As I unboxed my new laptop this week. It got me thinking. What's the best laptop for data science
in 2021? Let's talk about it. Whether you've just started your first data science project or job,
there's always the question of what laptop or computer to get it's hard not to get confused
with the plethora of options out there. When you think of computers for data science you might be
tempted to think of chunky gaming laptops with NVIDIA gpus or you might even think of custom
built computers but surprisingly the machine i've been using for the last four and a half years
as a data scientist has been the Macbook Pro I've used both the 13-inch version and the 15-inch
versions it ticks all the boxes for what you might need out of a data science computer we'll
get to why i recommend it but first let's talk more generally about the basic needs of a data
science computer so It'll be useful to you even if you're not looking to buy a Macbook pro because
obviously there's a lot more choices when it comes to laptops for data science. So in terms of basic
requirements for data science laptops, they fit in four categories ram, cpu, storage and gpus. So
let's talk about ram first off. Decent amount of ram helps with smooth operation of your computer
and help with loading in large data sets that you might need for data wrangling cleaning and
analysis obviously when it comes to loading in data sets there are programmatic ways that you can
minimize the memory usage such as using chunking in pandas data frame but it's always good to have
a good amount of ram on your laptop as it'll make sure that your computer doesn't chug
when it's running multiple parallel apps. At a minimum i would recommend 16 GB but the laptop I
just recently got I made sure to have 32 GB on it because that's definitely something i don't want
to be compromising on depending on the laptop it can be costly to upgrade ram. So definitely
pick an option that works for you. Let's talk about cpu so as a minimum i would recommend
four cores as that allows you to undertake parallel workflows which will speed up the running
of your code and that's definitely something very useful i've previously owned the dual core Macbook
Pro and it really chugged when you had lots of apps running. These days many high-end processes
have six core or eight core which will be more than enough for majority of your needs
the intel processors packaged with Macbook pros are generally quite good from my experience.
I don't really know how the M1 performs other than the reviews i've seen but I definitely
opted not to get the m1 laptop as I'm not too sure about the compatibility with various coding
tools. I know a while back it didn't quite work so well with docker so I'll definitely be waiting
to see what happens with the M1 chips before I opt in to buy an M1 laptop and I've also heard
very good things about the AMD Ryzen processor for data science purposes. Definitely make sure
to look at them. If you're enjoying the video so far then consider subscribing i make weekly videos
to demystify the land of data science. Next up let's talk about storage definitely make sure to
get a laptop with SSD drives. I mean SSD drives are generally standard these days. So I would
definitely opt for a minimum of 256 GB I know i like to definitely upgrade that to 512 GB but
that's not really necessary and if you need extra storage I would recommend ssd external drives
I bought this 1TB Sandisk ssd a couple of months ago and it's been running great accessing files
on your external ssd drive is just as fast as accessing it on your computer so that's definitely
very useful when I'm saving my video editing files on there. Finally let's talk about GPUs. So as
you might already know GPUs are very commonly used in machine learning especially for deep
learning algorithms but if you're just starting out getting a cpu on your laptop is not really a
necessity there's lots of online tools out there such as Google Colab* and Kaggle notebooks which
will allow you access to free GPUs and that'll be more than enough when you're trying out a couple
of new data science projects and most commercial companies out there that are doing ml in a serious
fashion will never really have your training algorithms on your local machine as It'll all be
done on cloud instances or on-prem computers which are basically custom built computers that sit in
the office or in some data center that's open for use by the data science team. On the other
hand if you are a serious ml users then check out some of these laptops that I found or better yet
you can look at custom built computers because that way at least you can upgrade the hardware
with time while I haven't really done this before I recently did find a medium article by Jeff Chen,
an AI engineer that built his own computer for machine learning for under three thousand
dollars. So I'll leave a link to the post below. So definitely check that out. That post is from
the end of 2019 so a lot has changed since then so be sure to find more recent resources if you're
considering custom building your own computer. Now that we've done a run-through of the basics,
let's talk about why I use the Macbook pro as a professional data scientist and most of the
developers and data scientists I work with also used the same machine as all of our model related
work aka gpu intensive work was always done in cloud instances or on-prem machine. There's a
couple of advantages to using a Macbook pro. So the first is that it's built on mac os which is
a unix-based operating system which provides a developer friendly environment as it's extremely
flexible stable and secure and the command line environment on the MacOs is very similar to the
environment that's running on a lot of these web servers. So it's really helpful to be working on
a machine that closely emulates your production environment the second reason is the build quality
and reliabilities due to the high build quality of the Macbook pros. They can easily last you
four to five years as a matter of fact the Macbook pro I purchased in 2013 still runs fine to this
day albeit a bit slow due to the outdated nature of the hardware. The third reason is good CrossOs
compatibility so it's very easy to run Windows or Linux on your Macbook. The same can't be said for
running MacOs on Windows or Linux so it's really helpful to have all of the different environments
if you're doing things like iOs app development or android development or windows app development
so it's quite flexible with how you use mac os and the final reason is user experience. You'll
definitely know if you've used one of the apple products like an iphone or a Macbook that Apple
is very big on providing a great user experience and it's important to highlight that you can get
all the benefits. I just described using a Linux machine but I've personally found Linux machines
to be very finicky to configure especially when it comes to dealing with different types of issues
related to driver installations. So consequently I do find the UI better to navigate on MacOs.
Additionally I think Macbook pros are better all around laptops because let's be honest you
won't just be using the Macbook for data science and you want it to be a great machine for things
like internet browsing or running any type of app that's available on windows but in saying
that if you are considering a linux machine for data science i'd really suggest looking
at the HP Zbook Studio G7. A couple of my co-workers have it and they seem pretty
happy with it. If you've got money to burn I would recommend getting the Macbook pro 16
inch with the six core intel processor and 32 gigabytes ram this is very similar to the
specs of the laptop i've been using for the last three years otherwise you can also opt for
Macbook pro 13 inch but it all really depends on to what extent you intend on pushing your
laptop. And at the end of the day buy whatever machine you can afford whilst keeping in mind
the basic requirements that I discussed earlier and it should be more than enough if you're just
getting started. So that's it for today. Thank you for watching and I'll see you next time.