8 Best Laptops for Data Science for Data Analysts in 2025

You’ll likely need a fairly powerful device to become a data scientist. Data analysis tasks will need a powerful laptop that doesn’t compromise specs.

I’ve also included some more approachable models here if you’re a student who needs this for your undergrad or master’s classes. While a beastly device is nice, it’s not a must!

Read on to learn more!

The 8 Best Laptops for Data Science Tasks

Here are some of the best laptops for data science! I’ve included a mix of gaming laptops, workstations, and ultrabooks for all scenarios.

1) Best Overall – HP ZBook Fury G9

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Features:

• 12th generation Intel core i7
• Variable GPU
• 32GB of RAM
• 16-inch WUXGA display (1920×1200 resolution)
• 256GB-2TB of internal storage space

The best laptop for data science and other number-heavy tasks is the HP ZBook Fury G9, an extremely beefy laptop with a powerful processor to match!

This laptop’s Intel core i7 chip has 16 cores that can run countless calculations efficiently and quickly. Just don’t expect a good battery life from this laptop!

As a mobile workstation, this laptop is designed to run numbers and output whatever you need, whether you use it for data science or 3D modeling.

One interesting thing about this laptop is its GPU: It comes out of the box with Intel UHD graphics, but it can be outfitted with an AMD Radeon Pro 6600M or an NVidia Geforce RTX A-series card.

The Fury G9 isn’t a budget option, so ensure an accommodating budget before you get this hefty machine. You can pick it up here.

PROS

• Available with different graphics cards
• Excellent selection of USB ports
• Upgradable memory and storage

CONS

• Very pricey for a laptop
• The lightest configuration is 5.4lbs

2) Best Value – Lenovo Legion Slim 7

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Features:

• AMD Ryzen 7 5800H
• NVidia GeForce RTX 3060 Max-Q
• 16GB of RAM
• 15.6 inch 165Hz screen
• 2TB of storage space

My favorite thing about the Legion Slim 7 is its aspect ratio. At 16:10, I could see more text at a glance and didn’t have to worry about black bars bordering the screen.

It doesn’t have an Intel Core processor, but the 8-core Ryzen 5800H within will help you run your models in a zip.

One small issue I ran into is the power limits. While the Slim 7 packs a powerful processor, its graphics card has a lower wattage than its non-Slim cousin.

The power limits mean a lighter charger and a slightly extended battery life. However, expect your estimated battery life to shorten once you begin running any models while unplugged.

Sacrificing a small amount of power for much more portability, the Legion Slim 7 is a thin but capable laptop for data science or leisure. You can get it here.

PROS

• Full keyboard with a number pad on the side
• Large, well-placed arrow keys
• Windows 11 operating system

CONS

• It lacks an HDMI port
• A smaller battery configuration doesn’t have extra slots

3) Best Budget – Lenovo IdeaPad 3i

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Features:

• 12th-gen Intel Core i5
• Intel Iris XE graphics
• 8GB of RAM
• 15.6-inch FHD Display
• 512GB SSD

Lenovo’s known for making affordable notebooks and top-notch ultrabooks. The Ideapad 3i is more of the former.

I recommend using this device if you’re offloading development to a cloud service because it isn’t built to do everything locally. But don’t think I’m discounting it!

One thing I liked about the Ideapad 3i is its long battery life; at 7 hours per charge, it’s got comfortable and lengthy battery life.

However, this card falters for data science because it doesn’t have a separate graphics card. Iris XE graphics won’t give you too much computing power, sadly.

But this is still a decent productivity laptop, so it’s worth it if you’re doing light work! You can get it here.

PROS

• Very affordable
• Upgradable memory
• Lightweight and portable

CONS

• No separate GPU
• The display is a little dim

4) Acer Swift X Creator Laptop

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Features:

• AMD Ryzen 5800U
• NVidia GeForce RTX 3050 Ti
• 16GB of RAM
• Windows 10 operating system
• 512GB SSD

At a slightly higher price and greater capabilities comes the Acer Swift X. This laptop’s an Ultrabook, even if its screen is a little smaller.

I liked the Swift X’s HD display, which was nice and clear during my testing period. Unfortunately, I can’t say the same about the keyboard, which didn’t feel good to type on.

Moving on to the GPU, I was pleased that Acer packed this with a low-powered but still dedicated GPU. The 3050 Ti here will help with both leisure and work.

I also got 8.5 hours of battery life from this during my tests, which should be enough for a full day on the job.

The Swift X is a value-focused laptop with a decent selection of USB ports and the right features for data science tasks. You can get it here.

PROS

• Fingerprint reader for security
• NVidia GeForce RTX card
• Good battery life

CONS

• Weak speakers
• The touchpad is easy to activate accidentally

5) Dell XPS 17 9710

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Features:

• 11th-generation Intel Core i7
• NVidia GeForce RTX 3050
• 32GB of RAM
• 17-inch 16:10 screen
• 1TB SSD storage

The XPS 17 is a fine Dell laptop for high-powered executives or running data analysis work. I’m particularly a fan of the screen, which was a joy to use.

You’d be forgiven for mistaking the XPS 17’s screen for an external monitor when glancing at its size and brightness. Make no mistake; this is a desktop-replacement laptop!

It’s premium in price and parts, but Dell designed this laptop for serious work. That leads me to one issue: The ports on this machine are very limited.

In a strange design choice, Dell packed the XPS 17 with only USB-C ports and 1 SD card slot. You’ll need a dongle for more ports.

Great for viewing documents and code, the XPS 17 promises power and clear visuals. You can get it here.

PROS

• Excellent display and aspect ratio
• 500 nits brightness
• Large and responsive trackpad

CONS

• Acceptable battery life
• Poor selection of USB ports

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Features:

• AMD Ryzen 7 4800H
• NVidia GeForce RTX 3060
• 16GB of RAM
• 17.3-inch FHD display
• 512GB SSD

I’ve got a soft spot for the Strix series because they’re middle-ground devices that punch above their weight class.

The G17 is a large and fairly heavy laptop ideal for either running data analytics or downtime.

I don’t particularly appreciate that it lacks a webcam, which has been an Asus-specific issue for a few years. However, it’s got a strong battery and ran everything I threw at it, so it’s alright overall.

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PROS

• Powerful GPU
• Large and well-lit IPS display
• Strong battery life

CONS

• No webcam
• Can get hot under load

7) MacBook Air

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Features:

• M2 CPU and GPU
• 13.6-inch display
• 8GB of RAM
• 512GB SSD

Despite its prevailing image as a laptop for creative work, the MacBook Air is perfectly capable of crunching heavy numbers and getting data science work done!

This laptop can run the gamut, whether you need to watch movies or run data visualization models. The Air is also fully equipped for meetings and remote work, thanks to its excellent webcam.

Apple perfected its communications tech while ensuring the Air is stuffed to the gills with power. A standard Air will also have Apple’s signature almost-obscene battery life.

Since macOS is a Unix-based operating system, you won’t have trouble with your tasks unless you need to use a Windows-exclusive program like PowerBI!

Supremely portable, powerful, and versatile, the latest Macbook Air remains a strong contender. Get it here.

PROS

• Light yet powerful
• Crystal clear webcam
• Touch ID for more security

CONS

• Fewer ports
• Pricier than the old model

8) Lenovo Thinkpad T14

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Features:

• AMD Ryzen 5 Pro 6650U
• AMD Radeon 660M
• 16GB RAM
• 14-inch display
• 256GB SSD

The T14 is another mobile workstation that’s perfect for data science work! It hits the sweet spot with a 16:10 aspect ratio display and a nice 14-inches in size.

Right-handed data scientists will love this laptop because it’s port-heavy on the left side, so your right side will be clear for just a mouse.

Lenovo’s managed to keep their latest T14 cool under load, but it hasn’t helped extend the battery life. I only got roughly 4 hours of use on a full charge, which was light browsing.

Fortunately, the laptop has retained a premium feel with its magnesium chassis, so the T14 has a solid feel. You can get it here.

PROS

• Stays cool under load
• Tougher chassis than Gen 2
• 16:10 aspect ratio

CONS

• Poor upgradability
• Stock storage is small

The Specifications to Look For in a Laptop for Data Science

If you plan on running data science projects on a laptop, here are some general data science laptop requirements you should keep your eyes peeled for.

CPU

Whether you want data analytics or machine learning, computing speed is king. If you’re sticking with Intel chips, I’d recommend getting at least a 9th-generation Intel core i7 processor.

Get an H-series Intel core i7 or i5 processor. Unlike the K and U series chips, these processors deliver high performance within a reasonable threshold.

If you prefer AMD, look for a Ryzen 3 or 5 H-series processor with at least 6 cores. Another reason to go for AMD is the extra cores and a (generally) more attractive price point!

TIP: Check the programs you’ll use and whether they benefit more from single-core or multi-core performance. Then cross-check whether your chosen laptop’s performance matches your needs!

GPU

Sometimes the computing power of your CPU isn’t enough, and you’ll need to spring for a discrete graphics card to make up the difference. Fortunately, NVidia and AMD have lots of options!

If you’re looking at standard consumer laptops, you’ll do well with an NVidia GeForce RTX 3050 up to a 3080 Ti. That’s what’s commonly available, and they’re all powerful mobile cards.

Some data science programs like TensorFlow and PyTorch now have increased support for AMD graphics cards, so you won’t have to worry about choosing between brands.

Regardless of the brand, ensure your chosen GPU has at least 4GB of VRAM, a high number of tensor cores, and high bandwidth memory.

A discrete GPU will let you access parallel processing capabilities and help you be more flexible! The computing performance it provides will help in training neural networks.

Display Size, Aspect Ratio, and Brightness

I firmly believe that display quality is an important trait of any laptop, whether you use it for simple daily tasks or coding for hours.

When getting a laptop, spring for a device with an HD display and a resolution of 1920×1080. 16:9 aspect ratios are more common, but get a laptop with a 16:10 aspect ratio if you can find one.

16:10 is the best aspect ratio for productivity, but they need to be matched by your display. I think the sweet spot for laptop displays is 15.6 and 17.3 inches for productivity and leisure.

You also need to avoid eye strain while sitting at your desk, so ensure your laptop’s bright enough to be used in a well-lit room!

250 nits and above is a good baseline, but 400 and above will mean you can use your laptop even in the bright and sunny outdoors. I recommend a brightness of 250-450 nits.

Storage Space

Capacity is crucial when working with big data sets, and more complex programs will consume larger and larger amounts of storage.

You don’t want to spend too much time waiting for your laptop to access data and pull it up during work or presentations, so you’ll need a laptop with SSD storage.

In my opinion, an SSD has to be at least 256GB total, and an external drive can make up the difference, but it changes if you need to store data locally.

Aim for a decently large storage capacity, like 512GB or even 1TB, so you can work with large datasets without worrying about your laptop slowing down.

I’d recommend getting a laptop with an extra SSD slot in case you need to expand your laptop’s storage in the future.

Battery Life

Sometimes you’ll have a day packed with presentations, and you’ll need to haul your setup from place to place. Without the time to charge, your laptop for data science should pack a big battery under the hood!

If you can’t find a laptop with a large battery, ensure your device has average battery life. A new laptop with a full charge should have a battery life of 7-9 hours.

This is enough for a full day of tasks and will help you save on weight, especially since you won’t have to bring a charger around.

If you’ve got a powerful data science laptop, don’t expect it to have a good battery life! Stronger laptop parts are a recipe for the opposite of long battery life!

NOTE: This range doesn’t consider brightness levels and the power draw of the programs you’ll be running.

RAM

Any budding data scientists will eventually ask, “How much RAM will I need for my data science tasks?” Data science work is resource-intensive, and that includes your RAM.

Any personal data science projects you pursue outside of work or classes will require an absolute minimum of 8GB. I recommend sticking to this spec as a non-negotiable necessity.

For a smoother experience, a good data science laptop should have 16GB of RAM. That’s also slowly become the standard for decent computers in the last few years.

However, more detailed data analytics will typically be performed via a cloud-based environment like Jupyter or Google CoLab.

If your work requires extremely complex programs, your RAM will become irrelevant. You’ll be fine if you have a device with 8GB of RAM minimum and a decent keyboard and display.

It’s also possible that your work will require everything to be performed locally. If you run intricate machine learning programs without a cloud program, I suggest investing in a device with 32GB RAM.

Portability

If you’re a data analyst on the go, you’ll need a lightweight but powerful device. However, a laptop with enough processing power for your needs will typically also be heavier.

Data science laptops tend to be workstations or gaming-focused, so you can expect them to be fairly heavy. Aim for 2-2.5Kg without the charger as a middle ground.

I’ve listed a few ultrabooks and more portable options here to offset the trend. If most of your data analysis work is offset to the cloud, you can prioritize mobility over power in your laptop.

Laptops for data science with smaller screens and lighter chassis will be within 1.5-2Kg, which will help if you’re moving around giving presentations or attending different meetings.

Extra Features

Depending on your branch of data science and your employer, you may need a more secure laptop. I’d recommend investing in a device with a fingerprint scanner to keep your work under wraps.

Connectivity is also a bonus in any data science laptop worth its salt. I’d recommend getting a laptop with multiple ports, even if you do most of your work through a cloud program.

If you’re unsure how many external devices you’ll need to connect to your data science laptop at once, ensure it has lots of ports.

Good data science laptops should cover the basics: 1 Ethernet port, 2 USB A ports, and a USB C port. Your device should also have an HDMI presentation port and a combination mic/headphone jack.

Data scientists who work with older tech may also need a VGA port for their devices, but you can also find dongles to compensate for any USB ports you lack.

If you’ve set your sights on a laptop model with a backlit keyboard, that may help if you plan on working in dimmer environments, but I don’t think it’s necessary.

Operating System

There isn’t a significant benefit to sticking with either Windows or macOS concerning data analysis work since technologies like Google Colab are available to make your life easier.

However, this will also depend on the kind of work you’ll be doing. Multiple programs like Excel and Microsoft Power BI are practically Windows exclusives.

If these two programs are essential for your work, you’re locked into using a laptop for data science with a Windows operating system.

There are also your job limits to consider. While data scientists prefer to use macOS or Unix-based operating systems, your employer may ask you to stick with a Windows laptop.

While I recommend sticking with either macOS or Linux, you should match your laptop’s operating system with the data science tasks you need.

Some data science applications have much better functionality when used on a specific operating system, so keep that in mind when selecting your laptop for data science.

Tips and Tricks in Picking the Best Laptop for Data Science

Here are some easy-to-miss aspects that’ll help narrow your search for the best laptops for data science!

Prioritize Upgradability

Laptops for data science should be able to keep up with the demands of current trends and be usable in the future, too. Your current laptop’s specs may not be relevant a few years from now, so how can you keep up?

Via upgradability! I know what you’re thinking, “Laptops for data science can’t really be upgraded like a desktop computer can.“

That’s true, but it also helps focus your options into 2 categories: RAM and storage. Check if your data science laptop has extra memory slots and drive bays for additional upgrades.

You may have to contact the laptop’s manufacturer to find out, but I’d recommend getting a model with 1 extra RAM slot and 1 extra SSD slot.

A bit of extra RAM and a new SSD is often enough to make an old laptop run like it’s brand new. This will keep you running machine learning models forward into the future!

NOTE: Several laptop models have 1 soldered stick of RAM and 1 removable stick. If your data science laptop looks like this, ensure that any memory sticks you add have the same clock speed as the soldered one.

Opt for a More Comfortable Keyboard

I mentioned the potential benefit of a backlit keyboard earlier, but that’s secondary to comfort. Whether you’re entering the field of machine learning or data visualization, you’ll want to prioritize comfort.

Data scientists will rely on their laptops for practically everything, so you should ensure you’ve got a device that won’t make your hands cramp up during a long typing session!

Consider the size of your hands, and look for a device with a full keypad on the side. Regardless of your chosen field, data scientists typically look at many statistics.

If you’re sifting through and encoding large data sets, you’ll need those additional keys on your laptop.

Once you have more experience, you may opt for an external keyboard for longer work sessions. Or upgrade to a desktop if you handle extremely taxing data science work!

Don’t Forget the Touchpad

Aside from the keyboard, you may encounter situations where it isn’t feasible to have a mouse and pad setup alongside your laptop, so ensure that your device has an accurate and sensitive touchpad.

Data science professionals who want a smoother mouseless experience should opt for a device with a glass touchpad, like what a MacBook Air typically has.

While this will raise your budget a little, your laptop’s frictionless movement and impressive responsiveness will be worth it.

Tablets and phones have almost perfected the touchscreen space, but don’t forget that laptop touchpads came first! I recommend getting a laptop with a smooth touchpad.

I’m also a big proponent of clearly visible left-and-right mouse buttons, even if they may seem old-fashioned. Being able to see (and feel) the buttons you’re clicking will be better for your touchpad in the long run.

One more thing to remember; don’t get a touchpad that’s too sensitive! A data scientist whose palm accidentally brushes against the touchpad may cause the cursor to zoom across the screen.

Consider Your Laptop’s Size

Data visualization is difficult, but it worsens when using a tiny screen! You don’t want to strain your eyes by squinting at figures and code on a small screen.

Consider your preferred display size and whether it’s feasible for your needs if given a choice. Larger screens can display clearer and crisper images, which will help your eyes.

A larger display means a larger laptop and, thus, less portability. However, your portability will be easily matched in power and competence.

I recommend a 15.6-inch display at a 1920×1080 resolution as the sweet spot between size and portability. You’ll be able to see enough on the screen with ease.

Frequently Asked Questions (FAQs)

Learn more about what makes a good data science laptop here!

Does Data Science Require a Good Laptop?

Data analysis work is a multi-faceted field and requires a powerful device for efficiency’s sake. A baseline amount of processing power is preferable, but that’s true regardless of what you’ll be using your laptop for.

I recommend getting a relatively powerful laptop: A minimum of 256GB SSD storage, a 9th-generation Intel core i7 processor, and a discrete graphics card are all necessary.

The GPU will help take some load off your processor and allow you to run parallel computing to speed up your tasks. Everyone wants to save more time and be more efficient!

Heavy-duty tasks like TensorFlow will use your laptop’s graphics card for efficiency. Then there’s the SSD, which will have faster data transfer speeds than a spinning HDD.

How Much RAM Is Enough for Data Science?

Anyone serious about becoming a data scientist will need a basic amount of RAM to run machine learning tasks and perform data processing on their laptop.

The smallest reasonable amount of RAM for data scientists should be 8GB. If you want more room for error, move up to 16GB of RAM.

This 16GB can be further supplemented by a discrete GPU like an NVidia GeForce RTX 3050 or better. Since GPUs also have RAM, you’ll effectively have 20+ GB of RAM.

Can You Use a Gaming Laptop for Data Science?

Anyone who wants incredible performance coupled with mobility will look at gaming laptops, and data science tends to be quite resource-intensive.

Some of the best laptops for data science are gaming laptops since they usually have a decent amount of processing power and storage space for your tasks.

A gaming laptop is more appropriate for master’s students or beginner data scientists, as professionals will often be given access to their institution’s resources and licenses.

Is Intel or AMD Better for Data Science?

Even data scientists will run into the age-old debate of Intel vs. AMD. Regarding computing power, I cannot recommend either chipmaker over the other.

Neither CPU will outclass the other regarding performance, but you can look at the core count as a pretty solid performance indicator.

More cores will translate to a smoother experience in any task (not just data analytics) and generally make your laptop run faster.

However, I need to mention that any extremely heavy data processing tasks you may do will be offloaded onto a cloud platform like AWS or Microsoft Azure.

Summary

Here’s a refresher on my picks for the best data science laptops!

Best Budget – Lenovo IdeaPad 3i

Great for light projects and offloaded tasks, the Ideapad 3i is a slim and portable device with a comfortable keyboard and upgradability.

Best Value – Lenovo Legion Slim 7

The Slim 7’s a gaming laptop first and a data science laptop second, but it’s equipped for data scientists who want to do it all. Work and play in one package!

Best Overall – HP ZBook Fury G9

The best data analysis laptop is the HP ZBook Fury G9. You won’t have trouble with your data science tasks with the Fury G9’s customizable GPU and killer HD display!

Conclusion

Data science requires a certain measure of power if you want to be efficient and process data quickly, so it’s vital to have a laptop with enough grunt to run the programs you’ll be using.

Before picking up a data analysis laptop, I suggest determining your limits regarding programs, budget, and any job or class requirements.

Remember that the best laptop for data science-related tasks will depend primarily on YOUR needs and the data science applications you’ll be running.