173: 13 Signs You’re on the Right Track to Landing Your First Data Job

13 Signs You’re on the Right Track to Landing Your First Data Job

[00:00:00] Avery Smith: Feeling behind on your data journey. Don't worry. If you're doing even a few of these things, you're actually ahead. Number one, you can analyze data in Excel without panic. And what I mean by that is you can open up a data set inside of Excel and do something useful with it. Whether that's create. New columns, use formulas to, you know, clean or analyze any sort of existing columns inside of your data set.

[00:00:22] Avery Smith: Uh, but most importantly, there are two things you need to be able to do inside of Excel. Number one is create some sort of a V lookup or X lookup system that allows you to input something like an ID or a business name and output some sort of key value, like their phone number or something. And you need to be able to create a pivot chart if you don't know what a pivot chart is.

[00:00:41] Avery Smith: It's basically a way to analyze your data with aggregations. Things like max Min average sums off of categories. So if you can do those two things in Excel, you're in a good place. Number two, you need to know how to write basic SQL queries. And what I mean by that is you need to be able to use, select from and wear at [00:01:00] a minimum.

[00:01:00] Avery Smith: If you're not comfortable with those three things, you're probably not ready to land a job yet. If you really wanna see if you're ready, if you can answer the question, what's the difference between a wear clause and a having clause, and answer that quite comfortably. You're in an excellent place and you're probably ready to land interviews.

[00:01:17] Avery Smith: The third sign that you are on the right track is that you can build a bar chart and a scatterplot in Tableau or Power bi. Now you can do either or. You don't have to be able to do both, to be honest. Once you've learned one, you'll be able to learn the other quite easily. And I'm not gonna get into the whole debate of which one's actually better versus the other.

[00:01:35] Avery Smith: But the point here is can you create charts on the fly in one of those BI platforms? Those are the two biggest data visualization tools that you'll be using in your data career. And a bar chart and a scatterplot, in my opinion, are the most common charts. You could probably add line chart in there as well for a bonus, but can you build all.

[00:01:51] Avery Smith: Two slash three of those charts with ease and be able to know when to use those charts. If you can, you're on the right track. Alright? The fourth [00:02:00] sign is you can Google or Chachi PT your way through any error. And what I mean by that is you're going to experience so many errors in your data career, like.

[00:02:10] Avery Smith: To be honest, I spend a lot of my time debugging my own code, especially now with ai, right? When we're not even writing half the code, AI's writing it for us. Your debugging skills become very important, and so a lot of analysts who are on the wrong track will run into an air and they'll be like, oh, I ran into an air like this sucks.

[00:02:27] Avery Smith: My life's over. I can't figure it out. Oh, well, I'm gonna give up. Ah, that's not you. If you're on the right track, if that happens to you and you're on the right track, you can Google like we used to in the olden days, or you can chat CPT or Claude or whatever your AI model of choice is, you're way out of that air.

[00:02:40] Avery Smith: Figure it out. If you hit errors and you get past the errors, you are on the right track and you're going in the right direction. Sign number five is something that you can actually test right now, and that is you can send me a portfolio project right here, right now. In fact, I encourage you to do that in the comments down below just as a test that you can actually do this.

[00:02:58] Avery Smith: A portfolio project is [00:03:00] really important because why are hiring managers and recruiters supposed to just trust that your resume is telling the truth? Can you actually give them any evidence that you can analyze data that, that you can use Excel, that you can do those SQL queries, that you can create those charts in Tableau that we talked about earlier.

[00:03:14] Avery Smith: The best way to do that is have some sort of a portfolio project. You wanna make it as easy as possible for the recipient of SUD project to open it and view it. So that's why in my data analytics bootcamp, the data analytics accelerator, we publish these online on LinkedIn. Or on our custom portfolio websites that we build on card.

[00:03:32] Avery Smith: If you can't send me a link right now, you don't have a project. And if it's not easy for me to open, you don't have a project. You want to have projects in order to land jobs, they will help you get hired, I promise. So if you can send me a project. Post it in the comments down below. Sign number six is you're actually talking about your data journey with your family and friends regularly.

[00:03:51] Avery Smith: And I know that kind of sounds basic right, but if you start talking about it more often, one, it means you're more confident and you're more comfortable. The more confident and the more [00:04:00] comfortable you are in your journey, to be honest, the better you're going to do If you feel kind of unsure and squeamish, that's going to show everywhere.

[00:04:06] Avery Smith: That's gonna show on your resume, it's gonna show on your LinkedIn, it's gonna show on your job applications and your job interviews. So. The more confident you are in your whole journey, the better. And I feel like you gain that confidence by doing things like building portfolio projects. 'cause literally they're not only proof to the hiring manager recruiter, but they're also proof to you as well that look, oh yeah, I did analyze all this data in Excel.

[00:04:27] Avery Smith: Look, I am awesome. Also, when you're talking to your family and friends regularly about your journey, you're opening up doors because it's like a statistic that 70% of jobs are either recruited or referred for. Meaning the remaining 30% are the cold applications online that are not really fun, that kind of suck, that you spend most of your time doing, but don't actually lead to any results.

[00:04:48] Avery Smith: If you want quick results on landing a job, you need to talk to your friends and family. That means your neighbors, your church members, your pickleball, teammates. I don't know. Whoever you spend time with in real life, there is a [00:05:00] chance that they can help you land this job. Even if they don't work in data, even if they don't work in tech, they all work somewhere, right?

[00:05:06] Avery Smith: Most of 'em, unless you got really rich retired friends, then maybe they don't, they don't work, but then they're really rich and really connected and networked so they can help you too. So yeah, no matter who your friends, whether they're rich or poor, they can help you land a role at their company, but they can't unless you tell them you're looking for a role.

[00:05:22] Avery Smith: So that means when you're getting the mail and you see your neighbor. I mean, don't make it awkward and weird and be like, Hey, I'm trying to land the data job, but casually bring it up and like, oh, what'd you do this weekend? Oh, you know, I'm in Avery Smith's data Analyst Accelerator, so I spent a lot of time on this online course trying to learn sql.

[00:05:36] Avery Smith: Oh, okay. Great. Did you know that I work with the data team over here at at Alteryx and. I can get you like a little tour of the studio if you want. You know, those types of things. That is the type of serendipitous connections that happen when you start talking about your data journey with your family and friends.

[00:05:50] Avery Smith: Alright, sign number seven is you're actually applying to data jobs, not just watching and doing data tutorials. Here's the truth. You're not going to land a data job if you're [00:06:00] not applying for jobs, and unfortunately, in today's economy. You're gonna have to apply to a lot of jobs, especially if you're not doing step number six, which is like networking with friends and family.

[00:06:08] Avery Smith: If you're trying to do this without networking, you're gonna have to apply to hundreds of jobs. That's just the state of the market that we're in right now. But many of you guys watching this aren't actually applying to jobs at all because you're scared. And I get it. It's scary. It's like, why would I wanna apply to jobs when it's just a waste of my time?

[00:06:24] Avery Smith: I'm just gonna get rejected or ghosted, right? But here's the truth. You need to apply to jobs. You need to get rejected. You need to get ghosted in order to land the job. Even with my 10 years of experience, I've literally taught at MIT. I've worked with the Utah Jazz. I've been a data scientist for ExxonMobil, biotech startup, all this cool stuff that I've done in my career.

[00:06:40] Avery Smith: I'd still get well over 50% rejections if I was trying to land a new job. It's just the truth. That's the environment that we're in. Nothing's wrong with you. It's just how the market is today, and so don't take the rejections personally. In fact, you shouldn't worry until you get to a hundred applications sent out and you have zero interviews at that point.

[00:06:56] Avery Smith: We need to figure out what's going wrong. We'll talk about that. Step nine and step 10, [00:07:00] but if you can get to a hundred applications sent, you're in a good place. Number eight, you've joined the data community. And this one is kind of optional, but I don't really think so because everything's better in community.

[00:07:11] Avery Smith: It's a lot more fun to be doing this process with other people. And the number one data analyst killer these days is honestly burnout. People are working really hard to try to land this data job oftentimes in like the wrong ways. You know, just doing a bajillion tutorials, never applying to any sort of data jobs, or never building a portfolio or never networking at all, right?

[00:07:30] Avery Smith: And you can avoid these types of things if you join good data community. So it's highly suggested to join some sort of a data community with some sort of a mentor or a coach with peers around you that can help you stay on the straight and narrow on the right path to landing your data job. We have a community here at the Accelerator if you wanna check it out.

[00:07:45] Avery Smith: It's at data code jumps.com/daa. Okay, number nine. Your resume now includes lots of the right keywords. The truth is your resume is your number one weapon for landing interviews. It's [00:08:00] basically the thing that speaks for you. It is the representation of you and it's kind of a silly game that we have to play with the applicant tracking system or the a TS.

[00:08:08] Avery Smith: This system is wack, you guys. It is kind of wild, but here are two really easy things to know about the a TS that will help you get more interviews. They're both about having the right keywords, just two different type of keywords. Number one is everyone and their dog wants to hire people with experience already.

[00:08:25] Avery Smith: The chances of you having experience in the relevant fields if you're watching this video is probably low. If you wanna be data analysts, you probably don't have data analytics experience on your resume. And recruiters, the hiring managers, and more importantly, the a TS, they don't like that. So a number one way to trick the a TS into thinking you have lots of experience with.

[00:08:43] Avery Smith: You know, whatever you're trying to get into is by including those job titles on your resume, even if you've never had them. So for example, if you're trying to land a data analyst job, one really easy hack is to put the word data analyst next to your name on the top of your resume. ATSs are pretty stupid most of the time, [00:09:00] and they're just like trying to count, Hey, how many times does this person have the job title data analyst?

[00:09:04] Avery Smith: Anywhere on their resume? It'll just count and it'll be like, oh, they put it five times, even if you never even put it in your experience section. If there is a chance that you can get past a TS that way and it kind of tricks it, then you can talk to a hiring manager and a recruiter and explain, well, I've never been a data analyst, but I've done a lot of data analysis at my previous jobs.

[00:09:19] Avery Smith: 'cause here's the thing, a TS does understand nuance. You can't explain anything to a computer. If you can actually get your foot in the door by tricking the computer and talk to a human, you have a chance. And that's one of the trickiest things. The second keywords you need to have on your resume are going to be the skills that are listed in the job description.

[00:09:36] Avery Smith: So if a job description says you're going to be working with SQL and specifically Postgres. Then I would try to include that on my resume. I like to have a skill section on my resume, which just kinda shotguns a lot of the skills, but you'll also want to be using those skill names in your experience section, in your education section and your intro section.

[00:09:54] Avery Smith: Like the more you use those skills in different places in your resume. The better because once again, these applicant [00:10:00] tracking systems, they're kind of silly and they're just trying to count how many times you put the word Excel, how many times you put the word Python on your resume. So the more times you put the word data analyst and the more times you put SQL on your resume, the better.

[00:10:11] Avery Smith: To be honest, sign number 10 that you're on the right track is you've optimized your LinkedIn for data rolls and LinkedIn is very important. If you've been ignoring it, it's at your own peril. I owe so much of my success in my career to LinkedIn and specifically you'll wanna do something. It's quite similar to your LinkedIn that we talked about in sign number nine, which is your resume is full of keywords, so should your LinkedIn be full of keywords, you also have a really clear profile picture, a clear cover photo, a good about section, and make sure you have bullets in all of your experience section.

[00:10:43] Avery Smith: If you've done that, you're on the right trek. Sign number 11 that you're on the right. Hath is a recruiter reaches out to you on LinkedIn, and I know you might be thinking that doesn't happen in this day and age. What the heck? I've never even heard of that. But it happens. You guys, if your LinkedIn is up to date, if it's really optimized, [00:11:00] if your resume is really optimized, recruiters live on LinkedIn, like 98% of recruiters use LinkedIn.

[00:11:07] Avery Smith: A ton, like for like 20 plus hours a week, and they're trying to find people just like you. And if you've done nine in 10 accurately, if you have a portfolio, then there is a good chance that a recruiter is eventually going to reach out to you for an appropriate role. Now, when that happens, that is an excellent sign that you are really close to landing a data job, because that means it's probably going to happen again.

[00:11:27] Avery Smith: One good thing would be to pause, take a minute and try to figure out why this recruiter specifically reached out. To you, are they in your area? Are they local to you? Are they hiring for a role with kind of a unique background that you fill? For example, I got reached out to by a meta recruiter for a role called Optimization Scientist, and I used to be an optimization engineer, so that was a very specific title that I kind of used to have.

[00:11:49] Avery Smith: Try to figure out why the recruiter reached out to you and double down on that because when there's smoke, there's fire, and honestly, that's probably going to be your door to your next data job. The 12th sign that you're [00:12:00] on the right track is you've had at least one real interview. Once again, going back to, you're actually applying to data jobs, right?

[00:12:06] Avery Smith: If you've actually had an interview, that's good. Now that obviously is. Obvious, but my point here is if you can actually land an interview, it means your resume doesn't 100% totally suck. If you apply for a hundred data jobs and you get no interviews, your resume sucks and it's time to redo it. But if you've landed at least one, hopefully somewhere between one in 10 interviews, you're on the right track.

[00:12:28] Avery Smith: The industry standard for every 100 applications you send out is about four interviews, so it's not really high right now, but if you can at least get one interview, you can try to figure out what worked. Once again, was it that they're a local company? Was it that you have something on your resume that kind of resonated with them?

[00:12:46] Avery Smith: Was it your portfolio? Double down on that, and if you do, you're probably likely to get a second interview and a third interview, so on and so forth. The 13th sign is you're comfortable not knowing everything because here's the truth. If you want to wait until you know [00:13:00] everything about data analytics to apply into land data job, you're gonna be a bajillion years old before you even apply to a data job.

[00:13:06] Avery Smith: I don't know everything about data, and yet I'm making YouTube videos about it. I don't know everything about being a data engineer. And yeah, I was the instructor for the MIT Data Engineering Bootcamp. If you wait until you know everything, it's too late. So just become comfortable not knowing everything.

[00:13:20] Avery Smith: Be comfortable learning, and if you become comfortable learning, you know that you can learn anything no matter how technical or how difficult it may be. This is the attitude you need to have in the data analytics fields because everything is changing literally the last two years and the next two years, everything will probably change again.

[00:13:37] Avery Smith: So just be comfortable with your ability to learn.