Credit: Tina Huang
Hey, Guys, I'm Tina Huang So what we're going to cover today we're going to go through the website and go through a general overview of the certificate. I will quickly walk through the course mission advocate and I'll give you my thoughts on the content as well as the delivery.
Also be focusing really heavily on job readiness claim because I think this is what makes a certificate special is that it has a very bold claim, about the fact that you will be able to get a job just having a certificate and having no other experience outside of that.
So we're going to cover that and then I'll talk about why people benefit the most from the certificate. And finally, I have I'm going to have an entire section that's dedicated to how to actually maximize your chances of successfully completing this certificate because it is a pretty intensive process.
So we want to be able to maximize our chances of actually being able to complete everything I read all those benefits.
Google Data Analytics Professional Certificate Review, Overview
This is the Google Data Analytics professional certificate. And it claims here that this is your path and proves to data analytics, and you'll learn in-demand skills. So happy job-ready in less than six months. No developer experience is required in a very, very bold claim here. So we'll be evaluating that.
You get a free seven-day trial period, where you can just audit the course which is what I did in order to go through everything. We don't actually need a certification. You just want to look at the knowledge available. Okay, so what you'll learn in this course, you gain an immersive understanding of the practice's processes, just basically the everyday immersive understanding of what data analyst does on a day-to-day basis.
You also learn these key analytical skillsets which are tools, data cleaning, analysis, and then they go to spreadsheets SQL, R, I'll come back to this choice later. The choice of using R and have low finally, you know they're going to teach you about visualizations, dashboards, presentations, all extremely, extremely important skill set and how to clean data and complete analysis and calculations using a spreadsheet support arm. Okay, cool.
So yeah, I'm not going to go over so much over here because you guys can read it yourself as well. It just basically says there are lots of jobs that are available in the US and around the world as well. And the average salary is around $67,000.09 100. That sounds about crap. To me. This is the part of which I was so excited about.
It's a pie learning project. They have like they have a capstone project in the end, which we'll be going through as well, which I think is absolutely brilliant. It's the best way to learn and most certificates don't offer this. So it's very applied learning. There are eight different courses that are being covered here.
Google Data Analytics Professional Certificate Reviews + Course Breakdowns
And we're going to go through each of these pretty quickly and I'll just kind of give you my thoughts on how they all fit together and how I feel like they cover the bases of data, data analytics. So you have
- Foundations data everywhere
- Ask questions to make data-driven decisions.
- Prepare data for exploration
- Process data from dirty to clean.
- Analyze Data to Answer Questions
- Share Data Through the Art of Visualization
- Data Analysis with R Programming
- Google Data Analytics Capstone: Complete a Case Study
Total 8 Courses in this Professional Certificate
1. Foundations Data Everywhere
And so on. These are the foundations. And I think this course, is very it's like a general overview of what's happening. I think it does a pretty good job of introducing what data analytics is. I went through them personally like I've looked at a few of the videos myself, and I think they are very well. production.
So that's something that I really want to give Google credit for. They're very well produced and they're easy to digest, and they weren't kidding when they said that they're going literally from zero. They like literally explained to you what data analytics is.
If you know what data analytics is we are dabbled in it yourself. I don't think this question is very necessary for you. Truthfully, most of you guys who are going to be taking this course probably already know why you're going to be taking this course and what data analytics is.
So I would probably just like personally just take the quizzes to take the expedient version, but take the quizzes throughout and look at some of the gaps in your knowledge that potentially you might not be aware of where you think could be interesting and how to go through those. Also talk a little bit later about how my recommended approach is to like take notes and how to actually, like, retain that information later on.
2. Ask Questions to Make Data-Driven Decisions.
So the next one is the one that I'm actually really excited about. And I think I'm so happy that Google included this and this is the ask questions to make data-driven decisions. Okay. So let me tell you guys why I think this is such a great course.
So the reason for this is because I feel like there's such an emphasis nowadays on just like learning tools of the trade you know, like learning Oh, like up there in Python you learn or you learn, like statistics where you learn like spreadsheets have low and you just think that oh, if you do like lots of personal projects, and you know how to use these tools, that everything is fine, you know, then you're like, wow, like I must be really good at Data Analytics already.
And that's really not the case. Like I'm saying this from experience myself. I'm still an entry-level data scientist and I went in being pretty confident like I got this, you know, I got the technical stuff down. I know how to do SQL. I know how to do art. I know how to do Python. I know how these things. And really though what actually matters, in the end, isn't the technical stuff.
It's your ability to actually take business problems and ask the right questions, and be able to translate that into doing analyses that would then answer those questions and then communicate it back to the people. Right, you have to be able to communicate that back to the people who asked you those questions in the first place in order to drive impact with that. And I think this is a skill set that's so minimized like people don't really look into that at all and you're just kind of expected to figure it out when you get on the job.
And it took me like months and months. I'm still kind of figuring it out now because I never had that basis. So this is why I'm so excited that this course is there, you know, like that they actually have an entire course dedicated to asking questions to make data-driven decisions. So yeah, like and I look through each of these like week one, week two, week three, I think this is really great.
They actually teach you like a very systematic approach to figuring out how to ask the right questions, and how to use data to actually drive those decisions later. So out of this course, I love that this is on and I highly, highly recommend you like pay usually pay attention anyway. But paying extra attention to this section is really important once you actually get on the job.
3. Prepared data for exploration
The next one is prepared data for exploration. Again, I think this is really really important. You can't really do anything with data if your data is not even prepared for you to do that. Right,
So this was this is when I think they introduced like tools like spreadsheets and sequel to extract and make speaking to the right data for your objectives.
If you work in a larger company, most of your work is going to be done using SQL I want to say like yes, spreadsheets are useful when you're doing like, certain like models and things like that, um, as a data analyst,
But SQL is is very fundamental and for data scientists, you pretty much just need to focus on SQL like nobody's going to ask you questions about spreadsheets. Okay.
4. Process Data from Dirty to Clean
This is course number four is also one that I am really excited about process data from dirty to clean. They actually have an entire course dedicated to processing data, data cleaning, and this is again, it's like that business question kind of thing, right?
This is another skill set that's really neglected. When you just look at traditional certificates or programs, especially when you like. I know like what's really popular these days is something like cowboy gray and people just like do Kaggle competitions and they think like oh like this is how I this is how work is going to be and when in fact like How will covers like maybe like 20 to 30% of what data scientists and data analysts even do.
A lot of that time is spent on asking the right questions, communicating the right questions, and then processing your data. I like also like finding your data. That's another one finding your data and making a cleaning routine extract those insights that can be used in the business. So I'm really really glad that they included this.
5. Analyze Data to Answer Questions.
So moving on, analyze data to answer questions. This is really the meat of how to actually answer or answer their questions and have they like have that business question in hand and be able to analyze it using SQL or spreadsheets.
I really also like the way that they did so that they have a very systematic approach to how to answer these questions, which is very hypothesis-driven. And that's something again, I think it's really great that they're stressing out like that framework of how to approach these questions and answer them systematically. I believe they also have a section dedicated to making sure that your queries are actually correct.
And making sure that everything, you know, double-checking what you're doing, because another thing I've talked about a lot is you know when you're doing data science or data analytics you always get a result. You know, if you're doing like software engineering,
If something doesn't work, then you get a bug, right? Like you clearly know that it's not going to work. But, with data science and data analytics, a really big issue is that he can like screw something up pretty bad. And you wouldn't even know because they would still give you a result for it.
You still get an answer even though the answer is incorrect. So it's really really important to be able to develop a system of going through analysis to make sure that what you're doing is actually crack because there's nothing more embarrassing and also damaging than you doing like analysis you like you know making a presentation about it and showing it to people and then they're just like, like some like you either find an issue yourself or someone else finds an issue.
And then you have to like redo the presentation and go like I'm so sorry like this. This is like no raw, right? This is completely wrong. And it's also it's like bad for the firm, especially if you don't even catch your mistake. So I really like how they develop a system for you to actually go through these questions.
6. Share Data Through the Art of Visualization.
Movie for a stay on track. And then they are data visualization again, great they used to have low as a data scientist, I don't usually use data visualization tools.
I do things programmatically but as a data analyst, that is extremely important and I'm really glad that they are covering visualization and not just the tool but also how to hear how to form a compelling narrative through data stories. I think this one is I this is also something that's really important because you can get all these insights, right?
And if you just like, plop the data in someone's lap, or you send them an email, like literally, nobody cares. That's the funny thing. Right? You're like wow like this analysis is so amazing. I like spending so much time on you know, you're able to improve whatever like business things so much better. But you realize if you don't know how to tell a compelling narrative, through data stories, nobody's gonna even look at your analysis and they're not even gonna care.
7. Data Analysis with R Programming
So this is extremely, extremely important course number seven data analysis with R. Okay, so I knew this is gonna come at some point, we're gonna have to talk about their choice of using R.
So I use R myself, she started my programming journey using R. And that's mostly because I started off doing research and in research hours a lot more prevalent.
However, I quickly migrated using to Python because of the much better ml algorithms that were available, and also an industry. Python is going to be the predominant language here that you can there are people who I work with who kind of do our like once in a while because I know R is really convenient in terms of like visualizations, as well as manipulations and statistics.
So you can do that the majority of code is going to be written in Python because it integrates a lot better into all the other aspects of data science, data analytics, and then the software engineering stack. So it kind of all integrates together and things are just more or just there's better support for using Python.
So most companies in the industry will generally prefer that you use Python. However, what I want to say is I don't want to invalidate this course I'm not saying like you should, you should definitely not be like I'm not going to take this certificate, because teaching me are instead of Python, like that is not a good reason to not take this course.
What I have preferred to do in Python, yes. If you're doing an R, you know, that's okay. Right. You learn it, learn R it's completely it's a perfectly good language. And especially if you're going to research or anything like that like ours perfectly.
It's actually a preferred language. You're going to industry, you can just learn Python by yourself like once you know how to do are just learning Python. It's just another coding language, another scripting language. So it's really not that bad.
What I would recommend if I go through this class, and it's gonna teach you how to like do all these like data, data analysis, these manipulations, and then just go and learn them in Python.
There's similar not like a lot. The syntax might be like slightly different, but they're really similar enough and the approach to how to do these analyses is very, very similar. So yeah, that's my two cents. What do you guys think? What do you guys think about the teaching on our part versus Python? Okay, cool.
8. Google Data Analytics Capstone: Complete a Case Study.
I'm gonna move on to my favorite thing. That's so exciting to me. The most exciting part. It's course number eight, which is the Google Data Analytics Capstone. Complete a case study. Do you guys like this?
I'm so excited about this because there's a reason why most certificates and just programs in general, they don't give you a capstone project, and more if they do give you one, it's usually in boot camps where you have to pay a lot more money. Right here. It's $39.
And that's per month, we actually paid by the month so the faster that you finish it, the less that you pay, and the recommended number of months were six months. So you're paying like what, like 200 something dollars, if you actually do it in a recommended amount of time.
This is so cool as like you paying such a small amount of money, but they're actually going to support you and help you with a capstone project. And the reason why people don't usually do this is because capstone projects take a lot more resources on their project projects with people and there's going to be a lot more like questions to answer.
So the fact that they're including this is so great. And this is so good because you can take everything that you learned and actually I that to your own project. It's going to be unique and it's going to be a way of demonstrating those skills that you learn and build up that portfolio that shows like, hey, not only do I have the certificate here, I can show you guys all the skills I'm listening on my resume. These are the things that I can actually do and here is evidence to support that. Do I think that this is all you actually absolutely need to eventually land a job?
I'm a little skeptical about that. I think there are so many people who are going to be doing this certificate you can imagine there's going to be a lot of people it's really popular people everybody does a certificate and they all do this capstone project. How do you choose who stands out? So I think it's still important to build up a portfolio that includes this capstone project but also includes other areas as well to make you really stand out as a candidate.
But overall, this is amazing as a way to get yourself started. It's optional hate worse the project, but I highly highly, highly recommend that you do it. In fact, I feel like if you don't do it, you are really missing out like what what is the best part of this class? So yeah, that's my two cents.
Next up, I wanted to focus more on the job readiness claim because they're essentially saying like, Hey, if you do this course, then you know, you don't need any other experience. And then you're just going to be able to come and get a job through Google or some other places. So this is their claim. You will learn in-demand skills are have job-ready in less than six months. No degrees were experienced.
How do I feel about that claim? (Google Data Analytics Professional Certificate)
Okay, so how do I feel about that claim? I think even worse, anybody that wasn't Google thing that I probably would be far more skeptical. You know, how are you going? To have that leverage tray? Like how are you going to change other companies to accept your certificate?
Because they usually don't, right? If you look, their job requirements is always even if they're like entry-level job requirements, like needs, these skill sets and then you have to have some sort of experience. They're always looking at that. But the fact that Google is making this claim makes me feel more confident that this is something that they're actually gonna get to make happen. And, you know, they do the whole part.
Within these courses. They give you instructions and they walk you through how to build up your portfolio. You have that capstone project, and they also go through like interview tips and they do all of that stuff. And what's really unique about it is see where it is,
Job Search Process? (Google Data Analytics Professional Certificate)
okay, here, what resources will be available to help the job search process? Yeah, so this part, in addition to extra leg training and hands-on projects, you get access to all these like workshops and whatnot. This is really what's most important, you'll also be able to connect with over 130 US employers or search for candidates who have completed a Google career certificate.
So they go on to talk about they're gonna talk about a little bit more about like, you know, this consortium, you call it that they have in which they have all these employers that are going to be ready. Unfortunately, we can't access that right now.
At least I can't because you have to complete all of the courses. And actually, nobody can because not all the courses are available yet. But in that hub, you will get to connect directly with these employers who I actually believe they pledge that they will be hiring people who complete the certificate, and Google itself will be hiring from here as well.
You know, they're making this entire claim. So they're going to go through and I'm sure that they will go through that whereas they wouldn't be making that claim they will be hiring that pool of people will be the certificates as well.
So if they're going to be doing that other companies like with these 130 us employees and even other employers that are out there are probably going to be like hey google like such alike you know huge tech company is going to be are saying like you know immediately sir Timothy, then you're qualified enough. So these other companies are probably also going to follow suit and be able to take people who complete a Certificate as well.
So I am quite confident that because of that whole, that is Google, and they do a really good job in connecting you with these people. And you know, making it such that you don't even learn about your knowledge. What you have in the end is something that you can present to employers. With that being said, though, what I touched on a little bit earlier is the fact that there are so many people that are going to be doing this.
I think like last time I checked, which is yesterday, there was like 10,000 people already enrolled like how much that's like you're just assuming, I don't know, like a few 1000 People actually finish the certificate because most people don't finish a certificate but assuming there's like a few 1000 people that do you know, how do you stand out like if nobody has any experience? And everybody just does the certificate and just the capstone project? How do you stand out in that pool? Right?
And I think doing a dissertation is not going to be enough in itself. You're what's also important is how you're going to have to build out your own portfolio. So not only do you have to have a capstone project, but I think you should also put in other areas of this course and apply that to several different projects.
And I think they should be end-to-end projects, all the way from like a real business use case something that is complete, very, very applicable, applicable to the workplace, and then going through the entire analysis process.
And then showing that you drive, you're able to drive impact and I think that is what's going to make you really stand out having those projects in your portfolio. I also made several videos previously about like the best data science projects, things like that. So feel free to check that out.
If you haven't already. I emphasize a lot on not doing just like passion projects or projects that you do. Just you know, you're like do a project and then it's like in your portfolio and then nothing ever happens out of it. I emphasize a lot more doing like pro bono consulting projects, research for the professor.
If you're still in school, things that really drive impact in the business, because that is real-life experience. And that's what employers are going to be looking for. Now that we've kind of covered everything else.
How to Actually Make Sure You do it? (Google Data Analytics Professional Certificate)
I'm going to cover the last section about how to actually make sure you do it. Say you're committed, you're like this, this course is great. You want to become a data analyst or a data scientist, you know, we're something similar to that. And you're like I'm committed, I'm going to do all these things to get a job.
But as our put it you know, 10% of people actually complete this and there's a reason for not because so study is so freaking hard. It's so hard man.
For those of you that might know I know some of you who are also on my Livestream, I lie to myself going through like these online, these self-study courses because if I don't, I literally just like do nothing.
I've tried so many times like that's temporary said I'm the 90% and most like every single time I would start like oh this is so interesting. I'm like so dedicated, but then in the end I was just like, oh man, like you know, I could be doing like data science we do data analytics, I can be doing this course but I can also be watching the enemy.
And then guess what always wins. The enemy always wins. So they just, you know, it's basically I'm trying to say it's really freak in hard. So you have to find a way of actually getting through especially for something that's this intense.
This is a really, really intensive program. It's, you're supposed to like this recommend six months you're doing 10 hours a week. You can always do less. You can always like finishing law faster if you have time for most working professionals if you're trying to switch careers 10 hours per week is a pretty reasonable time period to actually finish this course because there's just so many other things that you got to do and the work that you're doing here.
It's very applied. So it's not like you can just sit there and absorb the information you actually have to like work on projects. So 10 hours a week. is reasonable. If you're late, currently unemployed where you're a student you have extra time, you can probably knock this out in like a couple months or so. You also can pay a little bit less, although I don't think you should try to knock it out faster just so you can pay less because it's already really really cheap.
And also, there's a community that we kind of hang out together now like I recognize people's names, who frequently come and do the live stream with me. On top of that the words, the time period doesn't work for you. You can also have a discord group is available as well and you can find a partner to work with within that discord group.
Or if you just have someone else who's really interested in learning, that's always good to have like having an accountability partner is extremely extremely important.
Whether that be me while I'm live streaming, or it's like somebody else keeping you accountable. Be careful though, because one of the things that I ran into personally when looking for when like doing accountability Buddies is that you keep each other accountable, but if one person is not dedicated, that can also make you less dedicated because you know a works both ways.
So if you're going to find an accountability partner, make sure that they are someone that is just as motivated as you are to get something to actually get this done. And that your learning styles kind of Jive together.
I purse I usually would say like don't go with one of your closest friends. Because you're you're a lot more comfortable. Like if you screw up and you forgot, forget one thing you're a really good friend. You're just like oh like whatever right?
So you should choose someone who you don't know super well. So there's like that awkwardness I think about how awkward it is he just goes from be like, hey, like I was too lazy to do this work, you know, canceling something like that. So that would be my personal accountability, either with me or find someone to do this.
Do this with you and make time to have a bearded assistant. I will you will always do it these days and at a specific time. I will you will always do it these days and at a specific time period. As for me, I prefer the mornings because I get really tired after work. So I probably won't do it if I try to do it at night. So I always try to do them in the mornings.
This Course Most Productive Fashion.
The other. I'm going to go into more about how to go through this course most productively product highly productively in a most productive fashion. It's going by breadth over depth.
This is pretty much how I approach most technical really skill set based learning now. Don't try to go through the course and be like, memorize every single thing in the course. And just like taking copious amounts of news. No, no, we what you're really doing when you're going through a course like this that's very analytically driven, a very skilled pyramid is that you are learning what you don't know.
A lot of issues that we face are that we don't know what we don't know. Right? It's like, if you don't know what you don't know, you can't look up and you don't know if you know what you don't know, then you can always look that up.
So what you're really doing is getting a good breath and understanding of all the different subjects and then you can always go back and look it up. Google made this course. Google is also a great search engine.
Tips for Complete that Course, (Google Data Analytics Professional Certificate)
So I am gonna go ahead and talk about a few more of my tips. Um, in terms of making sure you actually complete that course.
So we just went over the accountability of breath over depth and kind of going on for breadth or depth how to take this is a mistake I made because I came from a non-technical background. I was pre-med added pharmacology and undergrad and pharmacology is very memorized and like it's a very memorized, heavy kind of subject.
So you just sit around and memorize drugs all day, essentially, and drug pathways. But because this is analytics, right, we're this is about like it's more technical. It's about critical thinking and analysis as opposed to memories and stuff.
So my tip is I don't go and take a bunch of notes don't like a kind of like write or typing up every single thing that's bad that person is talking about because that's not really the best way to learn in this field. What's a lot better is to have a framework that really writes down the framework of what the person is teaching you, I guess the teacher is teaching you for each of these sections.
And then just like kind of write that down, like these are topics that have been covered and take some notes that are more that like maybe just pique your interest so that you can look back on in the future. But don't try to write every single thing down because I said it before. Cover it up.
What you what you're really learning in this class is like you're understanding the framework of how to approach questions. And it's not like after you finish this class, you're just like, so you can always go back.
So just know to write down all the things and if you forget and just come back and look at it again and you're probably going to go over your notes multiple times because each time that you go through it, you're going to learn a little bit more and you can increase your depth in all of these different subjects because it's covering a lot of different topics.
So you're able to go deeper and deeper into that. I do recommend that you actually do take notes stuff because it helps you stay concentrated especially in these kinds of online learning where self-study it's really easy to just be like you know, like watching a class or something if you're taking notes, you're just like oh,
So you have to you should definitely take notes and it helps. It helps you retain information as well. studies do show that taking notes even if you never look at your notes again, actually helps you retain better when you do a test afterward, compared to people who don't take notes. Finally, you should not skip the practical experience of the capstone project. I touched on this previously, I'm just gonna sound like a broken record here. But please, please, please do not skip on the capstone project.
This is the best way of learning Google Data Analytics Professional Certificate
The best way of learning by far is by doing and they have this indle into the class and that is like frickin, that's a miracle at $39 per month like that's hard to find. You know. And like capstone project is one of the reasons why this course is so amazing.
You know, it's not it's you can apply everything that you learn today, you actually learn it very deeply, and it helps you build that portfolio out. And I know they have a lot of practical exercises as well as you go through the course.
Do not skip those as well. If you want to skip some of the videos that's fine if you do like a piece of note information already that's totally fine. Just take the quizzes and passes. But don't skip the hands-on stuff.
Especially if you come from a non-technical background. You may be like oh like I understand what how things are working right? By when it comes to like technical tools, but she's really programming are things like that. The implementation is everything. It's like a whole other beast, implementing stuff that we're going to spend most of your time on.
And as you're implementing it, you start to realize that there's actually so many things that you don't understand. You would never have known that if you just feel like you just went through the overview and just feel like you understood things at a high level.
Finish up this Certificate. (Google Data Analytics Professional Certificate)
So yeah, those are my tips on how to keep accountable and actually finish up this certificate. Again, this is such a great certificate, but it doesn't mean anything if you are not able to finish it right which is what happened to most people is Who is this good for?
So Google says pretty clearly that this certificate is really good for people who are wanting to transition into data analytics. I think it's especially good for people who may have a similar job right now like maybe as a business analyst.
Your skillset is different get another two to either like get promoted where you know, parallel change to a data analyst job, or at another company because that will make you stand out. The surgeon pa will definitely make you stand out and you stand out from the rest of the path because you actually have real job experience. It's also definitely for people who are entry like who don't know anything right now I want to get into data analytics.
Again, I think you do need to build out your portfolio a little bit more than just whatever it is covered in the course, but it is very entry-level. They start from the very, very beginning and go all the way up to the capstone projects.
If you follow that through, you will have the basis of being able to become a data analyst and again, the entire job hunt. They're going to help you out with that. I trust Google, they said that they're going to do it they will do it.
So I think this is something that you can rest easy that if you complete that and then you make yourself stand out. Like nobody's gonna be like no, like, I think it'll be like your job as I'm trying to say. But one of the things I do want to say like who is enough?
I don't think that this course is good for people who wouldn't be able to dedicate that time because like I said, it's a really extensive program. Your recommendation is six months, even to be judged. And you know, some people do that for two to three months. We don't have other dedications. But if you're like, I already have a full-time job. I don't have a baby.
Or else like we're like, you know, just we're really really busy. I wouldn't recommend taking on this because it's going to be in because you know you got to put up that time and effort to learn this new skill set.
And so make sure that you actually have that time to dedicate yourself to motivation is one thing but really having like being able to plan that on your day-to-day basis is extremely important. Another thing I wanted to bring up here was, if you are international like if you're not employed or not, I'm sorry, we're a little bit less biased here even though I'm not using American. If you are not American.
Do realize that doing this certificate will not help you get a job in the states like you can't take the certificate and then expect employers to hire you in the states if you're not in the States and also the job support right now is only for the United States. So if you're not in the US, they say they're going to be expanding it but they haven't actually expanded it yet.
Do keep that in mind that you will get all the benefits of course, like all the information and also learning how to make a good portfolio but you wouldn't be able to utilize that hub, the job hub that they have that will connect you to these 130 different employers including Google.
So yeah, that's pretty much everything that I wanted to touch on today. Please let me know if you have any questions or some things that maybe I didn't cover, but I feel like I've covered through all the different topics of the course.