 Nowadays, loads of people want to be a data scientist, get to build cool models, carry out in-depth analyses, and get to work for a right range of businesses. However, with this increasing supply of people wanting to get into data scientists, it's making it very difficult to break into the field, not to mention the current climber in with all the tech layoffs that are happening. In this video, I want to go over several ways that you can make your data science application stand out in 2024. I truly believe that adding in this extra 20% of effort will get you ahead of 80% of other people. Let's get into it. Now, obviously, I'm quite biased because I have my own blog that I post quite frequently. But my opinion, having a blog or writing technical articles is by far one of the easiest ways you can stand out. Your blog hasn't got to be groundbreaking. If you look at mine, mine's pretty much just discussing introductory and fundamental statistics, machine learning, and data science concepts. I discuss probability distributions, Bayesian statistics, and the theory underlying most machine learning algorithms. Again, none of this is truly novel. I'm simply just documenting my understanding about certain topics online. I write mainly to learn, and I started by mainly teaching myself the basics. Only recently have I started dabbling a bit more complex topics such as convolutional neural networks. Anyway, having a blog is really good for your career. It serves a purpose for you to certify your understanding, improve your communication skills. It's like an online portfolio, and it even shows a self-starter attitude. You can literally write about anything. You can write about theoretical concepts like I do mainly, a project you've worked on, or anything in between. The point is, just get started, and then you can tailor your post and direction over time. I recommend you start on medium, because it's very simple to use. It already has an in-built audience and a large data science community on the platform. There are also more developer-focused platforms, such as Hash Node, or you can even go making blogs on your own website using things like Ghost or WordPress. By simply having a blog, you'll immediately be ahead of other data scientists, because most of them don't have one, not to mention all the skills you learn as a byproduct. Perhaps even an easier way to get started than from blogs is to enter Kaggle competitions. Kaggle will test your technical knowledge and get you to work on business-related problems. Kaggle literally has a leaderboard for each competition, ranking you against other data science and machine learning practitioners, so you know exactly where you stand. If you are a Kaggle grandmaster, then you'll definitely stand out, although it does take a long time to get to this stage, and you also need to know about the tips and tricks on the platform and how to do well on it. If you do enter competition, I wouldn't go in expecting that you're going to win some money or even a gold medal, but if you do, that's great. To get good at Kaggle, there's a lot of time and a lot of practice, so don't worry about where you rank right at beginning. The competitions vary in terms of the domain that they cover in data science. It ranges from computer vision to time series forecasting, so you can tailor the competitions you enter to the field you want to specialize in and learn the most about. Any competition that you do enter, you can then write a blog post about it after. You can discuss the things you learned, how you solved the problem, and any challenges you had. This way, you're killing two birds with one stone by having a blog post and also a Kaggle competition under your belt. While having all these projects and blog posts is a great idea, it's important to have them under one centralized location, and the best way to do that is to have an online portfolio or website. These are some of the things I would add to your website. A short autobiography about your skills and experiences, any blogs or articles you've written, lectures, videos, or presentation you've done at conferences, and any projects you've also worked on. These are obviously suggestions and your portfolio or website can literally have anything you want on it, but make sure you tailor it towards data science or machine learning themes, if that's what you're applying for. Like with the Kaggle competition and the blog post, having a website that showcases your abilities as a data scientist shows that you're really interested in the field and that you're really keen about learning and also developing your skills. It shows us all in a tangible way, which is something employers love to see. If you want a more physical representation of what a website could look like, on the screen now I'll show you mine. It's very basic, but I'm planning on revamping it in the next few months. I built it using GitHub Pages, which is a platform provided by GitHub that allows GitHub users to make websites completely from free and it's very simple to use. There are many online tutorials detailing how you can make your own website using GitHub Pages. But the one I followed was this blog post by Eugenia Anelo. It's very simple and it's about 10 minute read, so I really recommend this if you want to implement your own portfolio using GitHub Pages. I'll link in the description below so you can check it out. Obviously, GitHub Pages is just one way of making a website. There are literally so many methods out there, whether you build up from scratch using HTML and CSS or you can completely go the other way and use no code solutions. I do eventually plan to make a video detailing how you can make your own website. But for now, this video linked on screen here by AdditAbdow is a great way to get started and learn all about all the different ways that you can make your website. This one is quite different and one that I haven't dabbled in too much myself, but I have seen several data scientists land roles through contributing to open source. Open source is basically public code that you can modify, edit, distribute under a certain license. To contribute to open source, you can start very simple by just fixing bugs or even typos and comments of certain libraries, and then you can slowly build complexity from there. You can also build your own library to solve a certain problem. For example, Pedram built this library that solves the traveling salesman problem using the two-opt algorithm. By writing open source projects like this, you're not only developing your technical and theoretical knowledge, but you're also improving your software skills. If you want a full breakdown of how you can contribute to open source, I really recommend this free co-cap article. It details everything you need to know right from the beginner stage to how you make expert level commits to big libraries. Now, this last one is kind of harder to do, particularly if you are not a research student or studying data science or related subjects at a degree level. However, presenting at conferences is a great way to network, get your name out there and also showcase your skills. Most data science programs will have some sort of industry placement or research here where you can do some novel work or business related activities and you normally get to present your findings at the end of that placement or year. I highly recommend you take advantage of this if it's offered to you. Presenting your work is such a great way to network and you never know who might be sitting in the audience listening to your presentation. Not to mention it will develop your public speaking skills and your presentation abilities. If your research is part of a wider project or in itself is so novel, you may get a chance to publish it in some top journal. If you are going for research positions in data science or machine learning, then publishing in top research journals is by far the best way that you can stand out. I do appreciate that this one can be out of control, particularly if you're going down the self-taught room. Hence why I put a last on the list. Now, of course these are just a few ways to make your data science application stand out and you may think of others that may benefit you more. It is important to note that doing these things won't immediately make you land your dream job within a few months, but they will give you a slight edge over other applicants. Nevertheless, I recommend you try out at least one of these things and see how it goes for you. You never know, you might fall in love with writing, doing chemical petitions or even presenting at conferences. If you enjoyed this video I want to see more content like this on this channel and make sure you click the like and subscribe button and I'll see you in the next one.