Most best laptop for data science videos are made for professionals, not for the students that are just starting out. But here’s the thing, what you need as a beginner is completely different from what the full-time data scientist uses. So, in this video, I’m breaking down the best laptop for data science students in 2025. Before I show you the laptops, here’s something more important. A lot of people buying a GPU laptop only end up using Excel or PowerPoint. Others buy a basic system and regret it the moment they start working on ML projects that crashes mid-range. So ask yourself, am I just trying to pass college? Then go with a reliable, affordable machine. Use the cloud. Don’t overthink it. Or am I serious about this fit? Then get a laptop that gives you freedom to train models offline to breed late at nights and to go beyond what the slavers teaches. All right, let’s start with the budget end of the lineup, the Asus Vbook Go 14. and it’s packing a surprising amount of value for the price. You’re getting an AMD Ryzen 5 7520U CPU, integrated Radon graphics, 16 GB of LPDDR5 RAM, and 512GB of SSD. All of that under 40,000. And yeah, that’s entry-level pricing, but with mid-range performance where it matters. If you are just starting out in data science, learning Python, working in Jupyter notebooks, cleaning data with pandas, making basic visualizations, this laptop checks all the boxes. the 16 GB of RAM plus SSD combo, it keeps things snappy. You can have multiple notebooks open, 10 Chrome apps running via code in the background, and it doesn’t choke. Now, let’s address the obvious. No dedicated GPU. But here’s the truth. In your first year, you won’t need one. So, don’t get distracted by high-end specs you won’t use. Focus on your skill, and this laptop will keep up while you grow. Now, if you’re ready to go beyond the basics, the Acer LG priced at 55,990 brings solid performance without breaking the bank. You’re looking at 12th generation Intel Core i5 450H CPU, 16 GB of DDR4 RAM, and a fast 512 GB of SSD, and most importantly, an Nvidia RTX 2050 with 4 GB of V RAM. That’s right, actual GPU acceleration under 60,000 for that’s a huge win. Now you can train models locally, run TensorFlow, experiment with PyTorch, build deep learning projects without relying on collab cues. You are not training NLMs here. Let’s be clear. But for image classifications, CNN’s basic vision task or larger data sets, this setup holds up. And it’s not just about this speed. It’s about learning how real GPU workflow function. Something you won’t fully cross with cloud tools alone. So maybe you like what the Acer ALG had to offer, but you want something that feels a bit more refined. That’s where the HP Victas steps in. 459,500. It comes with the same 12 generation Intel Alpha Tile 450, 16 GB of DDR4 RAM, 512GB of SSD, and an Nvidia RTX 2050 with 4GB RAM just like the Acer. So on paper, same performance. But here’s the difference. The Victors just feels better. You get tighter design, better thermals, things like a smoother keyboard, more responsive trackpad, and quieter fan under loot. It’s not just about specs, it’s about day-to-day experience. So, if you are ready to go beyond just learning and start building real ML projects on your own machine, this is where the game changes. This higherend version of the HP Victas priced at 67,990 comes with the AMD Ryzen 5865HS, 16 GB of DDR5 RAM, and a 512GB SSD. and the real star an Nvidia RTX 3050 with 6GB of VRAM. Now that 6GB VRAM, that’s a big deal. It’s a solid step up from the RTX 2050 we looked at earlier. You will feel the difference when you are training models, especially if you are working with image classifications, deep learning pipelines, or data set heavy projects. The Ryzen 5 8645HS is from AMD’s series. Built for sustained performance. Whether you are running Jupyter notebooks, multitasking across Chrome, VS Code, Collab, or doing light video editing on the side, this CPU handles all of it without breaking a sweat. And then there’s the 16 GB of DDR5 RAM. It’s not just about the capacity. DDR5 is faster and more efficient than the DDR4, which means better performance when juggling larger data sets or keeping multiple apps open during a long session. Now, maybe you just want something quieter, lighter, and more productivity focused. And that’s exactly with the Assus Vbook 16 comes in. It’s priced at 72,000 and it’s powered by the Intel’s new Core Ultra 5225H processor paired with the integrated graphics, 16 GB of LPDDR5 RAM, and a 512GB of SSD. Now, let’s get the obvious out of the way. There’s no dedicated GPU here. And that’s not a downside. It’s just knowing about what this machine is built for. If your work revolves around Jupyter notebooks, Python scripting, data cleaning, pondas, mattplot lip, all of that runs smooth. And if you’re using cloud platforms like the Google Collab or Kegel, which let’s be honest, most students rely on anyway, you’re not missing out. You don’t need a heavy GPU just to write good code or test models in the cloud. So if you’re a student who wants a reliable future ready laptop under 75,000, something clean, modern, and built for cloud first workflows, this Vbook 16 just makes sense. If you are someone who prefer Mac OS, the MacBook Air M2 is one you should definitely have on your radar. It’s priced around 85,000 and comes with the Apple’s M2 chip featuring 8 core CPU, 10 core GPU, 16 GB of unified memory, and 256 GB of SSD. And look, let’s be real, 256 GB of SSD fills up fast. Install Python, a few libraries, maybe setup anaconda or Docker, and that drives start disappearing. So, if you are considering this machine, make a budget for external SSD from the day one. But once you pass that, the actual experience it’s insanely smooth. But that’s one thing that doesn’t get talked about enough. Mac OS has limitation for some data tools. You can’t run PowerBI desktop which is dealbreaker for certain analytic workflows. Some Windows only utilities like the SQL tools or Excel plugins are also off the table. Sure, you can run walkounds like parallels cloud desktop, but that’s not really what a MacBook Air is built for. But if you are ready to move and start building real models on your own machine, enter the Lenovo lock powered by the Intel Core i5 13450 HX and paired with an Nvidia RX 450 GPU with 6GB of V RAM. You’re also getting 16 GB of DDR5 RAM and 512 GB of SSD, fast memory, faster storage, and a seriously pin performance. This laptop gives you workstation class power without needing a full tower setup. And yeah, it does cost more than the entry-level options we talked about earlier. But if you are serious about machine learning, building advanced projects, or prepping for the future in AI development, it’s the kind of performance that don’t slow you down. It actually keep up with your ambitions. There’s one more machine we need to talk about. The MacBook Air M4 is probably the cleanest experience on this list. Upgraded 10 core CPU, 8 core GPU, 16 GB of unified memory, and 56GB of SSD priced at 97,000. And yeah, that’s not cheap, but you are paying for more than just a spec. You’re paying for the Apple experience. No fans, no lag, and battery life that doesn’t care if you forget your charger at home. For most A IML students, it hits the sweet spot. You can easily get through an entire day on campus or in a cafe without touching the charger. That said, this isn’t a perfect machine for everyone. It’s also important to know when Mac OS draws the line. If your coursework uses PowerBI desktop, you’re out of luck. It doesn’t run natively. You will be stuck with the web version, which is okay for basic testboard, but not as powerful. So, that’s it. Now you know exactly which laptop to buy and what to look in a laptop as a data science is ranking. If you want to check out some of the best options I recommend, then you can find the Amazon’s best buy link down below in the description box. If you buy through those links, it won’t cost you anything extra, but it will really support the channel and let me keep making these videos for you. And don’t forget to hit that like button and subscribe if you haven’t already. Got any questions or want me to cover any specific topics? Drop a comment down below in the comment section. So that’s it. Thanks for watching. Stay awesome and I will see you in the next one.