159: Is The Google Data Analytics Certificate ACTUALLY Worth It? (FULL REVIEW)

The Google Data Analytics Course Isn’t Enough

[00:00:00] Avery: If you're thinking about doing the Google data Analytics certificate, let me save you six months of your life. Don't. Here are the five brutal reasons why it's a total waste of your time and what to do instead. Number one, certificates don't matter. Let's just get this out of the way. You're not going to get hired because of any certificate, and honestly, certificates carry little to no value in the data industry, period.

[00:00:23] Avery: Now people clinging to these because it kind of feels safe. It's like I can't get rejected if I'm not a. Yet. I'll just get the certificate first and then I'll be ready to get a job. But that's a lie. They clinging to it because it feels like what they're used to. It feels like school. It feels productive.

[00:00:38] Avery: Well, here's the problem, despite what you might hear online, data analytics isn't like the nursing industry or becoming a lawyer. You don't need a certification to become a data analyst, and there isn't some like magically agreed upon certificate in the industry that everyone likes, that everyone trusts.

[00:00:55] Avery: In fact, to prove this, I actually studied 3000 data analysts, job [00:01:00] descriptions, and not even one of them. Mentioned having any sort of certificate. In fact, most people in industry, including myself, aren't actually certified. I have zero data analytics certifications. None zilch. But you're probably thinking like, come on, how is this possible?

[00:01:15] Avery: The Google data analytics certificate, it's talked about so much. Here on YouTube, on podcast, on LinkedIn. Like someone has to like it, right? Like hiring managers must love this, right? Well, if they loved it, wouldn't they mention it in the job descriptions? At least as like a nice to have outta those 3000 jobs descriptions that I analyzed, I couldn't even find one mention.

[00:01:35] Avery: Of the Google Data Analytics certificate in any of the job descriptions. And not only could I not find any mention of the Google Cert, I couldn't find mention of any other cert at all. Not the PL 300, not the IBM, not the meta, the data camp. None of 'em, if certifications mattered, I feel like they would be on job descriptions and they just aren't flat out.

[00:01:55] Avery: And to confirm this data, quest actually went out there and did interviews with data hiring managers and decision [00:02:00] makers. And produce 200 pages of transcripts, all about the data hiring process and data job requirements. And the number of times that the word certificate was mentioned in those 200 pages, you probably guessed it zero.

[00:02:12] Avery: And I mean, let's get real. Let's be honest here. Anyone can get these starts, right? Like it's not that hard. 2.5 million people have already started the data analytics cert from Google. And basically you just have to sit there for six months, watch some videos, answer some multiple choice questions, and boom, the certificate's yours.

[00:02:28] Avery: That's not gonna carry as much weight as solving real data analytics problems. Something like creating a project portfolio, and we'll talk about that more later. In this video, a certificate says, I watched videos and did multiple choice questions. A portfolio says I can solve problems. Which one would you hire A.

[00:02:45] Avery: Number two, it teaches the wrong skills. The Google Data Analytics course might look impressive on the surface, but under the hood, it's teaching you skills that aren't the most in demand skills. In this course, you'll mostly be learning spreadsheets, [00:03:00] sql, Tableau, and R. And now let me start by saying I like all these data tools.

[00:03:06] Avery: And each one of them has its own pros and cons and honestly place in the data sphere. But when you're first trying to break in and land your first data job, you need to be strategic. For example, if I told you that Onyx was a really good, powerful data tool, would you wanna learn it? Probably. What if I told you it was pretty hard to learn and really hard to use?

[00:03:25] Avery: Would you wanna learn it a little bit less? And then what if I told you that on top of that it was only required like 8% of the time on job descriptions? Would you spend months trying to learn it? Or instead, would you try to focus on more relevant data tools that are easier to learn and more in demand?

[00:03:39] Avery: You wanna focus on the data tools that are high in demand and are quick and easy to learn. Well, Onyx unfortunately, is not a real data tool, and in fact, it's actually a Pokemon right here. The R is exactly that scenario. It's a great data tool. I love it, but it's only required 8% of the time. And if you're just starting, it is a coding language, so it is a little bit hard to learn.

[00:03:59] Avery: And despite all those [00:04:00] facts and literally the data, Google is like, yeah, let's teach R. Let's go ahead and do it. For example, they could have taught Python instead. And that is listed over two times the amount on job descriptions than R. But to be honest, I don't even think that learning Python when you're just starting out is all that great of an idea.

[00:04:16] Avery: 'cause it's not nearly required as much as Excel, sql, and Tableau. And it's a lot harder to learn as well. So if it was me personally, I would just cut our and Python from the beginner course. Now let's talk about the spreadsheets aspect of this course. They teach you everything in Google Sheets for reference.

[00:04:32] Avery: Hardly anyone uses Google Sheets in industry. Like I'm talking about basically no one. I. Everyone and every company uses Excel. Now, go ahead. I can already tell that someone's gonna leave a comment saying, oh, my company uses Google Sheets and I believe you. That's probably true. My own personal company, data Crew Jumpstart uses Google Sheets a lot as well.

[00:04:50] Avery: Like I use Google Sheets a ton. In fact, the smaller the company, oftentimes the more you use Google Sheets. But the data is the data. And based on my web scraping and my analysis, Excel [00:05:00] is listed a hundred times more than Google Sheets on job descriptions. So you can go ahead and learn Google Sheets. But Excel is going to be used basically every single time.

[00:05:09] Avery: Now, most of what you learn in Google Sheets is applicable to Excel, but if you're going to be using Excel in the future, why not just learn Excel and use Excel now? Just like use the tool you're going to use. So I personally think let's get rid of Google Sheets. Let's just focus on Excel. Now, I do like their selection of Tableau for a data this tool, because it's the most in demand data, this tool here in the United States.

[00:05:31] Avery: And the other candidate would've been Power bi. Which is in fact a Microsoft product. And since this is a Google course and Microsoft and Google are like kind of rivals, they're not really going to ever teach Power bi. But my research shows that this is a good decision. Tableau is used 66% more than Power bi.

[00:05:48] Avery: So I think this is a good choice. And when it comes to sql, well SQL is sql. It's the gold standard of data tools, and they do teach SQL a lot in this. Course they teach it inside of BigQuery, which is basically [00:06:00] Google's online cloud version of sql, which isn't the most popular flavor of SQL at all. So I do think starting with like some sort of cloud version is a good idea.

[00:06:08] Avery: But I will say that I personally find BigQuery not intuitive at all, and it's super hard to set up as well. So I don't love the BigQuery, but we'll let it slide. It's fine. I personally think teaching Excel, SQL and MySQL and then Tableau, and then cutting out the Python and the R. It's a great place to get started when you're getting started.

[00:06:26] Avery: That gives you a solid foundation of high in-demand skills that aren't that hard to learn. Now that we've covered what they teach, let's talk about how they teach, which brings me to number three. It's slow. A lot of theory and not really that hands-on. Alright, it's slow. Just based off of what it says on the website.

[00:06:45] Avery: They claim that it takes six months at 10 hours per week. Woof. This lends to 258 hours of total studying, and that's a freaking lot. That's just in learning. That doesn't really include any time of job hunting, job [00:07:00] applications, interviews, so on and so forth. Lending a data job is really hard. I'm not gonna take that away.

[00:07:05] Avery: And it takes time. It takes a lot of time. I don't wanna make it seem like it's easy and you can do it in two weeks. I think you can do it a lot faster than six months. I've had a lot of students go through my program and lab jobs in 50 days, 75 days, a hundred days, and this includes all the time of learning from scratch, the job hunting, to actually when they're starting their first day.

[00:07:23] Avery: Part of it, like I said earlier, is you're spending a lot of time in this course learning things that really aren't that important in the end, like R it doesn't really make that big of an impact. But the other thing is, is they just drag this stuff out, if I'm being honest, like they teach. Stuff so slowly and in such a, like a long formed manner.

[00:07:40] Avery: For example, on the second module of the course, which is called Process Data from dirty to clean, I mean even that title feels like really long to me, but in this section it takes two freaking hours. You start and then you two hours later, you finally get to the first. Hands on parts, the first hands on SQL part, and even then the hands-on part is very short.

[00:07:59] Avery: It [00:08:00] doesn't really go that deep at all, and it's kind of boring, and I'm not alone in this data. Content creator, Kelly Adams said something very similar on her blog about the SQL portion of this course. She said the downside of this course was it didn't give you enough practice for sql. I didn't feel like I had a good handle on SQL at all after this, and I was actually curious.

[00:08:17] Avery: I was like, okay, well, how much hands on, how much in depth. Does the cert give you? And it's kinda hard to quantify that, right? But I thought, well, let's just go ahead and count how many times you execute a SQL query in the course. How many times do you like press go on the SQL code? And the answer was 25 queries.

[00:08:33] Avery: Now that averages to about one query per week. 'cause we're about six months. Ends up being about 25 weeks. And for reference, just so you know, I have 40 queries in my 10 week program. That's almost four times the amount of hands-on learning. So I just think they could have included a lot more interactive stuff more often and earlier in the program.

[00:08:51] Avery: Now, a lot of people that are in my accelerator program took the Google data cert beforehand, and this is what they had to say about the cert. They called it quote, slow and [00:09:00] laborious and quote a mile wide in an inch deep. I felt like I didn't know enough about any single skill to be able to effectively use it.

[00:09:07] Avery: So let's go ahead and talk about that. Let's talk about what it takes to feel confidence that you can actually use Excel, that you can actually use sql, that you can use Tableau, but not only that, but to prove it and be confident to a hiring manager that you can use these skills as well. So that brings me to my fourth point, which is number four.

[00:09:23] Avery: There are no projects and no portfolios inside the Google Cert, which is that there is nearly no portfolios and no projects in the Google Cert. Here's a sobering fact. After finishing the Google Data Analytics certificate, many students can't complete a basic data project on their own. Wow. Why is that?

[00:09:42] Avery: Because the course never truly requires you to build something at all, especially from scratch. That would simulate a real analytics project from the real world. It's all theory guided exercises and multiple. Multiple choice quizzes, which by the way, there's no multiple choice quizzes in real life. There is [00:10:00] a lightweight capstone at the end, but it simply doesn't cut it.

[00:10:02] Avery: You don't even have to do the capstone project to get credit for the certification, which basically means it's 0% required. This made one reviewer on medium, straight up say you won't be able to make a single basic project even after learning everything in the course. Yikes. Ouch. Think about that. You've invested six months of your life, six months of your time into this program, and by the end, you have really nothing but some sort of PDF to show for it.

[00:10:30] Avery: No meaningful case study, no analysis presentation, no portfolio piece. So when employers or hiring managers or recruiters ask, Hey, do you have a portfolio or some sort of project experience? All you've got is this PDF with a certificate on it, and that's a massive problem. So why is having projects so important?

[00:10:48] Avery: Well, it's because, like I said earlier, in the real world, no one really cares about certificates in the data world. At least they do care if you can actually do the work. When you apply to entry-level data jobs, [00:11:00] you and hundreds of others might list that same Google search on your resume. But not everyone's going to have a portfolio.

[00:11:07] Avery: The only way to stand out in the a TS against your other candidates is to showcase actual projects. Maybe you analyzed a public data set to find insights, or you built a dashboard or you solved a business problem using data. The Google course should have been the perfect place to do that, but it doesn't really deliver.

[00:11:25] Avery: Now, of course, some people, their comments, they're gonna be like, well, what about the Capstone project? And okay, it exists, but it's frankly too guided and too cookie cutter. It doesn't actually result in a unique project that screams, Hey, you should hire me. I'm really talented. Literally, millions of people have done that same darn bike project over and over and over again.

[00:11:45] Avery: In the past, there was like a Titanic data set and a housing dataset. And a flower data set. And those are like the most popular data sets. But that bike data set, I'm telling you I've seen it a million times and there's an option to try to do a project on your own, but they like kind of just give you like a PDF and they're like, [00:12:00] okay, here you go.

[00:12:01] Avery: Make a project. And another portion they try to help you create a portfolio. And they're basically like, yeah, there's this thing called Tableau Public, and there's GitHub, and there's Squarespace and there's Kaggle. Try one of those. Okay, well how, teach me how, like what do I do? Like, how do I start, gimme step-by-step instructions.

[00:12:15] Avery: And I actually think it's hilarious because they mostly focus on building your portfolio in Kaggle, which once again, super funny because Kaggle is like 94% Python notebooks. In fact, they list two portfolio examples. All of their projects are in Kaggle, in Python notebooks. You didn't even touch Python in this program.

[00:12:32] Avery: They chose not to teach you Python in this program. Why are the portfolios they're teaching you all about Python? It makes no sense. So why are they pushing Kaggle? Well, it's a trend we've seen before in this video. It's because Google bought Kaggle in 2017. So it's a Google product. So they're trying to get you to use their own products, which I'll be to them.

[00:12:51] Avery: They can do whatever they want. But let me be clear, having a robust portfolio, in my opinion, is a non-negotiable. If you're switching into data analytics, you need at [00:13:00] least a few real world projects under your belts to prove that you can apply what you've actually learned. And the Google search, it leaves it up to you to go get that on your own.

[00:13:10] Avery: After six months of wasting your time. As Kelly Adams says in her review, it did not prepare the person to complete the Capstone project. Just with this course, it would be difficult to complete any project. It's an overwhelming first project, and a guided project would've been much better for this.

[00:13:27] Avery: Exactly. If you wanna do a project after the Google Cert. You're gonna have to go find data sets, wrangle, analyze them, create visuals, and put those projects on some portfolio. You figure out how to make up on your own, because this course is not going to teach you how to do that. And this is a huge flaw for career switchers who expect the search to provide some sort of project, right?

[00:13:45] Avery: If you don't build a project on your own, you're just gonna be stuck with that theoretical knowledge and no evidence that you can actually do what the job description requires. Which freaking sucks. In my opinion. This just makes the job hunting process so much harder. Projects are so important that [00:14:00] they're one third of the formula for getting hired.

[00:14:02] Avery: You may have heard of my SPN method before, which just says skills, projects and networking skills are important. It's only one third of the equation though. Projects are another one third, and that leaves the last one. Third, which is networking and career skills, which actually brings me to my next point.

[00:14:16] Avery: Number five, there is no career support or networking support at all. One of the most underrated parts of learning a new data career is having a support system. People like mentors, peers, a network to lean on, to ask for help with the Google data analytics search, you basically get absolutely none of that.

[00:14:33] Avery: It's a self-paced online course. You watch videos alone. You do those silly little quizzes alone, and you finish it all alone. There's no built in community. There's no cohort to speak to. There's no real teachers that you can actually talk to one-on-one. You know, maybe there's a forum, but let's be honest, that's not really going to be that productive.

[00:14:52] Avery: There's no instructor who actually knows you or knows your name, or actually who cares about your progress in the program? Who knows where you're at? And there's [00:15:00] certainly no mentorship or career coaching included. For a lot of folks, that means utter isolation. If you get stuck or confused. Good luck.

[00:15:07] Avery: You're literally scouring Stack Overflow or Reddit for help because the course itself isn't anywhere to support you in your journey. However you choose to learn data analytics, you'll wanna find someone who might guide you, who will actually help you not be on your own, because when there's no accountability or support, it's way too easy to get discouraged involved behind.

[00:15:26] Avery: I know that that's exactly what I would do. I know people who started the Google search and honestly just gave up after a little bit and never ended up transitioning data analytics. And I blame part of that due to no community. And no guidance. And no accountability. Now even, let's say you do push yourself and you finish, you've missed out on a huge factor of landing jobs, which is the end part of the SPN method.

[00:15:47] Avery: Which is networking. Think about it. When you take a college course or you enroll in a bootcamp or something, you get to meet instructors, you get to meet the teaching assistants, you get to meet the fellow students. Some of those connections, maybe they can refer you to jobs, or at [00:16:00] least they can just share advice, or heck, at least they can just say, Hey, I'm sorry that landing a job is hard and I'm in the same boat as you with the Google Cert, who's in your network now?

[00:16:08] Avery: Maybe a few strangers on Reddit. That's like the best that you could possibly hope for. The responsibility is a million percent on you to go out and network from scratch, from absolute zero. You're gonna go hit up LinkedIn if you even think of that and maybe find a community. But what community? Or can I go to a meetup?

[00:16:24] Avery: Or if you wanna find a mentor that's on your own time and your own diet and it's doable and you should do it, but the course isn't going to help you here. Contrast that with people who go through my bootcamp program. They can talk to me, they can talk to my other coaches. They can talk to each other. We have live office hours every week.

[00:16:39] Avery: We have accountability hours. We have all sorts of fun activities that we get to do together. They also get access to my network. If I know a company or recruiter is hiring, I can send my students resumes over to them and they can get a chance. And not only do they get access to my network, but we have a super large alumni network.

[00:16:54] Avery: We've had over 500 students go through this program, and I'm super proud to say that we have several companies that have hired more [00:17:00] than three of our students, which is super fun. 'cause now that these people have been there for like two or three years, they're becoming managers and now they're hiring our students, which it's just amazing.

[00:17:08] Avery: So that's a huge leg up that Google search certainly doesn't really give you at all. So here's the blunt truth. When you finish the Google Data Analytics course, you're just another name with a piece of paper. Heck, it's not even a piece of paper, right? It's just a PDF and no one's there vouching for you.

[00:17:23] Avery: You don't have an automatic network of data professionals backing you, and you'll need to build your community and find mentors from scratch on your own. For many career switchers, that's too intimidating and too difficult to even get started. On top of that, they do have some resume content. Uh, they gave you access to these two resume templates, which are sadly so unbelievably basic, like I can't believe how basic they, these are.

[00:17:44] Avery: They're so crazy. Like, I don't even know how you're supposed to fit anything on this green resume and on this blue resume. Funny enough, and actually not funny, it's kind of sad. It isn't even a TS compliant because it has two columns. And I just think it's so funny because if you Google, you guys can [00:18:00] try this, go Google, like a TS two column resume and Google's AI product will actually say, Hey, if you have two columns in your resume, it's not a TS compliant.

[00:18:09] Avery: But then you have Google giving you a non a TS compliant resume. So do they want you to get a data job? Do they not realize that? I don't know. In my program, we give you 10 resume templates to choose from. They're all a TS friendly, and we've actually already collected 150 plus student resumes to browse from as well and to choose from.

[00:18:26] Avery: So that alone can save you hours because you're gonna have more resume options. They're all gonna be a TS friendly and you can get inspired by 150 different people. Now in the Google cer, they never talk about applying for jobs or job hunting. So applying, interviewing and negotiating that stuff, you're left to on your own, and that alone can take weeks.

[00:18:43] Avery: Like that is a very difficult thing to do. So the Google data Alex certificate is an okay place to start. It is that it is a start, but if you want to do it faster, quicker, more efficiently, and more fun, this is what you should do step. You should watch this episode right here, or if you're listening, you can [00:19:00] find it in the show notes down below, and that will tell you step by step exactly what's to do to land your first data job following the SPN method, which is the fastest and easiest way to land a data job.