188: 7 Brutal Myths About Landing a Data Job That Are Keeping You Stuck

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Number one is that data analysts, data scientists and data engineer are the only titles, and in particularly I'm gonna focus in on data analysts. Now I love becoming data analyst. I talk about becoming a data analyst all the time. But really, when I say data analyst, I really mean like a hundred different titles and roles that you possibly could be applying for.

Data analysts is someone who analyzes data, but there's a bunch of other titles that you may not be thinking of that actually do the exact same task. So for example, a financial analyst and data analyst. Kind of do the same thing. It's just a data analyst, very focused on financial things. A business analyst is like halfway data analyst, halfway like a business operations person.

uh, logistics analyst, like. [: 00:01:00

Now, that's not to say that those titles are better than data analysts. They're not necessarily, but they all are kind of the same. And in the end, there's actually a lot more jobs that are data analyst jobs that aren't called data analysts, for example, I can't remember the numbers off the top of my head, but on my data job board.

Find a data job.com. There's actually more financial analysts and business analyst roles than there are data analyst roles. So not only are there more opportunities in those two different roles, but also you probably have less people applying and looking for those roles. So there's uh, more of a supply and less of demand, which is great for job seekers like you because it means it's less competitive.

doing yourself a disservice.[: 00:02:00

Myth number two is that you currently right now have zero data experience and there's just no way on planet earth that's true. I don't care what your profession is, I don't care what you've done in the past. You have some data experience and you need to stop selling yourself short.

So here's what you're gonna do. You're gonna look at everything that you've ever done in your life and try to figure out when you have some sort of data analytics experience.

So an easy thing to do is be like, okay, when have I ever used Excel or Google Sheets? When have I ever used some sort of a spreadsheet or a table? If you're using a spreadsheet or a table, we can chalk it up to some sort of data analytics experience. And if you're like, Hey, I've never used it before. Okay, but you probably have right?

In school or at work, or even at home, you've probably used a spreadsheet that counts as data analytics work. Next, I want you to think about whenever you've made some sort of a decision, you probably did it in some analytical way by like making a pros and a cons, right?

decisions, and I think that [: 00:03:00

Also, I think on your resume, whatever job you've had in the past, you can figure out a way to make it have some sort of data analyst experience. Figure out when you used Excel, figure out, when you made decisions and craft bullet points around that. Uh, I promise, no matter your job, let's just take a teacher, for example.

Like does a teacher do data analytics? No. But can we make it sound like it? And is it kind of a little bit data analytics? Sometimes yes. For example, they have to do grading, right? Grading is, you put the data into a spreadsheet and then you're kind of monitoring like who's failing, who's doing really well, who maybe needs a little bit more.

well. Um, so you're not only [: 00:04:00

And that's at least, you know, two bullet points in my opinion, on your resume. Um, you could put the word Excel. You could put the word Google sheets. You could put the word analyze. And the more that you have those different keywords on your resume, the better. So you're not starting from zero. I don't care who you are, I don't care where you're at in the world.

You have some sort of data analytics experience. So congrats. If you didn't know. Now you do. Myth number three is that you need to learn all data skills. And the truth is you don't. There's so many people in their career who never touch Python, who don't even touch sql, who don't even touch Power bi. Like you'd be honestly so surprised at how many data analysts go their whole career just using Excel, and you can make it pretty far.

influencers and creators and [: 00:05:00

And I promise you they don't admit this, but they don't know Python. How do I know that? Because they've come to me with a coaching call before and they've told me, uh, I've been at certain events with them where we were all coding in Python and they didn't participate for some reason. So you'd be surprised, like there's a lot of people out there who don't know things like Python, and if they don't know Python.

And they're really successful. That should be like a really good sign to you that you don't need to know Python. So like what skills you need to learn. I've made so many videos about that, so many episodes about that. I'm not gonna talk about it here. Although if you have to know Excel, tableau and SQL are the places to start, in my opinion.

But my point here is like. You don't have to know, like r you don't have to know Python. Uh, if you just wanted to do Tableau, you could have a really great data career just with Tableau. Uh, there's lots of people that do that, that make a lot of money just being really ridiculously good at Tableau. And that's okay.

, with three things. You can [: 00:06:00

And the truth is to be a data analyst, there's not really a degree and there's not really a certificate. Like for instance, my wife is a nurse. You definitely need to be, uh, like cert certified to be a nurse, right? You don't have to be certified to be a data analyst in, in the least. There's no like certifications really even for it.

And up to like, I don't know, maybe a few years ago, like three years ago, the number of bachelor's degrees for data analytics was really low number of. Master's degrees was maybe a little bit more, but like you guys, this stuff is pretty new. Um, and data engineering is even newer and there's even less like bachelor's degrees and master's degrees, so you don't need a degree or a certificate.

, without having to go get a [: 00:07:00

The Google data analytics certificate, the IBM certificate, whatever other meta meta data camp certificates. Even my program, I run a bootcamp called the Data Analytics Accelerator. I give all my people a certificate at the end. They kind of mean nothing. I'm being honest, like they literally don't mean anything.

They're fun, they're cool. They, they might make you feel good, but to an employer, they don't mean a lot. Do they mean a little? Honestly, no. Maybe a little bit, but not even that much. So if you're really foc focused on getting certified or getting some sort of a degree, maybe rethink your, your method because it's, it's a myth.

You don't need one. Myth number five is that data, jobs in general, uh, are easy to land, and the truth is they're not easy to land. Um, is it that oversaturated? I don't think so, but it depends on your definition of oversaturated. Someone said, Hey, data analytics, it's so oversaturated. I have applied for 20 jobs and I haven't heard back once.

of oversaturated, then, then [: 00:08:00

And that's a myth in this economy. Unfortunately, it's probably going to take triple digits to land a job. And I hate to say that I wish that I could tell you that it's easy, but it's really not. There's certain things that you can do to increase your odds. Um, of actually landing a role, but just know that that's kind of true for any role right now in this economy.

Uh, I, there's something called the, uh, interview ratio, which is basically like how many applications do you have to send to get, uh, an interview and for every 100 applications you send. Right now, the average interview rate is like four, so it's 4%. So if you're not getting any callbacks after 20 applications, you're normal.

really know that something's [: 00:09:00

Data. Myth number six is that data jobs are remote friendly. And this one's kind of tricky because they are remote friendly. They do really lend well to remote careers.

But I guess the actual myth is, is that there's lots of remote data jobs and the truth is there's just not in comparison to hybrid roles, uh, or in-person roles, remote rephrase. Remote roles only make about 16% of all data jobs, , meaning the remaining 84% are either hybrid or in person.

t is, uh, there's just a big [: 00:10:00

Uh, that doesn't mean that there isn't remote jobs. There definitely is. Uh, but there's not as many as you may think. And if you're only applying for remote jobs, just think about it that you're limiting yourself to literally only 15% of the available jobs. And then those are probably the most popular jobs too.

So you're making your life extra hard 'cause you can't apply to a bunch of jobs. And the ones you are applying for have a bajillion applicants. So if you've only been applying to remote jobs, I suggest switching to. Do a mix of hybrid and in person as well. Hybrid can be really cool. Hybrid can be like you're in the office, you know, four days a week and you work from home one day a week.

hybrid roles because you're [: 00:11:00

So I'd focus on hybrid roles if I was you. Myth number seven is that AI is going to take your job as a data professional. And I just don't think that's the case. Uh, is AI good? Yes. And something I need to actually come clean in this episode and on my podcast is I have done a lot of sponsorships and brand deals with AI tools telling you how awesome they are.

And they are awesome. They're very cool. And I try to paint them in the best light possible. But one thing I should tell you is there's days I get very frustrated with them. There's days where they don't work. There's days where they do the wrong thing, they make data up, they, they do the wrong calculation, they don't actually do what they're supposed to do, and.

data analysts if you're even [: 00:12:00

I liken it to like a pilot. A pilot is someone who's flying the plane. Right. But to my understanding, and correct me in the wrong rephrase. And correct me if I'm wrong in the comments, but I don't think pilots do a ton of the actual flying right now in these days. Like I think the plane does a lot of it on its own.

Now, of course, the pilot's there and does like the more complex parts of it, right? And they're there if something goes wrong. But to my understanding, the plane kind of does a lot of the work itself, the computers inside the plane. But that doesn't mean that. Pilots are going away. In fact, there's two pilots still on every flight.

Right? Even though the plane's theoretically flying itself, the same is true also for like robotic surgeries. Like you still have doctors there doing the hard parts, watching, making sure everything's okay. I honestly think that's where the furthest that a data analyst role would go is it's like, okay, I'm watching it do its thing.

anually. That's where I see. [: 00:13:00

personally. That's where I see AI plus data jobs going in the future. I don't think it's taking your job at all. I think you're fine.

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