How to Create Your Own Data Science Curriculum with Coursera in 2021

I used to work at the Apple Store, right? And we had this system where you had a problem with your device, you'd come in and say, hey, I've got a problem with my device. And we'd be like, oh, what's your problem? And if we could fix it on the spot, typically we would, we would try to do that. And, but if you couldn't, if we couldn't fix it on the spot, well then we'd book you in for an appointment called a Genius Bar appointment. And you see, I was one of those geniuses. So it was my job as a technician to go up and help you fix whatever problem you were having with your Apple device. And then one day I remember, I remember the exact seat she was actually sitting in. There was this girl and she had a problem with her phone and I saw her name on the list. We'd checked her in before. So I went over and said hello, introduced myself, asked her what the problem was. And I, when I did go over, I noticed her scrolling through a blue app. And so of course to to make conversation. I always loved just making conversation, meeting, meeting new people every 10 minutes. I asked her, what's that app you're scrolling through? And she said, Coursera. At this point, I'd never heard of what Coursera was. And I'm like, oh, okay, what do you do on that app? And she's just like, well, there's a whole bunch of different courses on here and you can pick them and you can learn something. And I was like, oh, okay, are you— is that part of whatever you're studying at university. And she's like, no, I just think it's, it's always good to be learning something. And we continued with the appointment. I solved her issue, of course, but that one line, that was like one of those lines where I was just like, oh, it's always good to be learning something. You see, up until that point, I'd only ever learned things that I had to. As in, I'd followed what I had to do at school, I followed what I had to do at university, I followed what I had to do with on-the-job training. But that one line changed my entire concept of learning. That taught me— well, I mean, that night I went home and researched, looked up what Coursera was, and I discovered this treasure field. I was like, oh my gosh, you mean I can just sign up to this course and start it now? And yeah, I could. It's funny how just tiny little chance encounters like that can just change your entire way of thinking. And so it wasn't actually until a couple of years later when I left Apple and decided, you know what, I want to get into building the, the programs that I'm helping to troubleshoot on these computers. I want to, I want to teach myself how to code. And as I went through that, learning to code for the fourth time, I stumbled across machine learning. And then lo and behold, I stumbled across Coursera. And I figured, hmm, I've already spent enough time at university this decade. How about I just create my own online AI master's degree? And so I did. I wrote a blog post about it and I just pieced together different courses from around the internet and put them together. And Coursera happened to be one of the resources I used a fair amount and still to this day use. So when Coursera reached out to me and they're like, hey Daniel, We've got this amazing offer on our new service called Coursera Plus. It's $100 US off until January 10th. And I mean, if you're watching this in the future, it's even if you don't get the special deal, it's still a good deal. Would you be interested in making a kind of a create your own data science curriculum video for 2021? And I was like, yes, of course I would do that. I've actually been meaning to make a video like this for a while, so Big shout out to Coursera for sponsoring this video, as well as creating an incredible platform where eager learners like myself and perhaps you watching this video can create their own curriculum. So without any further ado, let's dive straight into it. What I've done is I've gone through the Coursera Plus, uh, there's about 3,000 or so courses from there, and about a year ago now I created a video called 5 Beginner-Friendly Steps to Get Started with Machine Learning. And I've recreated that video/curriculum using purely Coursera courses. So let's jump in. So here we go. I've created all this in Notion, by the way. Now you might be wondering, why Notion? Well, you could actually use anything. I used a blog post to start my curriculum, but Notion is just— it's really cool. Let me just show you rather than talk about it. But anyway, here Here we go, 2021 Coursera Data Science Curriculum. Now, some yarn about how to create this. If you're looking at the link in the description, you can click duplicate. It should be in the top right corner. But because mine is— I've created this online, I don't have the duplicate button. Let's step through this. So what's the goal? The goal of this curriculum is to learn the foundations of data science and machine learning with Python. Now, why Python? Well, it's the programming language I have the most experience with. You can write data science and machine learning code in other programming languages. However, I don't have experience with those, so I'm going to stick to what I know. Now, date started yesterday because I created this little document yesterday. But how about we do a little and we can go @today. See, this is the cool little things that Notion has. And date to go through December 31st at the end of this year. So you'll find as we go through this, there's actually a fair amount of content here. That you could go through. It could take you an entire year. But remember, this is beginner-friendly, starts from the foundations, and anything worthwhile, or anything worthwhile to learn, takes a while. I said while a few more times there. Doesn't matter. So this is what I like to do whenever I'm sort of creating my own curriculums in Notion. This is the exact same layout I used for my Getting TensorFlow Developer Certified So the materials that I followed for that, I usually have status, to-do, notes, curriculum, curriculum tracker, extras, and log. So status is just a one line of exactly what I'm doing. So I'm currently putting together an outline of what I'd like to learn throughout 2021. Boom. That's all we're doing. We're keeping it simple. And I'll often change this as I make progress. So this entire like curriculum itself, is really just a note to yourself, like a way to keep track of what you're learning online. Because what does a university course give you? And again, this video is not— I actually haven't said this yet, but this video is not arguing whether you should go online or university. But if there's anything 2020 has taught us, it's that every individual needs to be in charge of their own health and their own education. So this is catering to that education piece. So to-do, I often put a bunch of little just tidbit to-dos here right below the status. So choose course outline. We've done that. We're going to see that in a second. Set up curriculum path in Notion. Boom. We've done that. Sign up for Coursera Plus. Let's click this. So this is the Coursera Plus deal we've got here. Coursera Plus, Coursera.org/courseraplus/holiday. Now This is in Australian dollars cause I'm Australian. You can get 14-day money-back guarantee. So if you go through this, you sign up and you realize, holy crap, I don't want to do this. You can get your money back within 14 days. Woo! But, um, right now there's a $100 US discount. Again, my numbers might be different to yours depending on where you're from, but it's only valid through till 10/01/2021. You've got a whole bunch of great universities here and different organizations you can learn from. Yada yada ya, I'll let you go through the details about Coursera Plus. What we're mainly focused on here is how quickly you can create your own curriculum. So sign up for Coursera Plus. Let's pretend we've done that. Now, these are just the 5 specialisations I've gone through on the Coursera Plus. Here we go. So we've got a whole bunch of different specialisations and courses that you can do that are included So you sign up to this, right? You get unlimited access to everything down here, which is actually an incredible amount. There's actually too much. Like there's 3,000+ courses, guided projects, specializations, and professional certificates. Now, too much for one person to go through. So that's why I've narrowed it down to 5 beginner-friendly steps that you can get started. Again, this is for beginners. If you have about a year's experience, experience in machine learning, you probably want to sort of follow your own curiosity at that point. Now, these are the main to-dos. Of course, you can upload— update those as you need to go. I've got my notes here. We'll dive into that, what that means. Curriculum outline. This is probably one of the most important points. So what and why I'll be going through to learn the foundations of data science and machine learning with Python. I always start off with the Learning How to Learn course. I did this and again, another one of those, those things like a book you read and it changes your life forever. This course changed the way I viewed learning forever. I believe this should be compulsory, the Learning How to Learn course. Let's click on that. For everyone who's embarking on any kind of learning journey, whether it be through university, whether it be through school, whether it be through online, this is phenomenal. If you're going to be creating your own self-driven learning pathway, you most will learn how to learn. Learning is the ultimate meta skill. If you learn how to learn, you can learn anything. So this is number 1. We come back. Oh, we need to go back to Notion. Now we get specific into learning the foundations of data science with Python, machine learning and data science with Python. So if you have zero programming experience, you're probably going to have to learn Python. Again, you could choose whatever programming language you want. But if you choose Python, you probably have a plethora of resource, or you do have a plethora of resources available. And I am a big advocate for Python because it's what I use. I can't advocate for other languages because I don't use them. So Python for Everybody Specialization. This will take you from not knowing any programming to knowing Python, the fundamentals of Python. I mean, Python is a general programming language, so it can do basically anything. So you're going to have to get familiar with the fundamentals if you're going to start writing data science and machine learning code with Python. Speaking of which, that is the next one. So the Applied Data Science with Python specialisation. So we've gone through Learning How to Learn, we've gone through Python for Everybody, or you, you have, and then you're going to go through the Applied Data Science with Python specialisation. Again, Coursera Plus has unlimited access to 3,000+ courses. So if you don't like the path that I've laid out for you, you can customize it yourself. But I've gone through them and I believe this is, this is phenomenal to get a foundation in data science and machine learning. So this one I've got, I've personally gone through this one. I have a video on that or I have multiple videos on this, I believe. And it will start to get you. So again, Python is a general programming language. So this will specifically start to get you hands-on with data science or data science-specific Python code. So the Pandas library, the Matplotlib library, library for visualizations, and the scikit-learn library for writing machine learning code. Now we come back. Next is the machine learning specialization. So now, so this one, the applied data science with Python specialization, is mainly focused about like manipulating and visualizing data, two massive steps with machine learning. So this will get you, give you experience starting to work with data, to get it ready to be used with machine learning models. And now, for actually using machine learning models is the Machine Learning Specialization. So if we come down here, what have we got? So it goes Machine Learning Foundations, a case study, Machine learning regression. So regression is a problem to predict a number. So say you want to predict the prices of houses, you would use a probably a regression machine learning model. Machine learning classification. So classification is a problem like say diagnosing whether, or using a machine learning algorithm to say whether an image is of pizza or an image is of a burger. Clustering and retrieval. So clustering is say, to group like individuals together. So say you had a lot of customers to your website, how do you group the ones that mostly purchase in summer months with the ones or against the ones that mostly purchase in winter months? Again, you might not know when you, before you cluster them, which months they purchase in. And then information retrieval, such as pulling out information from a database. So then we go back. So once you've gone through the machine learning specialization, The final one I've recommended here with this 5-step approach is the Mathematics for Machine Learning. So up until now, this curriculum has had a focus on, of course, learning how to learn, learning Python code. So the fundamentals of one of the most popular machine learning and data science programming languages. And then once you know the fundamentals of Python code, you've started to learn data science-specific Python code, then You've started to, once you've learned how to manipulate and visualize data with Python, you've gone into writing machine learning algorithms with Python. And now since we've been focused on code, you might be curious as to what all that code is doing. 'Cause right now we're just, you're just writing Python code and it's triggering some machine learning algorithm to do something to find patterns in data. Well, what's going under the hood is math. So that's why we've got at the end mathematics for machine learning specialisation. So once your interest has been sparked, once you know how to diagnose a machine learning problem, you might want to, or you probably will. I mean, I did dive deeper and figure out the nuts and bolts as to what's going on behind that machine learning algorithm. So we'll come back. That's the outline there. Let's go on to the curriculum tracker. Curriculum Tracker is possibly the most important part of this entire curriculum, other than the material itself, other than you personally going through that material. But this is how you stay on top of what you're learning. Because remember, what is a— what does a university course offer you? It, uh, it offers you accountability. But what we're creating here with creating our own curriculum is accountability for ourselves. So I just use the basic Kanban system here. So I keep it as a card. I put the Learning How to Learn course here, right? And I've broken it down. I've got a to-do at the top. I've got part 1, part 2, part 3, part 4. Then I've broken it down. I've got some headings here. So let's put this over this side and this over this side. And we will go learning how to learn. So, all I've done is— I'm going to open this one up as a page. All I've done is broken the topics or the syllabus into little headings so that I know I need to go through part 1, what is learning. I've got what is learning here. I'll add all of my notes into this section there. Then I come down to part 2, chunking. I'll add all of my notes there. And the same thing, so on for part 3 and part 4. So I could go back and I put a little due date here for myself. If I wanna start today, which is January 3rd, and I wanna go till, say, the 1st of February. Now, where did I get this timeline from? I believe all of the courses give you an estimated, there we go, 15 hours to complete. So maybe that timeline is a little extended, but this is where you want to create, if you're creating your own self-driven curriculum, You can decide how fast you go. One of my favourite blog posts— I'll link this below— is called "There's No Speed Limit" by Derek Sivers. Basically what it says, if you like to learn something, you can go as fast or as slow as you want. But check out that post. That's a little bonus for this. Now, if we come back to our curriculum, We're gonna go up here. So see how I said before we'll come back to notes? This is where my learning how to learn notes live now. So if I wanted to reference something back there before, I could just click in here and then I get the card. So we could do the same. Let me demonstrate how we do the same for one of the other courses. So we go here, Python for Everybody Specialization. Going to open this as a page. We've got a beautiful, beautiful little Python symbol there. That's one of the little tidbits about Notion that make it fun to use. So I've got a due date, February 1st, 2021 to April 1st, 2021. Of course you can adjust this for whatever you need, but I typically like to do one thing at a time. So I'll go through the Learning How to Learn course and then I'll go through this. But let's go here. So say if we go Python for Everybody, Boom. Let's write down. Let's see what's in here. And I mean, Coursera, you could probably develop this feature for yourselves instead of me having to do it through Notion. So if you're watching this, that's an idea. Well, that's a great web app, actually. I love a web app that does specifically this. Um, to-do. We got programming. So I'm going to break all of these courses down. Programming for everybody, because what I'm doing with this is making it so that, of course, if we looked at all of this in one hit, there's a lot going on. But when we break it down into smaller steps like we're doing now, so using Python to access web data. So that's course 3. And then number 4, using databases with Python. And then that's it. So it's 4 courses. Now where's the timeline here? Okay, approximately 8 months to complete, but that's at 3 hours per week. So again, there's no speed limit. If you decided to perhaps commit 20 hours per week, which is 3, 3 or so days depending how much you're doing, you could accelerate this by 6 times. So really, instead of 8 months to complete, it might only take you 2. So now we'll come down here and then what I'll do is I'll break these into their own individual headings. So that way I have— so part 1 is programming for everybody and then I'll write all my notes for that section there. And then 2, we'll do the same thing, create a little heading. I might speed this up. Boom, wonderful. So now I've got a little section and we go back, go back. So this is back in our curriculum. What I can do here— oh, I don't want to do that— is can press Enter. And then if I want to link to page, and then if I want to search for Python There we go. Boom. So now I've got all my notes linked here and I can do the same, or you could do the same for the other courses. So the Applied Data Science with Python specialisation, the Machine Learning specialisation, the Mathematics for Machine Learning specialisation. And now we've got them in a, a Kanban view. Another beautiful way that we can do, that we can look at them is to change it into a timeline view. So you can see where you are. Let's say, for example, we wanted to start at the start of the year. So look, I've set up some due dates for each of these. So I know how far or how long I have if I want to stay on top of what I'm working on. So again, Learning How to Learn goes to February 1st, and then Python for Everybody goes from February 1st to April 1st. And then I've got the Applied Data Science Specialization. I've given myself 2 months for that. It goes from April 1st to June 1st. The Machine Learning Specialisation, that's a pretty hefty one after looking at the curriculum. So I've given myself 3 months for that from June 1st to September 1st. And then finally, I finish off the year with Mathematics for Machine Learning Specialisation. So this is just a really cool way to not only keep on top of what you're learning, but to also keep on top of your notes. And I mean, once you've done— once you've added some notes here, if you want to come back, Everything in Notion is searchable. So you might be thinking, okay, yeah, Daniel, I've gone through all of this— it's actually pretty cool that I can just create it all into my own curriculum. I mean, this took me all of an hour. But again, don't be fooled by how quickly you can put this together to or get confused with how quickly you can put this together as to how quickly you can learn it like we've literally laid out a year's worth of, worth of study here. Again, you can take as long or as short as you want, but you might be wondering, what are some extras that I can do? Well, you might want to go on to the deeplearning.ai courses, but you will need some prerequisites for those, and these will definitely set you up for them. Now, the deeplearning.ai courses aren't included in Coursera Plus, so you'll have to keep that in mind. But all of these are. And then there's probably one more thing and that's this article here. Read through Q experience. So this is an article I wrote because a lot of people ask me sort of, I've gone through some foundational courses, what do I do next? How do I get experience? Well, I put together— that deserves its whole video. I think I've got a video on that and I've also got a full length article on it. So make sure you check it out. This is what you should do after you've done some There we go. I've done some courses. What do I do now? Hint: touch the stove. The main point in this article is basically if you're looking for a job, start it before you have it. And of course, share your work. So we come back here. There's one more thing I want to do and this is— Log is probably one of my favorite sections of this entire curriculum because it's just a little daily updater of reminder or something. No, I don't want a page. I just want today. It's just a little way to sort of write down what you did that day. So today, today I created my 2021 data science and machine learning curriculum in Notion. Tomorrow I start learning how to learn. And so when you first start, you're not going to have very many of these, but I like to just write 2 or 3 sentences at the end of each day of what I did that day. And by the end, you'll be able to scroll through and go, holy gosh, on those days where you feel like you've learned nothing, which is almost every day, you go, wow, I'm a lot further than when I started. So make sure you play around with this Notion template. I mean, of course you don't have to use Notion, you don't even have to use Coursera. Everything that I've listed in here you can learn online. One of the benefits of using Coursera or Coursera Plus is that everything is in one place. And if you are going to sign up to Coursera, they have that $100 US promotion until January 10th. If you're watching this video in the future, that might be expired, but that's okay. Press the duplicate button of this anyway and use it to create your own curriculum using Coursera or not using Coursera. The main takeaway of this entire video is that the internet has provided a resource for you to create your own learning journey. If you have any questions about anything that we've discussed, leave a comment below. And I'll answer it. If a few people are asking the same question, I'll add it to the, the blog post version of this video, which you'll be able to find where you find links. But as always, keep learning, keep creating, and happy 2021.