0:02 Here's how I would become a data analyst
0:04 if I had to start all over again in
0:07 2025. Now, I'm lazy and I'm impatient.
0:09 So, this method I'm going to be
0:11 choosing, the SPN method, is the fastest
0:13 and it's the lowest amount of work to
0:15 actually land a data job. But, it still
0:17 is a lot of work. Step one is I'd
0:19 understand the different data roles
0:21 available in the data world. There are
0:23 so many different data roles and it's
0:25 not just data analysts. There are so
0:27 many other roles that are just like data
0:28 analysts but have slightly different
0:30 names and slightly different
0:31 responsibilities. For example, business
0:33 intelligence analyst, business
0:35 intelligence engineer, technical data
0:37 analyst, business analyst, healthcare
0:39 analyst, risk analyst, price analyst.
0:41 There are so many literally so many
0:42 different options that you could
0:44 possibly choose from. And they're all
0:46 pretty similar for the most part, but
0:48 some things are going to be slightly
0:49 different. So for example, a healthcare
0:50 analyst, you're going to be a data
0:52 analyst, but specializing in looking at
0:55 healthcare data. Financial analyst, same
0:57 thing. You'd be looking at financial
0:58 data. A BI analyst like a business
1:00 intelligence analyst and a data analyst
1:02 really a lot of the time are going to be
1:04 doing the exact same thing. So it's
1:05 important to be looking for all these
1:07 roles, understand what these roles do
1:09 and what their slight nuances are
1:10 because there's a chance that your
1:13 previous experience is actually valuable
1:15 and would help you get a leg up in
1:16 applying for these different jobs. So,
1:18 for example, if you have a business
1:19 degree and you're trying to transfer
1:21 into business analytics, becoming a
1:22 business analyst makes a lot of sense.
1:24 Or a financial analyst makes a lot of
1:26 sense. If you've worked previously as a
1:29 nurse or like a CNA, maybe you become a
1:31 healthcare analyst. Whatever you've done
1:33 previously, there's probably a good
1:35 chance that that experience is valuable
1:37 in the data world to a specific role.
1:39 So, even like I have a lot of truck
1:40 drivers in my boot camp. Those truck
1:42 drivers can be logistics analysts. They
1:44 can be operations analysts. They can be
1:46 supply chain analysts because their
1:48 previous experience is actually
1:49 valuable. The second thing that I would
1:52 do is figure out what is actually
1:54 required. Because here's the truth.
1:56 There is actually thousands of data
1:59 skills and tools and programming
2:01 languages out there. But if you try to
2:03 master all of them, you're going to be
2:06 like 150 before you feel prepared to
2:07 start applying to jobs. You're going to
2:10 be dead. is impossible to learn and it's
2:12 impossible to master all the different
2:14 data tools and skills and languages. So
2:17 by default have to choose a few. Now you
2:18 have a decision to make is which ones do
2:22 you choose? And I like I said I am lazy
2:23 and I want to do the least amount of
2:25 work possible. So I believe in the
2:28 lowhanging best tasting fruit analogy.
2:29 If you can imagine that there's a tree
2:30 that has some sort of like a peach or an
2:32 apple on it, right? The easiest fruit to
2:34 grab is always going to be the closest.
2:36 So it's the lowest hanging fruit. But
2:37 not only do you want the lowest hanging
2:39 fruit, you want the tastiest fruit,
2:41 right? So this is stuff that is not only
2:44 easy to learn, but is extremely useful.
2:45 Those are the things you want to focus
2:47 on. Out of the thousands of data skills,
2:49 those are the ones you'll want to focus
2:50 on. You can do the research on your own
2:52 if you'd like by looking at job
2:53 descriptions and writing down what is
2:55 actually required. But that's a lot of
2:56 work. And you can dig it from someone
2:58 like me who's been in this space for
3:00 about a decade now. Looked at literally
3:02 thousands of job descriptions. I even
3:04 have my own data job board, find a data
3:06 job.com, and I look at it all the time
3:08 to see what is being required. So, I've
3:10 done this research for you already, and
3:12 I will have a link to my conclusions in
3:13 the show notes down below. But
3:15 basically, what you need to know in
3:18 terms of lowhanging fruit, it's Excel,
3:20 Tableau, and SQL. That is it. Those are
3:22 the top three skills that you should be
3:23 learning as a data analyst when you're
3:25 just trying to get started. And if that
3:26 is too hard to remember, you can
3:28 remember every turtle swims, right?
3:31 That's easy. Excel, Tableau, and SQL.
3:33 That is where I would start, and I
3:34 wouldn't really veer off of that until
3:36 I've landed my first data job. Now, you
3:37 might have noticed that I didn't say
3:39 Python, and that might come as a
3:41 surprise to many of you because you hear
3:43 so much about Python and how cool it is
3:45 and how popular it is, and it is really
3:46 cool. It can do so many different
3:48 things. It's so powerful, and it's
3:50 actually my favorite data tool, but it's
3:54 actually only required on 30% of data
3:56 analyst roles. And it's really hard to
3:58 learn. It takes a long time to learn
4:01 Python because Python is hard, but also
4:02 all programming is hard. And if you
4:04 don't have a programming background,
4:05 it's going to take a long time to just
4:07 kind of even get your foot in the door
4:08 in the Python world and understand
4:10 what's going on. What's a variable?
4:12 What's a loop? What's a function? Those
4:14 types of things just they take time. And
4:16 so if you only need it for 30% of the
4:18 jobs, that means 70% of the jobs don't
4:20 require it. And once again, I'm all
4:21 about doing the least amount of work
4:23 possible and doing it as quickly as
4:25 possible. So, I say save Python for
4:27 after your first day of job because it's
4:28 really just not needed to land that
4:30 first one. Once again, I have a free
4:32 video that kind of explains what skills
4:34 you should learn and in what order and
4:35 why. I'll have that in the show notes
4:37 down below. The third thing that I would
4:39 do if I was trying to become a data
4:41 analyst is try to figure out how I'm
4:43 going to convince a hiring manager or
4:45 recruiter to hire me even though I have
4:48 no prior experience. There's this thing
4:49 called the cycle of doom, which
4:52 basically says, I can't land a data job
4:54 because I don't have experience because
4:56 I can't land a data job. And it's this
4:57 never- ending cycle of, well, you're
4:59 never going to get a job unless you have
5:00 experience. You can never get experience
5:02 unless you get a job. It's kind of like
5:04 the chicken or the egg. You know, what
5:06 comes first? Uh, so you have to figure
5:07 out how am I going to beat the cycle of
5:10 doom and how am I going to convince
5:12 someone that yeah, I am a data analyst
5:13 and you should hire me. How would I do
5:15 it personally? I'd build projects.
5:18 Projects are a great way that you can
5:20 demonstrate your skills. It's basically
5:22 the tangible evidence for people to know
5:24 that you can do what your resume says
5:26 you can do. If you're unfamiliar with
5:28 projects, it's like almost doing pretend
5:30 work where you're pretending that you're
5:31 working for a certain company. You take
5:33 a data set and you analyze it and
5:35 publish your results. We'll talk about
5:37 where to publish them here in a second,
5:40 but basically it's allowing you to learn
5:41 with realistic data with realistic
5:43 problems. But also, you're creating some
5:45 sort of evidence, like literally
5:47 physical evidence that you can show to
5:48 hiring managers, recruiters and be like,
5:50 "Hey, look, I can do these things. I can
5:52 be a data analyst. I can use Excel. I
5:54 can use SQL. I can create a data
5:56 visualization in Tableau." Once I
5:57 understand those three things, the
5:59 fourth thing that I would personally do
6:00 is start learning. And I want to
6:02 emphasize this is not the first thing.
6:03 This is not the second thing. This is
6:04 not the third thing. That's the fourth
6:06 thing that I would do is start learning.
6:07 And I would start learning Excel,
6:10 Tableau, SQL, Every Turtle Swims, right?
6:12 And I would do that by building projects
6:14 because I think building projects is the
6:16 most realistic way to learn. I also
6:18 think it's the funnest way to learn
6:20 because just doing like pointless
6:23 exercises on like these like interactive
6:25 online learning things. It's just not
6:26 realistic. Like in real life, you're
6:27 going to be having real data sets.
6:29 You're not going to be in some like
6:30 controlled environment. you're actually
6:32 going to have to be analyzing real data
6:34 that's messy, that has issues, that has
6:35 flaws, and you have to figure it out.
6:37 And so building projects is the best way
6:39 to learn because you're also creating
6:40 this tangible evidence that you're going
6:41 to be able to show to hiring managers
6:42 and recruiters. You might be thinking,
6:44 well, where do I get started? Well, you
6:46 need to figure out where you can find
6:47 data sets. You have to have a good data
6:48 set. I just did an episode on this
6:50 recently, and I'll have the link in the
6:51 show notes down below, but the simple
6:53 answer, the one-word answer is Kaggle.
6:55 Kaggle is the best place to find a data
6:56 set. It's not the only place and there's
6:58 other great resources, but if you're
6:59 only looking for one, Kaggle's usually
7:01 the place I would go and I'd personally
7:03 build projects based off of what you
7:05 want to do ultimately. So, go back to
7:07 step one and think about it like if you
7:08 have a business degree. Let's say you
7:10 want to become a business analyst. I
7:11 would try to build projects that are
7:13 relevant to to business analytics, maybe
7:16 data on sales or marketing or
7:18 operations, anything that's business
7:19 related. Those are the projects I would
7:21 try to seek out. Or if you're not sure,
7:22 like if you want to be a business
7:24 analyst or a healthcare analyst, or
7:25 maybe you don't even care, you'll just
7:26 take whatever you've got. I would
7:28 suggest doing projects on lots of
7:30 different industries. Maybe dip into
7:31 healthcare analytics. Maybe do some
7:34 people and HR analytics. Maybe do a
7:36 project on manufacturing and engineering
7:38 data. That way, you're getting exposed
7:40 to multiple different industries so you
7:41 can kind of figure out maybe what you're
7:43 interested in. You're creating a robust
7:45 portfolio that will be attractive to
7:47 every industry and multiple companies,
7:48 right? Because if you just focus on
7:50 creating, you know, business projects,
7:51 but let's say you want to become a
7:53 healthcare analyst, it's like, oh, those
7:54 projects don't really match up. So that
7:56 way you have a project for whatever role
7:57 you might be interested in. So that's
7:59 particularly what I suggest doing and
8:01 it's what we do inside of my boot camp,
8:03 the data analytics accelerator, is we
8:05 learn Excel, SQL, and Tableau, my
8:07 building projects, and we built multiple
8:08 projects in different industries. So
8:10 that way we're very robust as
8:11 candidates. The fifth thing I would do
8:13 if I was trying to become a data analyst
8:15 is create a home for my projects. And
8:16 this is actually what's called a
8:18 portfolio. You know, projects are
8:20 something that we do, but if you just do
8:21 them and you don't publish them and you
8:23 don't share them, they don't actually do
8:24 much good. You need to create a
8:26 portfolio to home these projects. And
8:27 the portfolio platform you'll hear the
8:29 most about is GitHub. And I have a
8:31 controversial take that I'm not a fan of
8:34 it. I don't think GitHub is meant to be
8:36 a portfolio. Now, that's me being a
8:38 little bit picky, but I just don't think
8:39 it's the best option if you're choosing
8:40 from scratch. What you need to do is
8:42 make sure that your readmmes are really
8:44 good because if you have a good readme
8:46 on your GitHub, then it can work. But if
8:47 you're starting from scratch, I
8:49 recommend doing something like LinkedIn
8:50 using the featured section or choose
8:53 GitHub pages which is from GitHub but
8:54 kind of a separate product and it's
8:56 their portfolio solution. It's actually
8:58 what GitHub recommends as a portfolio or
9:01 I really like card c a r d. It's just a
9:03 simple website builder be really great
9:05 options inside the accelerator my boot
9:06 camp. So any of those three would work
9:08 just fine. The sixth thing I would do is
9:10 make sure that my LinkedIn and resume
9:12 are up to date and optimized. And I
9:14 would do this early, even before I've
9:17 actually mastered Excel or I've, you
9:18 know, tackled Tableau. The earlier you
9:20 do this, the better because your
9:22 LinkedIn is your professional business
9:23 card to the world. One of the really
9:25 cool things is LinkedIn has a feature
9:26 called open to work. There's two
9:28 different settings on it. We can talk
9:29 about it later, but basically you can
9:31 have open to work for the entire world
9:33 or you can just have open to work for
9:34 recruiters. And either way, if you set
9:36 up your LinkedIn correctly, your
9:38 LinkedIn can start to work for you. and
9:40 instead of you going out and applying
9:41 for jobs, recruiters and hiring managers
9:43 are actually applying to you for
9:44 specific jobs. They'll reach out to you
9:45 and be like, "Hey, I think you're a good
9:47 fit for this job." So, having an
9:49 optimized LinkedIn is is really key. And
9:50 then, of course, having an optimized
9:52 resume is a must because once you start
9:55 applying for jobs, if your resume isn't
9:56 optimized, you're probably not going to
9:58 get many interviews. And the reason is
10:00 there's so many candidates trying to get
10:02 into data analytics roles, especially
10:04 the entry level ones, that recruiters
10:06 and hiring managers have to use what's
10:08 called the ATS, which is the applicant
10:09 tracking system. And basically, it's
10:12 it's computer, it's AI. It's actually
10:14 not even really that complicated. But
10:15 there's certain things you need to do on
10:17 your resume to have it be optimized and
10:19 ATS friendly so you can get past the
10:21 computer screening and actually have a
10:23 human being look at your resume. because
10:24 it's so frustrating when you get
10:26 rejection after rejection after
10:27 rejection that you don't even know if a
10:29 human's looking at your resume. A lot of
10:30 the times you're just getting rejected
10:32 by the ATS. And so you need to make sure
10:34 you have an optimized resume. So in
10:36 terms of having an optimized resume, it
10:37 would basically look like not having any
10:39 columns on your resume or any tables on
10:41 your resume and then using really
10:43 keywords that match the job descriptions
10:44 so that way you appear as a good
10:47 applicant to the ATS. The seventh step
10:50 that I would take is to start applying.
10:52 And I think this is obvious, but a lot
10:54 of people don't ever start applying for
10:56 jobs. And I get it because it's scary.
10:58 How do you know if you're ready to land
11:00 a data job? It's hard to know and you
11:01 probably will never feel ready. So, I
11:04 suggest just start applying anyways. And
11:05 when you start applying, don't only
11:08 apply on LinkedIn jobs. LinkedIn jobs is
11:09 where everyone applies and there's going
11:12 to be hundreds of candidates in a matter
11:14 of a few days on those platforms the
11:16 majority of the time because everyone's
11:17 doing that. So, you might want to try
11:19 something new like going to company
11:21 websites or checking out my job board,
11:23 find a data job.com, or some other
11:26 combination of other job websites. The
11:27 point here is you need to be looking in
11:29 multiple places and actually start
11:31 applying. I know it's scary, but just do
11:33 it scared. The next step I would do in
11:35 this process is I would really try to be
11:37 networking. And I I would try to be
11:39 networking the entire time, like even in
11:40 step one, but this is where it would fit
11:43 on today's road map is step eight. So,
11:45 it's way easier to get hired when you
11:47 know someone. In fact, my brother was
11:49 just recently looking for a job and
11:51 having a hard time and he ended up
11:52 getting an interview and landing that
11:55 job because his wife's friend works
11:57 there and like I can't tell you how
11:58 often that actually happens. So,
12:00 networking doesn't have to be hard. You
12:02 can do it on LinkedIn by posting and
12:03 commenting on LinkedIn. I think that's
12:05 really important to do, but I understand
12:07 that's hard and a scary step. One thing
12:09 that's really a lot easier is just to
12:10 talk to your friends and family. Just
12:12 say, "Hey, I'm trying to become a data
12:14 analyst. Do you know anyone who's a data
12:15 analyst? Does your company hire data
12:17 analysts?" And have a conversation.
12:18 You're not even really asking them
12:20 anything. You're just opening a
12:22 conversation. I know this is hard and I
12:23 know it's uncomfortable and I know it's
12:25 not fun. Like, it's much more fun to
12:27 learn data skills than it is to network,
12:29 but honestly, networking gets you the
12:31 same, if not better, results than
12:32 upskilling and actually learning new
12:34 data things. So, you can't be ignoring
12:36 this. Couldn't be ignoring this. I have
12:37 to be networking no matter how hard it
12:40 is. Now, if all is going well and I'm
12:41 doing all the previous eight things that
12:43 I've talked about, I think at this point
12:45 I'd probably start to land interviews.
12:47 There's two parts to an interview. The
12:48 technical and the behavioral. The
12:50 technical interview is when they're
12:51 going to be asking you questions about
12:53 data skills. It might be Excel questions
12:56 or data visualization questions or
12:58 oftentimes SQL questions and they'll ask
13:00 you to write certain SQL queries. This
13:02 can be really scary and intimidating and
13:04 honestly they can be really hard. The
13:06 cool part is they don't always occur or
13:08 or if they occur, they occur very
13:10 easily. Sometimes they're very hard,
13:11 sometimes they're very easy. It really
13:12 just depends. And to prepare for the
13:14 technical resources, there's a lot of
13:16 things that I could do. There's a lot of
13:17 resources out there that would help me
13:19 prepare. There's something called Strata
13:20 Scratch that I'll have a link in the
13:22 show notes down below that you guys can
13:24 check. There's data lemur. There's a
13:25 bunch of tools that will help you
13:27 prepare for these technical interviews.
13:28 Behavioral interview is going to be more
13:30 like them trying to feel for who you are
13:32 and what you've done previously and like
13:35 how you would act as a human being, as
13:37 an employee. And that is a little bit
13:39 harder to prepare for because it's more
13:41 of like instead of answering technical
13:42 questions, it's answering like personal
13:44 questions. There's not a whole lot of
13:46 resources out there. One of the things
13:48 you would want to do is use the STAR
13:50 method. You want to answer every
13:51 question by saying this is the situation
13:53 I was in. This is the task I was given.
13:55 This was the action I took. and this is
13:57 the results that came from that action.
13:58 And if you answer using that method,
14:00 most of the time you'll be good. It can
14:02 be scary and there's not a whole lot of
14:03 resources out there for this. So, if you
14:05 want to check out one that I made, it's
14:07 called interview simulator.io and it
14:10 basically helps you practice these
14:11 questions where I'll ask you the
14:13 question via video and you will respond
14:16 via video and then we'll actually grade
14:17 your answer and tell you what you did
14:19 well and where you could improve. It's a
14:20 pretty cool software. I'll have a link
14:22 for that in the show notes down below as
14:24 well. Wow, lots of links in the show
14:25 notes. So, be sure to check those out.
14:27 So, those are the nine steps that I
14:29 would take if I had to start from
14:31 scratch and land a day job in 2025. And
14:33 remember, I'm lazy. I'm trying to do
14:35 this the easiest way possible. This is
14:37 what I call the SPN method. You need to
14:38 learn the right skills. Not all the
14:40 skills, but the right skills. You need
14:41 to build projects and put them on a
14:43 portfolio. That's the P part. And then
14:45 you need to be networking, updating your
14:46 LinkedIn, and updating your resume.
14:48 That's the M part. And it's the easiest
14:50 way to land a data job. Now, you can do
14:52 all this stuff that I told you on your
14:55 own, and you'd be 100% okay, but it's a
14:56 lot more fun to do it in community, and
14:58 it's a lot easier to do with a coach.
15:00 Once again, I'm all about doing it fast
15:02 and easy, and it's much easier to do
15:04 that with a given curriculum where you
15:06 don't have to be questioning, am I doing
15:07 this right? How do I actually do this?
15:09 So on and so forth. And so that's why I
15:10 created the data analytics accelerator
15:12 program, which is basically a 10-week
15:14 boot camp to help you land your first
15:15 data job. We'll go over all of these
15:18 nine steps hand by hand, step by step
15:20 together and make sure you're ready to
15:21 land that data job. If you want to check
15:24 that out, you can go to data
15:26 careerjumpstart.com/da. DAA standing for
15:28 data analytics accelerator. And of
15:29 course, I'll have a link to that in the
15:30 show notes down below. Let me know what
15:32 I missed and what questions you have.
15:33 I'll try to respond to everyone in the
15:35 comments down below if you're watching
15:37 on YouTube or on Spotify. And I wish you
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