How do you choose the right laptop for Data Science?

How do you choose the right laptop for Data Science?

Data Analysis, Machine Learning model training and the like require some serious processing power. If you’re someone who’s just entered the world of data or if you’re a veteran data scientist that needs an upgrade on his/her local machine this post will provide you with the comprehensive guide that is necessary to make the right choice when it comes to buying a machine that is capable of handling your data-sets.

When it coms to choosing the right machine you usually have to choose between two factors:

  1. Portability
  2. Processing Power

The higher the processing power the heavier the laptop gets and hence it’s portability is reduced and vice versa. The next thing to note is that with higher power the battery life also shrinks and as a result you are losing out on portability yet again. Huge datasets these days have outgrown the processing power of a single machine and will depend on you accessing the cloud for processing, in which case portability is going to be of value to you.

With that said, let’s identify the minimum requirements that you would require when it comes to a laptop worthy of being called a data scientist’s weapon of choice.


The minimum ram that you would require on your machine would be 8 GB. However 16 GB of RAM is recommended for faster processing of neural networks and other heavy machine learning algorithms as it would significantly speed up the computation time. Personally, 8 gigs of RAM works just fine if you build your algorithms very efficiently and you can put your machine on sleep mode while it takes its times to compute.


I cannot stress upon the importance of an NVIDIA GPU when it comes to choosing your machine. This is because most deep learning libraries (Theano, Torch, Tensorflow) use the CUDA processor which compiles only on NVIDIA processors. If you want to use a machine that is powered by an AMD or Intel HD GPU you need to be prepared to write a lot of low level code in OpenCL. With that being said, you can opt for the NVIDA 960 series and above.


Once you have the RAM and GPU in check, the processor should come right along with the machine you are selecting. However for the purpose of this guide, the intel i5, 7th generation would be the minimum requirement while the i7, 7th generation would be the ideal recommendation.


SSDs make your machine incredibly fast. However, getting a machine with a good amount of SSD would burn a hole in your wallet. Keeping this in mind, 1 TB of Hard Disk would be the minimum requirement as data sets tend to only get bigger by the day. If you’re opting to go for a machine with an SSD, ensure that there is 256 GB of SSD storage available on the machine. You might have to purchase an external HD in the case of the latter.

With the minimum requirements out of the way let’s find out what the best laptops are in today’s market both in terms of portability and processing power


As a developer you always want to go with linux. Luckily most systems with a MAC or Windows build can run the linux as either a virtual machine or on startup using software like BootCamp for the mac. Additionally parallels is a software that you can use to run two operating systems side by side on your machine.




Apple MacBook Pro – £1399/$1429/INR 139,000

The MacBook pro is an incredible device for data analysis that is light and has an exceptionally good battery life of 7 hours. The Mac comes with a 2.5 Ghz quad core intel i7 processor, along with 16 gigs of RAM and an NVIDIA 760 M GPU. It has a beautiful 15 inch display as well. The device comes well equipped with a 512 GB of hard disk.

Link to buy the machine in the USA:

Link to buy the machine in the UK:

Link to buy the machine in India:


MSI gl62 – £849/$939/INR 108,002


The MSI is a pure beast when it comes to processing power because it comes with 16 gigs of RAM, NVIDIA 960M and the intel i7. Apart from these it also has 256 Gigs of SSD and a 1 TB HD. The only downside is that it’s relatively heavy to tug around weighing in at a little over 5.2 pounds. The battery life is not the greatest with only 4 hours of battery life when running normal applications. This reduces to 2 hours when you run data intensive applications and programs. This means that you will always require a power cord in hand. The build quality is good, however you are not going to the premium feel that comes with a MacBook. Overall, it’s a powerful machine.

Link to buy the machine in the US:

Link to buy the machine in the UK:

Link to buy the machine in India:


If you’re looking for a cheap machine or amazing portability + battery life but still want to run neural networks there’s a solution – work on the cloud. Amazon AWS EC 2 is a virtual machine that lets you run any operating system you want and modify it to your preference and requirement. You can set up a web based IDE for R (RStudio) which is essentially running on the server for another computer that is powerful enough to run your algorithms while you use your computer. All processing is done on Amazon’s servers. So all you need is an internet connection. Amazon AWS EC2 comes with a year of free trial after which you pay according to your RAM/processing power requirements.

The only downside is that it takes some time to learn how to set up and configure the AWS and you need an internet connection at all times to work on your datasets. Barring this, it’s an exceptional way to buy any system of your liking and configure it for the AWS.

With this regard, the MacBook Air is a excellent machine. Windows machines that are as low as 250-350$ can also be configured for the AWS.

In conclusion buying a machine for data science can be a daunting task but this guide should have made things easier for you and you now know what to look for. Below is an infographic that shows you the ideal specifications for a laptop that is built for data science based applications.