 Hello everybody and welcome back to another video. Today we're going to be taking a first look at Google's new Advanced Data Analytics Professional Certificate on Coursera. Now this course was just launched on April 6th so it is super, super new, but it's meant to be the more advanced version of the original Google Data Analytics Professional Certificate that they launched I think about two or three years ago. The first Google certification that they put out for Data Analytics was just massive. I mean millions of people took it. I took it myself. I know a lot of other people were posting on LinkedIn and Twitter and Instagram saying that they had just completed it. It was really, really big. Hopefully this course will be even better as it's going to be going more advanced into a lot of these topics and we're going to take a look at a lot of those things today. So in this video we're just going to be taking an initial look at the certification, looking at all the things that they teach and how they teach it. At the very end we'll talk about timeframes, the cost and if I think it is actually worth taking. But before we jump into actually looking at these certification I want to give a huge shout out to the sponsor of this video and that is Coursera. Coursera has been one of my favorite platforms to learn data analytics. I have taken hundreds of their courses and they have been absolutely fantastic. If you have not checked out their platform yet I highly recommend it. I will leave links in the description to not only this certification but just Coursera as a whole because they are absolutely fantastic. So huge shout out to Coursera for sponsoring this video. Now one disclaimer I want to make is even though Coursera is sponsoring this video I promise you I'm going to give you my genuine thoughts, my genuine reactions to the certification. So with that being said let's jump into the certification and take a look. All right so really quickly we're just going to take a look at the landing page and then we'll kind of dive into the actual content of the certification. So this is what it looks like. We have the Google advanced data analytics professional certificate. You can start enrolling for it for free. Right now you can go and start taking it. Now if you want to take the entire certification you have to pay for it. I'm pretty sure if we click this you know you get like a free trial essentially. So let's scroll down really quickly it does say six months. Now we'll talk about that at the very end more but it does say it's going to take about six months if you study under 10 hours per week. Now let's scroll down because here's some of the skills that you're going to gain. Data science, regression models, predictive modeling, exploratory data analysis, Python, Jupyter notebooks, Tableau. Now all these are just the keywords but I'm going to tell you right now this is extremely heavily Python focused. I mean 90% of the course is on Python which I'm super excited about. I love Python. I wish they put Python in the original one and they didn't they put R and I was kind of a little bit butthurt about that and I think they really really listened to me on this because 90% of this course is Python. I will say this looks like a direct competitor to the IBM data analyst professional certificate here on Coursera which is very heavily Python focused. So again something I'll discuss more at the end but I was super excited when I saw that almost everything in here is Python and this is the advanced course so our got the beginner course Python got the advanced course. So let's keep scrolling down. Let's take a look at the different courses in the certification and then we'll dive into each of them very briefly on what they're going to cover in these courses. So for course one we have foundations of data science then course two is get started with Python. Course three is go beyond the numbers, translate data into insights. Course four is power of statistics. Course five regression analysis simplifying complex data relationships. Course six is the nuts and bolts of machine learning and then of course course seven is the Google advanced data analytics capstone. So that's a lot of stuff and it's all very heavily focused on Python. There is a little tab low and we'll take a look at that in a little bit as well but let's dive into these courses. I'm just going to briefly talk about what you're going to learn in each one. So the first course is foundations of data science. This course is very much just an introduction to data as a whole. What is data analysis? What do data professionals do? What skills do they need to learn? And it kind of flushes out. Here's what you're going to learn in this course as well. I definitely don't think this is anything too advanced in this first course. It's just introductions really but let's move on to the second course. The second course is get started with Python. In this course you're going to learn the basics of Python. You're going to learn about data types and for loops and functions and all these other things are kind of the fundamental basics of Python. As you're watching the videos they also have some reading materials and then they have labs where you actually go in and you practice writing out the code and they do this with an integration of Jupyter notebooks. I personally enjoy that Jupyter notebooks guideline because it really spells everything out for you. So even if you're a beginner you can still kind of follow along with this course because again it's really starting from the basics but hopefully in the next couple courses it'll ramp up to more of the advanced things. But let's go ahead and take a look at course number three. Course number three is go beyond the numbers translate data into insights. Now this is where I think things start to really pick up because it goes a little bit more advanced than just the basics. You start to get into exploratory data analysis. You start to look at how to handle missing data and bad data formatting so this is part of the data cleaning process and then you start taking all of those things and visualizing them in Tableau. Now let's look at course number four which is the power of statistics. Now this course really surprised me because I've taken a lot of courses on these statistics and data analysis but this one actually covered a few different topics that I haven't really learned before so I even started watching them and kind of learning from that and I was like ah that's really really interesting. So this course I think actually does a really good job of going in depth into the statistical side of data analysis. You're going to learn things like descriptive statistics, conditional probability, sampling, distributions, confidence intervals, hypothesis testing and more. Throughout this course there's a lot of videos, quizzes, labs, there's just a lot of things that I really liked about this course in particular so so far this is my favorite course out of the whole certification as a whole. Course number five is regression analysis, simplify, complex data relationships. Now it seems like this course went more in depth into regression analysis than other courses that I've taken in the past. I'm really happy to see that because this is an advanced course this shouldn't just be the basics or the beginner level stuff this definitely goes more in depth than just the surface level. The one thing that they go really in depth on is linear regression. Linear regression is super popular so they go really in depth into linear regression. Of course all of this is done in Python. They also talk about multiple linear regression, variable selection and model evaluation. They talk about hypothesis testing and they also go pretty in depth into logistic regression as well. Next in course six they have the nuts and bolts of machine learning. Now I have nothing against machine learning in fact I've taken lots of machine learning courses I think it's really fascinating, super interesting but I don't really understand why they included it in the advanced data analytics. I think sometimes there's a crossover where there just doesn't need to be. For the most part data analysts are never going to use machine learning. There are some instances where you work with data scientists or you're at a small company and you're just expected to do a little bit of everything but I don't think that this needed to be in here although it's not bad to have. The course goes into exactly what you think would be in this course. It's the basics of machine learning. It talks about what machine learning is, the ethics of machine learning and then just the basics of how to actually do that in Python. Now I will say that even though it is the basics it does touch on some more advanced concepts. It talks about fine-tuning hyperparameters. It also talks about feature engineering, unsupervised learning and then it also talks about a concept that I haven't heard before called bagging. I think I'm going to go through that one because I've never heard of that before but it looks like it's related to random forests but I'll need to watch and see. Lastly in course seven we have the Google advanced data analytics capstone. Now this is just the final project of the entire course. Throughout the course you're doing these smaller projects and then at the end you're doing one bigger project. They also talk about other things as well like building your resume and they have a whole workshop on how to do that and they also talk about preparing for interviews and building a portfolio. So all the kind of really relevant things as you finalize this entire project. The final project says it is providing data-driven suggestions for HR. So it looks like you're going to be given an HR data set. You're going to actually bring in the data and load the data, do some initial exploratory data analysis and data cleaning and then really dive into the exploratory data analysis phase. After that looks like you're going to be visualizing the data and then you'll be building a machine learning model at the very end. So now that we've taken a look at everything let's just kind of take a breather and just talk about the course and see if it's worth it. Look at time frames, the cost and just general thoughts on whether or not it's worth taking. So the time frame says six months to complete. I think a lot of people found that when they were doing the initial Google data analytics professional certificate that it took a lot less time usually like two to three months. That's what most people were saying. I think that this is going to be about that same time frame if you keep a steady learning pace. So now let's go back and look at the price. After that seven day free trial it's going to cost 39 US dollars per month. Now if it takes you three months and you're paying about 40 dollars per month it's going to be about 120 dollars total. And it even says right here go as fast as you can. The faster you go the more you save. So if you do it in two months or if you do it in one month you're going to save money just by going faster and completing it quicker. So now let's talk about my initial thoughts on the course. First off I absolutely love that it has Python. In fact I kind of wish that this one was the first certification that they had put out because it is just really really good. I understand that there's a lot more advanced concepts it goes more in depth into things and the first certification that they did kind of laid the groundwork for data analysis. You know it covered Google Sheets and Tableau and R and SQL and all those other things which I think is great. But this course just seems like it's going so much more in depth than the first one which again it makes sense that was kind of the beginner the basics and this is the more advanced course but I wish they had gone this in depth in the beginners course if that makes sense. So my initial rating after really reviewing a lot of the materials that they're doing everything that they are teaching in this course my initial rating is a 9 out of 10. If you're wanting to learn Python this could be a 10 out of 10. I mean genuinely it's like 90% Python. I really do compare it to the IBM data analyst professional certificate it's very similar although everything that I've seen in here looks much more new much more updated and kind of fresh whereas the IBM data analyst one was made like five six years ago I can't remember I took it myself like four or five years ago and even then it had a few things that were a little out of date. This seems very much fresh very new updated it seems very well made. Now I'm going to be taking this one myself I'll probably make a follow-up video in about two months or so after I've completed almost all of it and just give my more final thoughts on what I think but my initial thoughts about it looks super good it looks like it goes really in depth which is what I like I don't like kind of doing the surface level stuff but you kind of have to start somewhere and they're kind of trying to pair it with the initial certification that kind of lays that groundwork it gives you the basics and I think for what it's worth that course is still really good for people who are just getting into data analytics whereas this one looks like it goes a lot more in depth covers a lot more complex topics so with that being said that is the Google advanced data analytics professional certificate I hope that this was helpful if you end up taking it be sure to comment below your thoughts and your impressions as well hopefully they align with mine because I'm super optimistic about it but hopefully it's just as good as it looks if you want to check out this course I will leave a link in the description thank you guys so much for watching I really appreciate it if you like this video be sure to like and subscribe below and I'll see you in the next video