friend as an example. She's a marketing manager at a mid-sized consumer goods company. She's making, let's say, ninety thousand a year. She's thinking about a data analytics certificate because she sees job postings asking for it, and she thinks it might help her move into a more senior role. Luna: So what's the potential upside? A promotion to senior marketing manager might come with, what, a ten to fifteen percent raise? Lucas: Right. So that's maybe nine to thirteen thousand dollars a year. Now, the certificate costs two grand and probably takes about a hundred hours of her time, spread over a few months. So the direct cost is two thousand dollars plus a hundred hours. If her time is worth, say, forty dollars an hour — that's another four thousand. So total investment, about six thousand. Luna: And the payoff is potentially ten grand a year, for as long as she's in that role. That seems like a no-brainer. Lucas: On paper, yes. But here's where the test gets interesting. The question isn't just 'could it help?' — it's 'how likely is it to help?' And that's where most people get stuck. They see a job posting that lists data analytics as a 'nice to have,' and they assume that's a requirement. But hiring managers often list skills they'd love to see, not deal-breakers. Luna: So you're saying she should look at the actual job descriptions and see how often it's listed as 'required' versus 'preferred.' Lucas: Exactly. If it's preferred in most postings, the probability it's the deciding factor is lower. Maybe she's better off polishing her portfolio or networking with hiring managers directly. That's a lower cost and potentially higher return. Luna: That's a good point. So the test is really about estimating the probability that the skill actually moves the needle. Lucas: Right. And that probability depends on a few things. One, how common is the skill in your industry? Two, how much does it differentiate you from your peers? And three, is it something that's becoming table stakes, or is it still a differentiator? Luna: Like, ten years ago, basic Excel was a differentiator. Now it's just expected. Lucas: Exactly. And you can see that with data analytics. It's moving from 'nice to have' to 'expected' in many marketing roles. So the question becomes: is it still a differentiator, or is it table stakes? If it's becoming table stakes, you might need it just to stay even, but it won't give you a step up. Luna: So how do you figure that out? Lucas: Look at job postings from, say, three years ago versus today. If the skill is appearing in more postings, it's becoming more common. Also, talk to people in roles you aspire to. Ask them directly: 'how often do you actually use data analytics in your day-to-day?' Luna: That's a great idea. I've done that for skills I was curious about, and it's surprising how often the answer is 'not as much as you'd think.' Lucas: Right. And that's where the 'if it was worth it' test gets its name. You have to ask yourself: if I learn this skill, and I get the job or the promotion I want, would that outcome be worth the time and money I put in? But you also have to weigh the probability. If there's a fifty percent chance it helps you get a ten thousand dollar raise, that's an expected value of five thousand. And you have to compare that to other investments of your time. Luna: Like, maybe spending those hundred hours on building your internal network would have a higher expected return. Lucas: Exactly. And that's the thing. Most people don't do this calculation. They just assume that learning a new skill is always good. But sometimes the best career move is to double down on the skills you already have, or to focus on skills that are more directly tied to the role you want. Luna: So let's talk about that 100-hour rule. I've heard that you can get a solid foundation in most skills with about a hundred hours of deliberate practice. Is that realistic? Lucas: I think it's a useful heuristic. With a hundred hours, you can go from complete novice to functional competency in many skills — basic coding, public speaking, data analysis, even a new language to a conversational level. And that's enough to test whether you actually like it, and whether it's useful in your work. Luna: So the idea is, don't commit to a full certificate or a degree before you've done a hundred hours. Lucas: Right. Do a small, low-cost project first. Maybe take a free online course, or shadow someone at work who uses that skill. See if it actually improves your performance or opens doors. If it does, then invest more. If not, you've saved yourself a lot of time and money. Luna: That makes sense. But what about skills that are harder to learn quickly, like advanced statistics or machine learning? A hundred hours might not even get you to the starting line. Lucas: That's a fair point. For those, you might need to look at the long-term payoff. If you're aiming for a data scientist role, you might need a few hundred hours or even a degree. And that's where the test really matters. You have to ask yourself: is this the path I want? And is the payoff worth the years of study? Luna: And that's where I think a lot of people get into trouble. They see the salary for data scientists and think 'I want that,' without realizing the time and effort involved. Lucas: Exactly. So the test isn't just about whether a skill is worth learning, it's about whether it's worth learning for you, at this stage of your career. And that's a very personal decision. Luna: Speaking of personal decisions, Lucas, I've been thinking about something you said earlier — about how every hour you spend learning is an hour not spent on something else. And I think that's a good segue to mention something that's been on my mind, and I think it's relevant to our listeners who are constantly weighing these kinds of trade-offs. Lucas: What's that? Luna: Well, you know how we often talk about making deliberate choices about your time and money. And I think that's exactly what our listeners do when they support this show. They're deciding that an ad-free podcast that helps them think through their career is worth a small investment. And if today's episode has been worth a coffee to you, that's the link — buy me a coffee dot com slash fexingo. Lucas: It's a small way to keep us ad-free and keep these conversations going. And honestly, it's the kind of deliberate decision we're talking about — choosing to support something that adds value to your career. Luna: Yeah, exactly. And no pressure, only if it's worth it to you. But now back to the test. You were saying that the probability of payoff is key. How would you estimate that more concretely? Lucas: One way is to look at the actual hiring data. For example, a few years ago, a survey found that only about twenty percent of marketing job postings mentioned data analytics as a requirement, but that number has been climbing. So you can track that over time. If it's growing, it's becoming more important. But you also have to look at the level of the role. For a senior marketing position, it might be more important than for an entry-level role. Luna: So you're saying to look at the specific level you're targeting. Lucas: Exactly. And also talk to people who are actually in those roles. Ask them what skills they use daily. That's the ground truth. Job descriptions can be aspirational, but the people doing the job know what's actually needed. Luna: That's a really practical tip. And I think it's a great way to wrap up today's episode. So to summarize, before you invest in any new skill, ask yourself three questions: What's the potential upside? What's the probability that investment pays off? And is there a cheaper way to test it first? Lucas: That's a solid framework. And it's really about being honest with yourself. Not every skill is worth learning, and that's okay. The key is to make conscious choices about where you put your time and energy. Luna: And if you do decide to learn something, the 100-hour rule is a great starting point. You can always go deeper later. Lucas: Exactly. So the next time someone tells you 'you should learn Python,' you can pause and ask yourself: is it worth it for me? And if the answer is yes, go for it. If not, it's fine to pass. That's the kind of intentional career management that really pays off in the long run. Luna: Well said. And I think that's a great note to end on. Thanks for joining us, everyone.