 In this video, I'm going to go over seven subscriptions that help supercharge my productivity, focus, and learning as a later scientist. Let's get into it. I'm sure this comes as no surprise, but the number one subscription I recommend is ChatGPT. Even though there's a free version, I think paying that extra $20 a month to get GPT-4 is a really good return on investment from the regular GPT-3.5 that ships with the free version. Also, the premium subscription allows you to access ChatGPT during high periods, and you also have access to Dali and advanced analytics if that's something that you really want. To be honest, though, I'd happily pay the $20 a month just for the conversational interface. I pretty much use ChatGPT every single day in various formats. In my day job, I use it to help me write unit tests and do complex pandas queries. As part of my YouTube channel, I use it like a brainstorming partner to research new ideas, come up with titles, and just general kind of a creative tool that can 10x my productivity when it comes to creating content. It has also been an essential kind of study partner when I'm trying to learn data science. It's very helpful when you really want to break down a problem. For example, when I was learning recurring neural networks, I really wanted to understand exactly the flow of data, and using ChatGPT, I can work through basically worked examples, and I can see exactly the numbers going in, the activation functions, using back propagation through time, and having this step-by-step guide where I can interact with basically my own teacher. It's something that I think is really essential and also useful for any data scientist to have. It's like a pocket tutor, and it's only $20 a month. Now, in my opinion, I think most data scientists should have some sort of online presence or online portfolio that can be either a YouTube channel, a blog, newsletter, Twitter account, literally anything where you can showcase your knowledge and what you know about a subject, and just any opinions you may have on the field. However, I think by far the best way is just to have your own personal website of your own custom domain. Now, if you don't know what domain is, it's basically just a web address like eaglehow.com, google.com, medium.com, that points to a certain IP address where you're basically your website's hosted. You can purchase these domain names through different companies, and these companies are known as something as registrars, and they're basically just verified companies by ICANN, which is kind of like the government or the body that governs the internet, that allow you to sell domain names on their behalf. And so you could go to all these websites, I know Google has them, Cloudflare, AWS, loads of big companies sell domain names and a lot of them are registrars, and you simply just purchase them. I bought eaglehow.com for about $14 from Google Domains a couple of years ago, and like I said, it's only $14 per year, so it's really worth the investment, even if you don't think you need your domain name at this time, it's something that may come in handy in the future. And it's so cheap, there's no reason why you shouldn't have it before someone else may snap it up. If you are a practicing data scientist, then chances are your company probably deploys some of its data or products on cloud systems like AWS, Google Cloud, or Microsoft Azure. Having a personal account on one of these platforms, I think is very worthwhile because it allows you to learn more about it, play around, and basically just understand exactly how most software is deployed nowadays. Even though the price for most of the services and products is relatively cheap, remember to stay vigilant because you don't want to get a big cloud computing bill come the end of the month. I personally use the AWS free tier because well, it's free. And there's a lot of services that you can play around with, with no extra cost, and they're covered by some initial limit that AWS sets. I think knowing software engineering principles and cloud computing is a really useful skill to have the data scientist, because oftentimes, just because you simply write an algorithm doesn't mean it can be immediately set to production. You need to understand the steps taken. And to do that, like I said, you need to know software engineering, and basically this cloud systems on how you deploy your algorithm. There are many learning platforms out there to learn data science and machine learning using me and data camp come to mind. But my opinion, Coursera is probably the best, mainly because of Andrew Ung's machine learning and deep learning courses. These are the first courses I took when I was learning machine learning data science. So I can't recommend them enough. You can purchase these courses individually, or you can get access to a Coursera plus account, which is 46 pounds, but gives you access to 7,000 plus courses each month. The plus subscription is really useful if you plan on taking loads of courses, whose individual value sum up to be more than the monthly subscription. However, be careful because not all courses are covered by this monthly fee. Either way, I think having a subscription fee for a course platform, or even posting a course can be seen as a good thing, because it basically incentivizes you to complete the course because you've paid for it. As a writer on Medium, I can't not recommend a Medium subscription. Medium is a great platform to learn data science and also network with other practitioners in the field. It has a really big tech community, and one of the biggest publications is towards data science, which basically sets it all. To read all the articles on Medium, costs $5 a month, or $50 if you're paying for the year. Some people may argue that a lot of the data science information on Medium can be found elsewhere on the internet for free. Well, this is probably true. I think that kind of misses the point. I personally really like that human element of someone teaching me for a blog because it's a lot more fun, and sometimes the explanation is a lot clearer and more in layman terms than simply googling something on Wikipedia, for example. I also quite like the recommendation Engine on Medium. My homepage is curated of a lot of interesting things, and there are also topics which I don't really know much about, but they pop up on my feeds, and I read them like 10 minutes, and I learned something new, which is really, really useful. Now, I'm sure most of you use Spotify already. It's pretty much a global thing used by nearly every single person, but how much do you use Spotify, particularly for data science, or data science studying? I often use one of Spotify's Music for Concentration playlist when either doing some coding work, writing a blog, or editing a video. I find that soothing piano or instrumental jazz really boosts my productivity and makes me feel a bit more inspired when doing my work. I think there is some scientific evidence behind using just instrumentals or, like I said, kind of that classical music when you're working, but regardless, even if there wasn't, it helps me, so I just stick to it. Another reason why Spotify Premium really benefits me is that I listen to a lot of podcasts, and most of my podcasts are focused around the business, AI, and tech side. If there was an ad break every 15 minutes, this would really annoy me and also take me out of the zone of really trying to concentrate and comprehend what the people on the podcast are talking about. And the final one as we're on this platform is YouTube. YouTube Premium is really useful because I don't want to, you know, every other video have an ad in the beginning in the middle of my video when I'm trying to learn something new. YouTube is a bit more of a personal one because, well, I use it for a lot of leisure time, but I also use it a lot to learn data science. I subscribe to many tech-based channels, my favorites being Fireship, Yannick Kiltcher, and Two Minute Papers. These channels help me keep updated of all the latest going on in the field and also help me understand exactly the latest research through breaking down research papers. These are probably the most useful recurrent subscriptions that I use to help my data science career. I know some of them are a bit generic like Spotify and YouTube, but it depends how you use them. You can tailor them for learning or tailor them for leisure. I do a bit of both, but like I said, having these platforms where you can learn new things, kind of fruit osmosis is very useful. If you want to hear more from me, then I run a weekly newsletter called Edition of Data. I send it every Monday and it's all about my thoughts and experiences as a practicing data scientist. If that sounds interesting, I'll link it in the description below for you to check out. If you enjoyed this video, make sure you click the like and subscribe button and I'll see you in the next one.