The 5 Best Laptops For Data Science Reviewed

Data science is the beginning of all science.

Observation of all the available data, and the translation of that data into actionable information, has been the principle on which the likes of Copernicus, Galileo, Newton, Darwin, Lovelace, Curie, Turing, Hawking, and many others have made the discoveries which have shaped our world.

Can you imagine giving any one of them a modern 21st century laptop?

OK, Hawking, but mostly the point stands. A modern laptop enhances the scope of a data scientist’s work. It’s the ultimate abacus, and it can help modern data scientists do their job more efficiently, more effectively, and more thoroughly.

But what makes a laptop particularly suitable for data science work? Come with us – the data suggests we have some answers for you.

Best Laptops For Data Science

1. Apple 13-inch MacBook Air

The 13-inch MacBook Air tops our list for its supreme versatility – its capabilities are open to anyone, whether they’re students just getting their heads around the world of data science, or more seasoned practitioners, Python downloads in one hand and library files in the other.

While it’s got a user-friendly front-end, one of the most useful things about the MacBook Air is that you don’t have to squint too hard to feel the UNIX about it, which means it’s both relatively straightforward for data science work and reasonably easy to use as a data science machine.

That easy accessibility for data science elevates the MacBook Air into a machine that should be among the first rank of options for those at any stage of their data science career.

Let’s check a few specs, because they’ll help show the potential of the machine.

NB, this is not the newest MacBook Air – the newer version is slightly lighter, but saddles you with a significantly shorter battery life, which on longer data science projects can be a productivity-killer.

On the old Air, you’re looking at:
· Intel Core i5 2.9GHz processor
· 8GB RAM LPDDR3
· Intel HD
· 256GB SSD
· 13” 1440×900 TN screen size
· 13+ hours of battery life
· Under 3 pounds in weight

Fast, reliable processing, at least some heavy lifting in terms of RAM, and an acceptable initial SSD are great.

The thing to remember about a lot of data science work is that it increasingly involves remote working, punching through to storage and servers elsewhere for the heavier data-sifting and crunching.

A 13-inch screen is by no means the largest on the market, letting the MacBook Air flirt with tablet-size operation. But apart from the UNIX-like environment and the ease of doing data science work on the machine, check out those last two figures.

That combination of 13 hours of battery life and a weight of under 3 pounds means you can work, run programs, environments, scenarios, do what you need – and do it in a highly portable package.

Are we worried about the 8GB of RAM (expandable to a maximum of 16GB)? Could be, depending on the nature of your research. But for the vast majority of modern data science, certainly if you double up on the basic offering, you should be fine with the RAM on the MacBook Air.

If you find – or, ideally, given the tear-jerking cost of Mac machines, if you foresee – yourself needing more, you can get all the advantages of the Air, but with double the maximum RAM (32GB, no waiting!) on a MacBook Pro.

2. Acer Nitro 5 Gaming Laptop

Yes, this is a gaming laptop, rather than something dedicated to hardcore data science.

But there are areas of overlap, in that both gamers and data scientists want rapid processing of data elements and smart graphical presentation.

We’re not in any sense arguing that the Acer Nitro 5 can solve all your data science needs – it can’t. But it could well be a great entry-level device for those just starting out in data science.

If you have smaller datasets to deal with and don’t have the kind of eye-watering money needed to get a more singing, dancing, data-crunching behemoth, check it out.

If you’re a data science student, the Acer Nitro 5 might well have your name on it. With RAM of 8GB (the same as the MacBook Air at its basic plug-and-play level), you can run simple statistical models of small datasets on it with no difficulty at all.

The likes of R, MatLab, and SAS should give it no problems either, which means it’ll crunch its way through analysis, prediction, and even some simulation work, should you need it to.

Also, as a gaming computer, its graphical output is pretty impressive for its price point, with NVIDIA GeForce GTX 1650 graphics, and 4GB of dedicated GDDR5 VRAM to help smooth out your data presentation.

If all you need is a machine on which you can run your smaller datasets for translation into some pretty high-class graphical formats, the Acer Nitro 5 can genuinely be a good starting machine.

3. New Apple MacBook Pro

So, you know we mentioned the MacBook Pro as an upgrade option for the MacBook Air?

Naturally, it’s on our list in its own right too.

The love affair between data scientists and MacBooks has long been a thing of beauty.

Wake a data scientist in the night with cries of alarm and threats of fire, and you’re almost guaranteed to see them grab their MacBook and head to their designated safety zone, where they’ll stroke the machine to calm it down long before they’ll remember they have friends and loved ones.

Human connections, sure, but dammit, save the data!

Why is that? What’s the bond between a data scientist and a MacBook?

A MacBook understands data scientists, possibly more than any human being will. The new MacBook Pro is a particularly fine example of the breed. It runs applications, data modeling, and visualization tools better than most machines on the market.

From the point of view of the data scientist, while other people can share movies, meals, and maybe the rest of their lives, the MacBook can build them palaces for their mind. It gives them the tools to do the things they want and need to do, in environments that run smoothly in the languages that make sense to them.

The new 16-inch MacBook Pro has many of the advantages of the MacBook Air, with a couple of extra sparkly factors on top. On some levels, the MacBook Pro is the MacBook Air with extra mini-breaks.

The double RAM you already know about. The Pro runs with 16GB RAM as standard, expandable to 32GB. That means it has a greater capacity for smooth-flowing rapid-running data science work even than our list leading Air.

Processing power gets a boost here too, with the Intel Core i7 chip at its heart, clocking at 2.6GHz. That’s not just the bigger brother of the Air’s i5, that’s the processor the i5 dreams of being when it grows up.

Add a new thermal layout and the option of turbo clocking speeds to the picture and the Pro begins to make a real case to oust its younger, smaller, more portable predecessor from the top of our list. Add another 3 inches of screen size, and look again.

That’s an impressive offering for anyone. Data scientists though are absolute suckers for a multi-core processor, and the i7 is a 6-core thing of beauty.

Matched with the AMD Radeon Pro 5300M GPU graphics card, the Pro has both brains and graphical beauty.
Did we mention 512GB of SSD storage? Or the 12MB of L3 cache memory, cutting down the time you’re waiting for access?

So, wait – why isn’t the MacBook Pro the leader of our list, again? Is the Pro enormously heavy compared to the Air’s 3 pounds?
Nope, that’s not it either – the Pro weighs in at just 4.3 pounds.

Battery life? There’s a little something to say there – you’re looking at 11 hours in the Pro, rather than the Air’s 13. But what’s a couple of hours between friends when you’re bringing 6 cores to the party…right?

Bottom line, the Air and the Pro are not so much competitor products as stages in the evolution of your data science career.

The Pro may offer enhancements all along the line, but the Air wins the day for us mostly because of its comparative price accessibility to data scientists at almost every stage of their career, and because in the age of remote servers and the cloud, some – if not all – of the advantages of the Pro are mitigated by the ability to channel a lot of processing and storage through non-local servers.

4. MSI GS66 Stealth 10SGS-036

OK. So say you’re an experienced data scientist. You’re done messing around with smaller datasets. You’re on the Big Projects now. And for some reason, you’ve sworn off the MacBook of your one-time dreams.

Maybe Steve Jobs ran over your puppy when you were a kid and you swore never to have anything to do with Apple. No judgment here.

If all or most of that applies to you, you’re probably in the market for the MSI GS66 Stealth.

The MSI GS66 Stealth would be the winner of any notional reality show named Pimp The Ever-Loving Glory Out Of My Gaming Laptop.

It has specs that are turned up to 11 with the knob ripped off. It has – hang on, let’s let the specs do the talking for a minute.

Intel Core i7-10750H
32GB DDR4
NVIDIA GeForce RTX 2080 Super512GB PCIe NVMe SSD
15” full HD 300Hz IPS

Get the picture? Power, processing, storage, and a screen that’s almost at the MacBook Pro level.

This is not your standard workstation laptop.

It’s hardcore, from start to finish. Its cooling systems let you get the most out of its CPU and GPU, which you want to do because the GPU is – guess what? – super-powered, so when it’s working on all cylinders, you’re looking at the ability to speed up any process you like, including parallel computing.

The sound you’re hearing is the hearts of all the data scientists you know going pitty-pat.

It’s not exactly science fiction computing, but it’s a serious machine for serious data science.

So what’s it doing so far down our list?

Well, for one thing, performance and power like this don’t come for anywhere close to cheap. You’ll drop a big chunk of change, rent or mortgage-payment on this machine.

If the laptop itself were the best thing on the planet, that’d be fine. But it weighs more than an average house brick, and – you’re going to want to read this carefully because it’s not a typo – it has a battery life of 1 hour.

No, really. 1.

1 single hour between power sockets and the MSI will be gasping for juice and dying – taking your connectivity to your data with it.

That’s a deal-killer for a lot of data scientists. So yes, it does All The Cool Things. But the degree to which it functions as a laptop is smaller than most people would accept.

You also get a lot of gaming laptop-specific functionality which, if you happen to want a kickass gaming laptop, is great. But most data scientists will shrug at those elements, as they won’t add anything to their data science experience.

The MSI is a fantastic computer on which to run your data science. Just don’t ever think of unplugging it from the wall.

5. Dell XPS 15 7590

Dell is a laptop maker that regularly sets itself apart in the marketplace.

The XPS 15 7590 brings enough power to let you handle some complex data science without breaking a sweat.

A powerful Intel Core i7-9750 processor, clocking up to 4.5GHz, drives the bus here.

The NVidia GeForce GTX 1650 GPU graphics chip adds 4GB of VRAM to your game – similar to the graphic performance levels of the MacBook Pro.

And, while one of the big draws of the Mac machines is their ability to easily get their UNIX on, here you get Windows 10 giving you access to all the functions you could need.

Bringing the challenge to the Pro, the XPS 15 7590 comes pre-loaded with 16GB of RAM – that’s a lot of your hardcore processing taken care of. 256GB SSD of storage is one of the small issues that pushes the Dell down beneath the Pro in our list.

Oh, and in terms of battery life, you’re looking at an easy 14 hours here, which puts it ahead of even the MacBook Air on time between charges.

The XPS 15 7590 is probably the MacBookiest non-MacBook in town, so if you’re looking for a genuine alternative to the Apple behemoth, you can do worse than choose the Dell.

Laptops For Data Science Buyers Guide

If you’re buying a laptop for data science, there are a few things to keep in mind.

Process This!

Data science is the science…of processing data. Under no circumstances go for a machine with an underpowered processor.

Yes, a lot of modern data science is done via the cloud and remote servers, but you want a machine that can handle your work, without succumbing to information overload.

Storage Wars

Again, while it’s true that lots of modern data science makes the most of cloud storage, grab as much storage space on your machine as you can.

Also, this is the 21st century, so go SSD wherever possible. You want a machine that is as future-proof as possible, after all.

Juice Me!

The point of using a laptop is that you get a combination of lightness, power, portability…and battery. If you have a laptop with a battery that desperately wants to die, you might as well be using a desktop.

We’ve allowed the MSI laptop, which fits that description, into our list because of all the great things it does when it sits still.

But in general, check out the battery life of your laptop so you can genuinely run your data science unplugged for a reasonable length of time.

Frequently Asked Questions

How important is UNIX potential in a data science laptop?

It’s not by any means critical to use UNIX, but you can guarantee that most data scientists will choose a machine that easily allows the UNIX environment to run applications and programs over machines that don’t, eight times out of ten.

Why do MacBooks feature so prominently in data science?

Mostly, because of the ease with which Mac machines allow the UNIX environment and the statistical analysis programs to flow and function.

We think that the benefits of the 2 in 1 laptop outweigh the price tag. There are so many opportunities to be creative and optimize your productivity with design programs or tablet applications, whilst also being able to utilize office tools in laptop mode.

Is it important to get the most modern machine for data science?

No – it’s important to get the machine that most meets your needs.

Our list-leader is an older model of a modern machine, and that gives it a price-point that makes it attractive, without sacrificing anything important in performance.

What is data science

Data science may be hard for most people to understand, so here is a great explanation video for beginners:

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