How to learn data science with Coursera in 2022 (beginner-friendly)

So it's 2022 and you'd like to learn data science and machine learning. Well, this video is going to cover how you can use Coursera, an online learning platform, and Notion, a workspace where you can build almost any kind of document you can imagine, to create your own Python data science and machine learning curriculum. For reference, when I started learning to code and then learned data science and machine learning, Coursera was and still is one of the places I visit most. And Notion is a tool I use every day to write things down and plan and track projects. In fact, everything you see in this video was created with Notion. You can find the link to the template you can duplicate and customize below. As mentioned before, this video is really twofold. One, giving you an idea of how you can use Coursera to start learning Python, data science, and machine learning with no prerequisites. 2, showing you how you can create and track your learning progress using Notion. And so all of the courses mentioned, or that I'm about to mention, in the upcoming beginner curriculum are available in Coursera Plus, an offering from Coursera that gives you access to 7,000+ courses, projects, and certificate programs, all from world-class institutions and universities like Google and the University of Michigan. And just a heads up, if you're watching this video in January of 2022, Coursera, who is also the sponsor of today's video— thank you Coursera— have a deal going for $100 USD off your first year of Coursera Plus until January 13th, which means you could sign up now, complete all of the materials mentioned in this video, and be a very competent Python programmer and data scientist by the end of the year. And if you're watching this video after January 13th, Coursera Plus is still excellent value compared to other learning avenues. I mean, I wish Coursera Plus existed when I started learning there. I would have saved a bunch instead of signing up and paying for courses individually. Don't even get me started on how much my university learning fees are. Check out the link below to receive $100 USD off your first year of Coursera Plus before January 13th. And hey, if you don't like it, don't worry. There's a 14-day money-back guarantee.

So if you try it out and find that you don't like it, you can cancel.

Now let's get into the fun part. Once you've signed up to Coursera Plus, what should you actually learn if you want to become a data scientist, data analyst, or machine learning engineer? Well, first things first, let's remember this curriculum is targeted for beginners, those who have little to no experience in the world of programming, let alone data science or machine learning. And so in light of that, let's define what each of these roles are. For the sake of this video, a data analyst is someone who uses data to understand the past, I mean, you may already be one of these. If you've ever used a spreadsheet to track your budget or spending, that's data analysis. A data scientist is someone who uses data to understand the future. I mean, all science is essentially prophecy, using data from the past to predict the future, and that's what a data scientist does, such as using past sporting results to predict how a team will perform in their next game, or using last month's sales results to predict next month's results. And finally, a machine learning engineer— well, machine learning is a technique used by data scientists, so there can be some confusion here— but a machine learning engineer is someone who puts together various pieces of a technology pipeline to implement machine learning algorithms in a production setting. And by production setting, I mean it could be deploying a computer vision model to a self-driving car, or a recommendation model to an online store. Now the curriculum we're about to go through focuses more on the first two, Data Analyst and Data Scientist, though all three have many overlaps. In fact, all the materials you need to get started with the first two are available through Coursera Plus, though the last one, Machine Learning Engineer, since it's quite a new role in the tech field, some programs that Coursera offer such as deeplearning.ai are available separate to Coursera Plus, but there's more info about that in the Notion document. Again, we're focusing on a beginner-style curriculum in this video. Okay, really this time, let's cover what I'd recommend you to learn via Coursera to become a data scientist in 2022. And remember, all of these are covered in more details in the Notion curriculum template linked below. This curriculum outline is focused on learning to write Python Writing code for data science and machine learning. And it's broken down into 3 parts. Number 1, which is the beginner, which is what this video is covering. So the beginner path is full of courses I'd highly recommend to get started. Number 2, Section 2 is the bonus. So some extra materials to expand your skills if you've gone through beginner and would like to learn more. And then finally, number 3 is the advanced materials. Now these are the materials that require skill from the beginner path to get started. So I wouldn't recommend going straight into the advanced unless you've already got some experience with Python, data science, and machine learning. With that being said, let's get into course number 1, Learning How to Learn. Why this course? Well, if you're going to be learning online, you better learn how to learn. When I took this course, I thought, wow, I wish they taught me this at school. Or even university, because I realized if you learn how to learn, well, you can learn anything. And learning Python, data science, and machine learning is fun, but it can be tough sometimes. So if you could learn techniques on how to learn better, why not? Inside the course, you'll learn concepts and ideas on how to approach learning a new subject— not just coding, by the way, any new subject— techniques such as focused and diffused thinking, Now, I recommend this course for anyone starting out a new learning journey, regardless of the topic. Number 2: The Python for Everybody Specialization. Why this course? Well, it'll help you learn Python. But why Python? So, Python is a general-purpose programming language, meaning you can build almost any kind of software you can imagine with it. It's known for its easy-to-get-started nature, and it's also the language of data science and machine learning. Many of the largest companies in the world, such as Google, Meta, Tesla, and Apple, choose Python to handle their data science and machine learning workloads. The Python for Everybody specialisation is for absolute beginners and has no prerequisites except having a computer. Inside, you'll learn how to install Python on your computer, how to write your first Python program, and various Python data structures. Now don't worry if you're not sure what a data structure is. You'll find out inside the course. Number 3, the Applied Data Science with Python Specialization. Why this course? Well, since Python is a general-purpose programming language, there are many things you can do with it. But now that you've got a baseline understanding of Python, it's time to start writing data science-specific Python. The Applied Data Science with Python Specialization was the first course I took on implementing data science techniques with Python. And I still use the things I learned from it almost daily. Inside the course, you'll get hands-on with the Python Pandas library for processing, analyzing, and manipulating data; the NumPy library for numerical computing— lots of data science and machine learning is dealing with numbers; the Matplotlib library for visualizing data; and the scikit-learn library for implementing various machine learning processing techniques and machine learning algorithms. Number 4: The Machine Learning Specialization from the University of Washington. Why this course? After learning Python and various data science libraries, if you'd like to keep diving deeper into one of the most powerful data science techniques, machine learning, this is the specialization to do so. Inside, you'll learn about some of the major areas of machine learning, such as prediction, like predicting the sale price of houses, classification, like classifying whether or not someone is going to have dementia, a number of years based on medical records, clustering, like grouping together customers with similar interests, and information retrieval, like collecting information from different documents. All of this will be done with the Python programming language, just like the previous courses. And as a special mention, I'll also recommend Andrew Ng's and Stanford's classic machine learning course here. It's fantastic for the theory and ideas behind many machine learning techniques, and there's a reason why It's one of the most popular courses on the internet. However, the code exercises use MATLAB rather than Python. So although I would recommend checking it out, if you're looking to stick with Python, perhaps you could reproduce the exercises with Python instead of MATLAB. Number 5: Mathematics for Machine Learning. Why this course? Machine learning algorithms and code use math under the hood to find patterns in data. If you'd like to learn the math behind the code you're writing, this course will help you with that. And some people get scared when they hear math, but you shouldn't. Math is the language of nature, and many of the fundamental math concepts that power machine learning, such as linear algebra and multivariate calculus, you might have already covered in high school, except now they have a purpose powering the algorithms you're writing rather than just being assigned as homework questions from a high school textbook. Alrighty, let's get into the bonus round. We've got Bonus #1: Google's Introduction to Git and GitHub. Why this course? Well, this is the number one extra, the number one bonus course I'd add on to the beginner steps. Git and GitHub are two of the most important tools when it comes to writing software. Git helps you keep track of changes to your code, and GitHub is a tool that helps you collaborate writing code and shipping software with others. Alongside Python, the language itself, I use Git and GitHub almost every day. For the past 3 years, all of my biggest projects have been shared straight out of GitHub. This course will get you familiar with Git and using GitHub to do things like make pull requests, which is GitHub speak for contributing your code changes to someone else's existing code. Jingle jingle, it's time for bonus! Bonus number 2: Read "How can a beginner data scientist like me gain experience?" Why this? Well, once you've gained some fundamental skills, like going through the beginner path you've just gone through, chances are you'll want to use them, either on your own projects or to get a job in the field. This article puts together a few things I'd do if I was looking for a job or to get experience in the data science field. The main one being start the job before you have it.

Alrighty, we have covered a whole bunch of a 2022 Coursera data science curriculum to learn the foundations of data science and machine learning with Python using Coursera. And I've referenced the Notion curriculum template a whole bunch throughout the video.

So now let's, let's take a tour de Notion.

I'm not going to go through every single bit in here. I'd rather you duplicate this. So when you click on the link, there should be— because this is different because it's on my screen— there should be a button up in the top right that you can duplicate this and copy it to your own computer or own Notion account.

And then you can play around with it and customize it yourself.

But this is just an example layout of how I might put together a data science and machine learning with Python curriculum in Notion. It's a beautiful, beautiful piece of software.

But let's get started.

Here I've got the date started. I've got today. You can do at today, or maybe you start tomorrow, or because I've got next week, next Wednesday. Beautiful. But I'm going to leave it as today. And then you've got a date to go through. At the top of all of my Notion documents, I like to put just a few sentences of what's going to happen. And then next we have a table of contents. So you can put in a table of contents. Click that. And what a table of contents will do— I'll actually delete this one because I've already got one— it will turn all of these headings. Let me just show you how to make a heading. We can go Heading 2, and then we've got Test Heading. But really the most powerful thing in Notion is the slash for commands. Again, I'm not going to go through all of these. Notion have got some great documentation on what you can do.

Essentially, you can make any kind of doc or document that you can imagine.

There's a whole bunch here that even I don't know. I'm just going to show you the ones that I use most often. And you see how test heading just appeared there. And then we can jump to to-do, we can jump to curriculum overview, we can go back up curriculum outline, etc., etc. So let me get rid of that. The status, I like to put at the top of my Notion documents to say, what am I currently doing? Well, as of now, we're currently putting together an outline of what I'd like to learn in 2022. Then we've got a to-do list. Here I also like to put the, the first front and foremost to-dos of what to do. So number 1 is choose a curriculum course outline. And by the way, you can create a to-do by going /todo. So let's just test that. Show off the to-do. Click. Done. Sign up for Coursera Plus. So if you want access to all of the courses that we've mentioned in this video and that are going to be listed below. Coursera Plus gives you access to all of them. There's Complete Learning How to Learn, Complete Python for Everybody, etc., etc. And then here, I've got a little bit of a visual curriculum overview. I don't know if you're like me, I like to do things in text as well as visual. So we drop down here, got a little guide. I've spoken about all of these things in the video, so I'm not going to reiterate them. But The Notion document, as I said at the start, is where all of the content from the video came from. So here you can see the curriculum focus. We've got the key. You can change the color of text to make it look all pretty. How about we change this? What's your favorite color? How about yellow? That's just an example, right? And then we've got a little callout. If you want to make a callout with the light bulb there, you can go callout like, hey, 'You look cool.' 'Thanks, man.' And then we got down here, this is a little flowchart that I made using Whimsical, which is again a visual representation of the curriculum that we've just gone through. We've got the beginner steps here, learning how to learn. This is just an image. So if we go enter here, image, image. Again, the slash, the forward slash command is going to be your friend. Have a play around with that. I can't stress enough how fun Notion is once you start to get into it. But if you want to have a look at the original version of this, it's online. I'll just bring this over here. There's a little bit more text here. I've just inserted the image without the text, but there's nothing here that we haven't discussed in this video so far. Let's go back to Notion. And now we've got the curriculum outline. We've got another key here. A little definition of what data science and data analytics— data science, using data to understand the future. Data analytics, using data to understand the past. Again, nothing different, just the layout. And what I've got— let's go enter. I've just put the— put all the courses we've talked about into a nice little table. You can add some rows here and delete that if you don't want it. But we've got course number, there's a course name, links to all of them. Why, what it covers.

We've covered all the things in the green rows, but if you want to explore the extracurriculum that I was talking about, we've got Bonus 2, Bonus 3, Bonus 4.

We've spoken about that one though. And then finally, there's the extracurriculum, which is down here. There's Andrew Ng's Machine Learning Classic Course, or what have I got here? Does this make sense? Andrew Ng's Machine Learning Classic Machine Learning Course. Well, it's a classic machine learning course, and it's also a machine learning course. And then there's some extensions. After you've gone through the beginner path, you may want to check out deep learning AI. Remember, this curriculum is Coursera and Python code central. So that's the theme of the whole thing. And then we go down here, curriculum tracker. Now this is probably one of my favorite features of Notion is that you can use and modify this board to track your progress. Every card is its own page in Notion too. And wonderful thing about this is that there's multiple views.

Look at this timeline. Oh my goodness.

So we've got the green rows here, these green rows, the beginner curriculum. We've got Learning How to Learn, Python for Everybody. And if we jump into one of these, let's go back to the board view. There's also list. Let's jump into the board. To create a table, by the way, or a board like this, you just go board, database, inline. This one will take a little bit of practice to get used to, but this is just an example. We jump into Learning How to Learn. We've got— see how this is its own page now? We've got a link, we've got a date that we might want to complete it by, and I've got some notes here. So once you finish part 1, you can check that off. Part 2, part 3, part 4. This is just a way to keep track of your progress and also the notes that you, you take throughout the course.

They're all going to be stored in the same place, tracking progress and notes.

So jump back in here. So once you've finished or started the Learning How to Learn course, you might put it to in progress. This is using a technique called Kanban. I'll let you look that one up. Essentially, it's just creating a board where you can move stuff from not started to completed. And then as you go through— let's give an example. We'll jump to this link, Data Science Python. Put this on one side of the page and we'll get this one on the other. Open it out as a page. Now if we go into the syllabus courses, we might go— here we go, let's write to-do just like we have with all the other ones. So we've got to-do, we do Intro to Data Science, the next one is Applied Plotting. I'm not going to type it out just to save time. Then you could do the same with course number 3. Applied ML, and then course number 4, Applied Text Mining. Getting typos here, but you see what's happening. Essentially what I'm doing is I'm taking the course structure, putting it into Notion, so that way when I do go through— when you do go through, because I've already been through this course, the Introduction to Data Science—

you can keep all of your notes here.

And so if you want to do code, you can do triple backticks. Look at that, we've got Python. So we go import pandas as pd. If you're not sure what that is, that's fine, you'll learn that when you learn pandas in here. And then maybe you go df for DataFrame equals— well, this is— we'll write how to create a DataFrame in pandas. and then you can write text in here.

This is how you create a data frame alongside code, and then you could even link back to the course.

So how cool is that? Oh, and just one more thing. This is the due date. You can have an end date. So if we change this, maybe you start on March 1st, and then maybe instead of finishing on April 30th, you want to take a little bit more time and finish on May 15th. Again, the beauty of creating your own curriculum online, the beauty of Coursera, Coursera Plus, you sign up, there's no, there's no time limits, so you can take it at your own pace. And so we go back to here. You see how we changed that date? Let's now view this as a timeline. I'm going to full screen that. See how it's moved over here now? I wonder if we can move that. We can! I didn't even know that. See, that's the beauty of Notion.

You, you, there's actually so many features in here that Wow, that's pretty cool.

So there's the curriculum. Look at that, starting in January 2022, going all the way through to the end of, or the start of September. You'd be a pretty darn competent data science and Python programmer by going through all of this. And then if we change it to list, well, you've got completed Learning How to Learn, completed Python for Everybody. Then you jump back into here, curriculum tracker. We've got notes, so you can link in here. Let me see if we can now link in. We go link to page, and I'm going to search for, um, Applied Data Science with Python Specialization. There we go. So that's going to link to this note in here. Boom, right back to where we were just creating some notes. Then we jump back into here, scroll all the way down. We don't have to because we had the table of contents, but Here's a log. How about we update this and go today, today explored Notion in brief to see how I can create my own curriculum. And this is a bit of a lie here. Tomorrow I start the Learning How to Learn course. Let's put that up here. Well, that's a whirlwind tour of Notion. If you'd like to know more, check out the Notion documentation. I'm gonna delete this even though it's probably the most truest statement in this entire video.

But, um, go to the link, you'll have the duplicate button there. Play around.

If you have any questions, let me know.

And as always, keep learning, keep creating, keep moving. I'll see you next time.