156: I Built A Game That Simulates Your Data Career Journey

[00:00:00] Avery: So you control this episode with your keyboard. Now, for whatever reason, you're watching this episode because you want to break into data analytics. You've heard about how it's financially rewarding, how it's low stress, it's remote friendly, and just overall a great career, but you're not exactly sure how to get.

[00:00:16] Avery: Started in this episode, you'll play a choose your Own Adventure game that will allow you to play out different scenarios and allow you to see everything that would happen if you make certain choices. Now, some of you guys may be listening or you may be watching on your phone and may not have easy access to a keyboard.

[00:00:32] Avery: In that case, we've labeled all the choices that you will make in this. Episode in the show notes slash description down below, and you can just press on the choice that you'd like to make. When we come to a decision in this adventure, it's really important to be honest with yourself, so make decisions like you would in real life.

[00:00:48] Avery: Don't try to game the system otherwise this is not going to be as fun or impactful as it would be otherwise. With that, let's get started. Alright, level one time for your first decision. Now every data career starts [00:01:00] with a choice. What type of role do you want to start? And you'll need to choose between becoming a data analyst or a data scientist.

[00:01:08] Avery: Data scientists typically are more coding heavy. They work on more cutting edge projects, and they have an average salary of 125. Thousand dollars in the United States, and there's currently about 5,000 open roles in the US right now. According to LinkedIn, data analysts are usually more SQL or dashboard heavy, more business oriented, and have an average salary closer to $82,000 per year.

[00:01:29] Avery: And there's about 18,000 roles open in the US right now. Now this is a big decision. It affects what skills you focus on, how you approach your job search, and even how much your paycheck is going to be, but I know that you'll make the right choice. Press one for data scientist or press two for data analyst.

[00:01:45] Avery: And if you don't choose, we will have to choose for you. But I don't wanna do that. So make your choice.

[00:01:54] Avery: All right. You've chosen the data scientist path. Woo-hoo. This means you'll be working on more techie [00:02:00] projects and honestly getting paid the big bucks. Kaing. You also be working with more Python machine learning and deep statistics, which sounds really, really cool. Right. I loved it when I was a data scientist.

[00:02:11] Avery: It was a lot of fun. But wait. Hold on here. Must have a master's degree. Four plus years of experience must be experience with machine learning and deep learning master's degree needed. Ugh. Maybe we should have read some of these job descriptions before we chose data scientists. I mean, it turns out that landing a data scientist role might not be as simple as we thought by the looks of it.

[00:02:33] Avery: You might need to go back to school and pay a lot of money to get a master's degree. Plus mastering things like machine learning and deep learning is no joke at all. There's a lot of complex math and statistics involved in those. So, uh, maybe this isn't the right choice for us. And to be honest, we've maybe dabbled in coding a little bit once or twice, but we're no programming wizard.

[00:02:54] Avery: It's gonna take a lot of effort to learn all of that upfront before we can even like apply to jobs. So are you [00:03:00] sure this is the path for you? Maybe, if you don't mind, let's rewind and choose a different path that we could explore. Press one for data scientist or press two for data analyst. And if you don't choose, we will have to choose for you, but I don't wanna do that.

[00:03:15] Avery: So make your choice.

[00:03:20] Avery: All right. Smart choice. You have selected data analysts and data analysts are in demand, and honestly, you don't need to learn something super crazy like get a computer science degree or a master's degree to get started. In fact, there's a lot more interest for data science even though there is three times more data analyst roles open right now.

[00:03:40] Avery: So it's actually maybe less competitive than you think. Plus the requirements to get started aren't nearly as rigorous. Even just knowing Excel alone can help you get started with your first data job. And the cool thing is about this whole data analyst role is you can become a data analyst for the time being for now, and then you can become a data scientist down the road, or a data engineer [00:04:00] down the road.

[00:04:00] Avery: There's so many different options for you. This isn't you saying that you're going to be a data analyst. The rest of your career. It's just your entry into the data world. It's getting your foot in the data door. Data analyst is a great starting place and I'm really stoked that you made such a good choice.

[00:04:14] Avery: So congratulations. You have actually made it to level two now, but the choices are only going to get harder. Here is the next question. What skill should you start with? There's literally hundreds of different data skills you could be learning at any point. It's incredible. The data world is so vast.

[00:04:30] Avery: Let's just start with one to make things easy. So your next decision is going to be, which of these two popular data tools are you going to start with? Should you start with Python or sql? You've heard Python is quite popular and can really nearly do anything. You've heard a lot about it before on YouTube and podcasts, and you're pretty excited to learn more SQL or Standard Query language.

[00:04:51] Avery: Well, it's been around for a really long time. You've heard of it, of course, but you don't know necessarily a ton about it. So which is it? Which do you wanna start with? Select three [00:05:00] for Python. Or four for sql. Now this is a big decision because you'll have to spend some time learning these tools. Both are coding languages that we're not super familiar with.

[00:05:09] Avery: So this decision's really important 'cause it'll define how you work with data, what jobs you're qualified for, and honestly how fast you land your first data roll. So no pressure, but it's time to choose. Do you select three for Python or four for sql? If you don't choose, we'll have to choose for you. Woo.

[00:05:27] Avery: You chose Python, and Python is my personal favorite data tool. I absolutely love it. You can basically do anything you want with it from basic data manipulation to filtering and sorting data sets, using the Pandas Library to building machine learning models. Using PKI Learn, you can build websites like Instagram was originally built off of Python.

[00:05:47] Avery: It is insane, but fair warning, it comes with a really big learning curve. I'm talking. Really steep. It is quite expansive. Like you can do anything with Python, which makes it really cool, but also makes it kind of hard to use. So that's a little [00:06:00] scary. And don't forget, before you start, you really have to know a decent amount about programming.

[00:06:04] Avery: You have to know what a programming variable is, what a function is. Used in programming for what a for loop is and a while loop, and the difference between the two. One, you use, one versus the other. You'll have to know variable types, IDs and a bunch of other fundamental programming things. And don't even get me started on all the errors you're gonna get.

[00:06:22] Avery: Like get ready to Google and chat GPT basically constantly, every day, all day while you're learning. And by the way, Python is. Only listed on data analyst jobs, two out of every 10 jobs. Basically 80% of the time it's not going to be on the job description or required for data analysts to learn. So this is kind of a lot to learn from scratch for something that isn't used 80% of the time.

[00:06:44] Avery: So once again, your choice, but. Maybe we should go back and consider sql. Honestly, it's probably used double the amount that Python is as a data analyst, and it's probably double easy to learn to get started. So maybe we just quickly rewind and redo that choice. [00:07:00] Select three for Python or four for sql. If you don't choose, we'll have to choose four.

[00:07:05] Avery: You. All right, sql, good choice. SQL is honestly an absolute classic. It's been prominent the last 50 years. It is one of the most popular data languages that there is. It's basically omnipresent. In fact, it is the most data tool in all of the data roles. So data analysts use it, data scientists use it, and data engineers use it.

[00:07:26] Avery: Basically, everybody uses sql. So welcome to the Cool Kids Club, and the best part, while it's. Easier to learn than Python. There's less complex syntax, there's less debugging, nightmares, honestly, just clean structured queries, and it's not even really that hard to get started because there's only really like 20 commands that you need to learn to get started, and you can learn those in a few weeks.

[00:07:46] Avery: Those 20 commands will help you get ready for most entry level data jobs. So basically, for most entry level data jobs, if you know how to select data from a table, filter it with a aware statement, and then order it. That's an excellent start. You had some joins where you're able to combine [00:08:00] multiple tables together, and man, you're pretty much set to land that first JD job, so congratulations.

[00:08:04] Avery: I think you made a great choice. Alright, now that you've decided that you're going to be a data analyst and you're gonna focus on SQL to get started, you have officially reached level three. Congratulations. You've decided on your role and you've decided on your main tech stack. Now here's the next decision that you face.

[00:08:21] Avery: When should you apply for jobs? You can select, just focus on your tech skills and don't worry about job hunting quite yet. Or if you'd like, you can take a look at a few jobs and just see what's available, see what the job descriptions say, and kind of just browse around. So select five to continue learning sql or select six to look at some future jobs.

[00:08:41] Avery: And as the previous choices, this will obviously impact your career quite a bit. Are you gonna keep studying or do you wanna start looking at jobs? So once again, choose five to keep learning. Or six to start to look at jobs. And if you don't choose, we'll have to choose for you. And who knows what we're gonna choose.[00:09:00]

[00:09:00] Avery: All right. You selected five. You wanna keep learning and I get it. I don't blame you after all, you just started with all of this and it's very fresh and new, and there's a lot to learn and you don't feel ready yet. You wanna master the skills first and then worry about applying, and I totally get that mindset.

[00:09:17] Avery: It's very fair. But a fair warning, a lot of people get stuck in this phase and they never actually leave it. They never go and apply and they get stuck in this scary place that I call tutorial hell, which is you basically thinking just one more course and then I'll start to look for jobs as soon as I feel comfortable doing these types of things.

[00:09:35] Avery: Then I'll be ready to, you know, look at jobs. So you need to be really honest with yourself. Are you ever going to be ready? The truth is, data's really hard. You'll never even master a fraction of it. I sure haven't. It doesn't stop me though. And even if you did master a little bit of it, by the time you did something new would get released and you'd have to start learning that.

[00:09:54] Avery: And that process will just continue and continue and continue, especially with how fast things are going in the time of [00:10:00] AI right now. The truth is, learning on the job is key for every single data role. So you might as well get used to that imposter syndrome that you're feeling and the idea of you don't know enough because you'll feel that way a lot in your career.

[00:10:11] Avery: And be honest. Are you actually learning for learning sakes or are you kind of just scared of looking at jobs? Are you kind of stalling? Is it a stalling technique? Are you taking one more course? 'cause it's fun and it makes you feel good and looking at job descriptions kind of makes you feel that imposter syndrome and kind of makes you feel not enough and makes you feel sad.

[00:10:27] Avery: If so, I get it. That's how I feel too. But if you actually wanna get a data job and not just learn the skills. What do you think? Should we take a sneak peek at some job postings or keep grinding? I'll let you make this choice. Let's rewind here. So once again, choose five to keep learning, or six to start to look at jobs.

[00:10:47] Avery: And if you don't choose, we'll have to choose for you. And who knows what we're gonna choose?

[00:10:55] Avery: Hey, awesome. Look at you. I am proud of you for being here and making [00:11:00] this choice. You, my friend, you are brave and you are getting ahead of the game by seeing what companies actually want, right? What are they actually looking for in candidates? And this is the smartest move because now you can actually tailor not only your resume, but tailor your learning based on real job descriptions.

[00:11:17] Avery: And let's be real. Right now we're just looking at job descriptions. They're not gonna bite, they're not gonna hurt us. We are just looking at these to learn more. We don't even have to apply currently in this very instant. We can look at some job descriptions and just see what they say so that we know what's going on in the market and what the market's asking of us.

[00:11:35] Avery: The more information we have here, the better because we're just more informed, and with more information comes more power. Alright, so you have come so far. In fact, you're actually at level four now. Woo. Congratulations. You've picked your path. You've learned some awesome tech skills. You've checked out some job listings, but now this is the hardest decision yet, and you have three choices.

[00:11:54] Avery: After looking at some job descriptions, do you move on and just keep looking? You maybe hit apply [00:12:00] or do you hit apply and try to network with the recruiter or the hiring manager, and let's be real here. This job. It's kind of a stretch for you, so maybe you should just move on. But it's also kind of a cool role, right?

[00:12:10] Avery: I mean, and you've heard networking is a good idea, but you don't really know where to start. So maybe we should just apply and move on. I don't know. This is a tough choice, and if you feel like it is a tough choice too, you're not alone because this is where most people get stuck. They overthink, they hesitate, and before they know it, months have passed by without them really making any progress or without them even noticing.

[00:12:32] Avery: It is so easy to do and I've literally seen it happen to so many people. So which is it? Are you gonna click seven and move on from this job? Are you gonna hit eight and apply to this job? Or are you gonna hit nine? Apply to this job and try some networking. So go ahead, hit one. Don't let this choice take weeks.

[00:12:48] Avery: If you don't choose, we'll choose for you. All right. You chose seven. And look, I get it, that job description was a bit scary and you didn't reach a hundred percent of the requirements at [00:13:00] all. So you decided to wait and you told yourself, I'll apply when I'm ready, because that's when it'll be best. But once again, ask yourself at what point.

[00:13:09] Avery: Are you going to feel ready and be honest? Are you kind of just procrastinating here and putting off the inevitable? You're gonna have to apply eventually. You might be getting stuck in imposter syndrome. I'll be honest and clear with you. There's no such thing as being ready to apply for a data job. The longer you wait, the harder it gets.

[00:13:27] Avery: It's kind of like standing at the edge of a frozen cold lake and you're waiting to jump in on the dock. It never really gets easier. The water doesn't get warmer. You can keep learning while you apply. It's not like you're saying that I'm never going to learn again. It's just I'm going to start applying while learning.

[00:13:42] Avery: Plus one thing I can tell you with a hundred percent confidence. If you never apply to a job, you'll never land a job. So let's be a little bit more bold here and rewind to make that choice over one more time. So which is it? Are you gonna click seven and move on from this job? Are you gonna hit eight and apply [00:14:00] to this job, or are you gonna hit nine?

[00:14:01] Avery: Apply to this job and try some networking. So go ahead, hit one. Don't let this choice take weeks. If you don't choose, we'll choose for you. All right. You chose eights. I'm proud of you. 'cause that took some chutzpah to apply to that job. And you know what? That should be celebrated, especially if that's the first job you've ever applied for.

[00:14:21] Avery: Like seriously, this is me clapping for you right now, but not too loud that it's in the microphone. I'm super proud of you. Because you are in a precarious situation. You can be proud of yourself. You can, you applied to a job and you can let that one job be the one job you apply for this month, and you can go crawl back into your shell, into your peaceful comfort zone.

[00:14:40] Avery: That's an option. I'm not gonna take that away from you. But you could also start applying to another job and then another job because hey, this is kind of invigorating. You hit apply, and then you hit apply again, and then another job, and then another job. And soon enough, you're doing what I call the spray and pray method, which is basically you're shooting your resume out as fast as you can, [00:15:00] hours later, you've sense.

[00:15:01] Avery: Like, I don't know, hundreds of applications. And where have they gone into the black hole void of the automatic tracking system? Hey, I applaud the effort and the enthusiasm and the confidence, but here's the thing. Applying coldly and blindly online is kinda like throwing darts in the dark. I. You might hit something eventually, but it's probably gonna take a long time and a lot of frustration.

[00:15:23] Avery: And in fact, you're actually 10 times more likely to have success to actually land an interview when you can skip the automatic tracking system altogether and talk to a human being. This is called networking. So way to be brave, but maybe we can even be a little more brave. So let's rewind again one more time and let's apply and try some networking this time.

[00:15:43] Avery: All right, here we go. So, which is it? Are you gonna click seven and move on from this job? Are you gonna hit eight and apply to this job, or are you gonna hit nine? Apply to this job and try some networking. So go ahead, hit one. Don't let this choice take weeks. If you don't choose, [00:16:00] we'll choose for you.

[00:16:03] Avery: Booya, you chose networking, and I love this choice because it requires you to be double brave. You're being brave to one, apply to the job, but two, you're being brave to try something new in networking, even if you're not like a hundred percent sure how to get started with networking. And I think bravery will be rewarded with your time back instead of competing with literally hundreds of applicants online.

[00:16:27] Avery: You're skipping the queue and you're going straight to the decision makers, like the recruiter or the hiring manager, and this is how jobs actually get filled. Computers don't hire people. People hire people. Heck, a lot of jobs never even make it to the job boards because they get hired by referrals or something like that.

[00:16:43] Avery: I know networking feels weird. I. First, but this is how you get referrals. This is how you get insider information and real interviews. Now, of course, you're gonna get ignored. You're gonna get rejections in the networking. People are gonna say that they can't help you. But if you keep this bravery up, this consistency up long enough, [00:17:00] eventually you're going to get your answer.

[00:17:02] Avery: You're going to get something that says yes. I would love to interview you. Yes, you are hired. So I hope you enjoyed this game and found these scenarios helpful. If you made it this far, would you mind hitting subscribe? I make a great episode like this every single week. And if you'd like, you can press zero right now to play again.

[00:17:18] Avery: May the odds be ever in your favor.