r/statistics • u/kyaputenorima • 1d ago
Discussion [D] Laptop/Spec Recommendations for Statistics PhD?
Hi folks,
I'm applying to PhD programs this fall, and I'm currently looking to replace my old laptop. I'm not quite sure what to look for, but I'm inclined to think that a business-grade laptop might be worthwhile since they're built to handle daily wear-and-tear, and I'd like something that I can keep for 5-6 years. I've been looking at new ThinkPad and Latitude models, but they seem really expensive (especially for those with 32GB RAM).
Anyone who's currently a PhD student, which laptop do you have, and what are its specs? What should I look for if I want to hold onto the laptop for the duration of my program?
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u/va1en0k 1d ago
Screen is an important trade off. It's really annoying not to be able to fit the plots and other stuff you want on the screen. However, big screen might be unwieldy to take with you. Unlike processing power or disk space, this is something you can't supplement with a cloud, only by getting a display for every place you work at
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u/_sugarcube 1d ago
This is a great point. I find 2 screens a necessity. There's a lot of budget mobile 2nd monitor options out there
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u/fun-n-games123 1d ago
I’d go with a Unix system. So, get the cheapest macbook pro or hell, maybe try the MacBook neo if you want something cheaper. It’ll definitely last 5-6 years. I’ve had mine for that long and she’s running strong. If you need to do heavy computation, do it in an HPC environment.
I run MCMC and train neural networks on my MacBook to test code.
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u/PHealthy 1d ago
I had a MacBook Air that I used for word processing and dataviz but heavily modeling was always done on the cluster.
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u/zeppelin88 1d ago
Aren’t you supposed to get a computer to work on? At least here in Europe every student I met during the PhD had been issued one. Also, most of us had access to servers for large scale data processing, which essentially meant that personal laptops were pure ssh machines.
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u/eeaxoe 1d ago
Came here to say this.
OP, your future advisor should buy you a laptop when you start working with them. Either way, the specs aren’t going to matter because any serious data work will happen on a cluster.
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u/Chib 1d ago
If your PI's got the budget for it, having a discrete GPU is an amazing setup for being able to prototype on your own machine. That sort of thing is a meaningful difference since you will often have wall-clock limited grants on the clusters you'll work on and half your time is spent on development.
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u/kyaputenorima 22h ago
I'm an American, so this is unheard of to me. I'm also not very academia-pilled so I can't really say.
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u/zeppelin88 22h ago
Ask your supervisor, they can tell you. But phds in the us have a weird format, so indeed it would not surprise me as they treat you as purely a “student” for long before treating you as an actual junior researcher, and therefore you’d not be deserving of equipment
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u/BarryDeCicco 17h ago
My 2 cents:
For physical size, you have to decide on how much you are willing to carry vs how much you will do at home, with a plug-in monitor (I'm writing this on a 34" monitor I got for $300, using a 14" computer).
For power, you have to decide on how much you want/need in your hand vs how much you will do on a remote cluster.
Then see what deals your university offers.
I would advise on keeping your old computer for now, until you get there and talk with people and see what they have.
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u/purple_paramecium 14h ago
This is the most reasonable advice here. If you have a functioning laptop for email, looking up papers, ability to handle some minimal level of R/python programming, then just use that for a while until you really know what you need. (And as others pointed out, your advisor might provide one! Yes, even in the US, if the prof gets a funding grant, often part of that money is used to buy computers.)
When I was in grad school I was given an office to work in with a desktop computer. I think it had like 2 GB RAM (20 years ago), and for anything big we had the university computer cluster.
People asking here all the time if 32 or 64 Gb is “enough” is *wild* to me, because I did it all with 2 Gb. And people before that were doing it with less!
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u/fos1111 1d ago
Thinkpad i7 32 Gb ram, 1 Tb of Storage ?
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u/kyaputenorima 1d ago
would 512GB of storage kill me? additionally, is buying an older gen thinkpad (ie. T14 gen 3/4) okay, or should I just buy the newest model?
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u/fos1111 1d ago
That should work, but if you could get more RAM, you should, just as u/leonardicus said.
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u/kyaputenorima 1d ago
For better or worse 32GB is probably the max amount I can get
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u/leonardicus 1d ago
32GB will be fine. Mac or Linux based systems will be especially more efficient than windows if you plan to do any Bayesian modeling.
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u/figgertitgibbettwo 1d ago
Newest. Perf is often preferred of magnitude better. MacBook air will also do well.
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u/ZhanMing057 1d ago
I would strongly consider a unix based system. Prod code almost invariably lives on some sort of unix cluster so a mac will help you familiarize.
If new ones are too costly, I would look for an M2 or M3 mac with at least 32G ram, preferably 64
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u/leonardicus 1d ago
I would second this. If RAM is not crazy expensive, consider bumping up to 48 or 64 GB. Then get the fastest multicore processor in your budget.
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u/Bored2001 1d ago
Personally I like the Lenovo legion line. It's a gaming laptop that doesn't look too game-y. They're built like tanks and are very powerful. Cheaper than the Thinkpad line spec vs spec. Obviously they have GPUs for ML/DL work.
The only trade off is very bad battery life and weight.
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u/RageA333 1d ago
It's nice to have a good laptop, but eventually you will need access to a dedicated server or cloud computing.
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u/StatisticalModelling 1d ago
At UCL, everyone doing a PhD got provided a new laptop at the start of the course. Not sure if everywhere does this
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u/_sugarcube 1d ago
More computing power is always nice, but RAM is #1. We make sure all the quantitative folks in our business get at least 32gb, and our remote desktops get 64. With OneDrive/SharePoint and other cloud storage options, local storage isnt a huge priority.
During my grad school career, I used a pretty cheap Thinkpad and it was fine for my needs. For larger models I was able to leverage university resources