 What's going on everybody? Welcome back to another video. Today we are going to be looking at the best and my favorite Python course for data analysts. Now before we start looking at this course I want to give a huge shout out to the sponsor of this video and that is Udemy. You guys know that I absolutely love Udemy. I talked about them so much on this channel and they decided that they wanted to love me back and sponsor this video. Udemy has hundreds if not thousands of courses dedicated to data analytics and so if you were looking to learn a new skill I highly recommend checking out Udemy. I have a link in the description that will take you to my homepage that will have every single course that I personally recommend and have taken and loved. Without further ado let's move over to my screen and check out the course. As you can see the course is called Python for Data Science and Machine Learning Boot Camp. Now don't get intimidated by the words data science and machine learning. Honestly the course is really broken down very well. I don't think it's data science specific. I think it's data science, data analysis, data manipulation and it has so many incredible things that I think a lot of people not just data scientists can benefit from. And as we walk through this course I will be highlighting a lot of the things that I think are just so important to know if you do want to become a data analyst or data scientist. Now before I sign into my account where I've actually purchased this course I want to say that this course is actually not $95 or it is $95 but you don't have to pay $95 just google Udemy coupons and you can easily find a coupon and will bring this cost down to around $13 to $14. Or you can wait for Udemy to have one of their sales which they do I think maybe every two or three weeks where you can get everything on Udemy for like 10 to $15. And so for this course I do not recommend paying $95 wait for it to go on sale so you can get it at a much bigger discount. So this is Jose Portilla. He is the guy who actually created this course. He is phenomenal. I have taken about maybe 15 of his courses. He has many more than that and I have loved every single one of them. I'm going to stop this video in just a second because he's going to highlight some of the things that are super important that I think are fantastic about this course and that's right here. So some of the things that you're going to be learning in this course that I think are just phenomenal and things that everybody really should know if you're trying to become a data analyst or data scientist. These are the things that I will be focusing on really early on. Things like NumPy, SciPy, Pandas, Seabour, Matplotlib, Plotly and PySpark. I left out Scikit Learn because that is machine learning heavy. You know if you're becoming a data analyst you really don't need to know Scikit Learn but it is there in case you do want to learn it. I will of course get into some of the machine learning stuff later on why I still think it's worth going through and learning and looking at and so I think the real value of this course is in everything else and not really in the Scikit Learn part of things. When I was first starting out I didn't really know how to use Python at all. I didn't know how to run scripts or what an IDE was or any of that stuff and so genuinely this was one of the very first videos that I watched when I was learning all of those things and so in this video he's going to show you how to set up Jupyter notebooks. If you don't know what Jupyter notebooks is he's going to walk through everything you need to know about it, how to set it up, how to use it and this is what you'll be using through the entire course. If you are an absolute beginner he has a crash course in Python where you can actually learn all of the basics all the way from arrays and data types and strings all the way up to creating for loops and functions which are a lot of the things you're going to need to know in order to actually take this course and so you don't have to take another course to learn it. You can get a crash course and everything you need to know right here at the beginning. Right after that crash course it gets into the really good stuff the things that I think are super important to know. It dives into NumPy, Pandas, Matplotlib and Seaborn. These are a lot of the things that I've talked about so much on this channel about things that you should really know if you're going to learn Python. Now the reason I love this course and I recommend this course is because you are not just going to learn everything about Python. Honestly for a lot of the work that you're going to be doing you don't need to know everything about Python because it is so broad. You really should be focusing in on the things that make Python useful for you. And so honestly this course is getting really specific and really deep in the exact things that you should be focusing on. That is one of the reasons why this course is so fantastic. Right after you learn all of those libraries it takes you through two projects. The first one being a 911s called Solutions and a finance project. Now I would not say that these projects are necessarily advanced. I would put them more at the beginner maybe the intermediate level but it goes over almost all the things that you just learned and so right after you learn it you can apply your knowledge and have two projects on hand and I think they're really good starter projects. As you can see on your screen he is walking through this with you and he's talking through why and how and all the things that you were learning during the course and it's really really nice. All these things that he's filling in right now were not there and so you had to go through and fill all those things in and if you couldn't figure it out it's okay because he's going to walk through the entire project with you. Now the next several sections of this course are all focused on machine learning. I'm going to quickly walk through all the things that you're going to cover and then I'm going to talk a little bit about why even if you're not going to use machine learning in your job why you still should take these sections of the course. It's going to give you an introduction to machine learning. You're going to look over linear regression cross validation and bias variance trade-off, logistic regression, K nearest neighbors, decision trees and random forests, support vector machines, K means clustering, principle component analysis, recommender systems, natural language processing, neural nets and deep learning, big data and Spark with Python and then a thank you at the end which obviously is not machine learning but so if you're not going to be using machine learning at all why should you still take these sections. I'll give you a few reasons on why I still took them and why I'm very happy that I did. One is I work with other data scientists, people who are really good at this stuff and you know this stuff way better than I do but in meetings and stuff like that I would hear certain things and and you know K nearest neighbors and let's use this model. I had absolutely no idea what they were talking about and I kind of felt a little bit like out of the loop. I was like okay these people know what they're doing they know what they're talking about I should really kind of bone up on this so that I actually just know what they're talking about even if I can't engage with it and really talk you know intelligently to it and and offer advice and what I would do I can at least understand what they're talking about and you know be part of that and so that is the first reason. The second reason is honestly learning this stuff is extremely cool. I think machine learning is fantastic. It may be something that just kind of triggers something in you that you really want to continue learning this and pursuing this as part of your career and so don't just you know push it to the side or not take it because you think it's a little intimidating or you don't think you're going to use it. I highly recommend still taking this part of the course. A little hidden gem at the very end of this course is called Big Data and Spark with Python. I use Spark at my job and I honestly had no idea what it was even when I was taking this until I actually started using it and I was like I remember taking this section of this course that had Spark in it let me go back and review it and then I ended up learning things from this later on not actually when I was taking it the first time and so Spark is super cool. I'm not going to go into everything that it is but it really is great with working with Big Data so if you do plan on working with Big Data in the future I highly recommend taking this section of the course on Spark and so like I said before if you are a data analyst or a data scientist or you're trying to become one of those this course is absolutely fantastic for learning Python. I think Python is a fantastic skill to learn but there are so many things you can do with Python it's hard to know exactly what you should be learning and I think that this course does a really really good job at focusing in on the things that you should be learning and really excluding all the things that aren't super helpful in those careers and so that is why I recommend this course and that is why I think that this is one of the best Python courses at only 25 hours for the entire course you cover so many good things in it. If you want to take this course there will be 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 and I'll see you in the next video.