Best Laptop for Data Analysis

Best Laptop for Data Analysis – Data scientists are highly innovative professionals with high-end data processing requirements. Unless the data scientist always users a remote server to get the job done, a powerful laptop will continue to be a requirement.

If you are a seasoned professional who is only looking for storage, RAM, and processing speeds can go for the Asus ROG Strix Scar III or the Razer Blade Pro 17 from Razer. These gadgets also come in handy if you are looking to use the concept of GPU acceleration for neural networking.

If you are strictly looking for a MacBook, nothing beats the efficacy of the new Apple MacBook Pro. However, if you are a student or an aspiring professional with not-so intricate requirements, the Lenovo Yoga C740 is a decent enough device.

In the end, it all comes down to the criticality of your job and the nature of tools that you would be using for data visualization, data scripting, database management, and establishing connections with the data server.


The aforementioned list comprises of the best laptops for data science that are available in the market these days. With some great specifications, these laptops are have a multifaceted personality. You can use these for myriad other activities like quick browsing, enjoying games, video chatting as well as operating various applications related to data science.

1.Dell i5577

Dell has this persistent ability to surprise. While I thought we had seen it all, Dell came up with their own budget gaming laptop. The Dell Inspiron i5577-5335BLK-PUS we reviewed a while ago is one such example and our unit on review is not so different from it.

Not only does the Dell Inspiron i5577-7359BLK-PUS have a superior processor in the 7th Generation Intel Core i7-7700HQ Quad-Core, it also features the increasingly reputable GeForce Graphics Card from- the GTX 1050 which has steadily built a reputation as one of the best Graphics Card for people conscious about their display – like your typical gamer, but who abhors overpaying.

2.Lambda Tensorbook

First of all, let’s start with the actual parameters of Tensorbook. Those has been updated since last year, incorporating the new RTX series from NVidia.

From the machine learning perspective, crucial things are 16GB GPU, 8 cores processor, memory of 64 GB and 2TB storage. Tensorbook is pretty light relative to its size and capabilities.

3.Razer Blade

Razer’s Blade 15 Base isn’t broken necessarily, but it could use a tune-up anyway. It’s emblematic of the company’s excellent design chops — stuck in a loop of flexing it in practically the same way as last year and the year before.

The laptop’s all-aluminum build is still impressive to see and use. And yes, it has a webcam, unlike some of the gaming laptops we’ve reviewed recently. But the effect it gives off is getting stale, and Razer still hasn’t addressed all of the annoyances that have been present since this design debuted in 2018. It’s a magnet for fingerprints and smudges, and for a laptop that starts at , its lack of any biometric login method is baffling. You’ll need to type in your password or PIN every time you log in.

4.Dell XPS 15

One year after Dell significantly redesigned the XPS 15 , this exquisite desktop replacement laptop remains as excellent as ever. It’s a no-brainer for those who prioritize thoughtful yet bold styling in a 15.6-inch notebook that’s as compact as possible while still offering more powerful components than many competitors.

The refreshed XPS 15 model 9510 reviewed here adds an optional OLED display, a new “Tiger Lake-H” Intel Core i7 processor, and Nvidia GeForce RTX 3000 series graphics, though it’s otherwise unchanged from 2020’s major overhaul.

5.HP Envy

The screen is noteworthy because it has a 16:10 aspect ratio rather than the more common 16:9, offering an 11% taller view (1,920 by 1,200 pixels) for a bit less scrolling.

The Envy clamshell also shares a few frills with the upscale HP Spectre x360 14 convertible, such as a keyboard that incorporates a fingerprint reader, a webcam privacy shutter, a microphone mute button, and a launch button for the HP Command Center settings utility.

Best Laptop for Data Analysis- BUYER’S GUIDE

Graphics card

The first question which we should ask ourselves is:

Having the decent GPU on board will make easier to debug and work interactively on your code when you use Keras, TensorFlow, PyTorch or some other Deep Learning libraries. However, if you train something bigger, you can always use cloud resources to get extra GPUs or TPUs.

However, if we focus only on a simple models (e.g. built in scikit-learn/sklearn or R/RStudio/SAS/SPSS) and Deep Learning is not our thing, we can resign from the dedicated GPU or choose something lighter — e.g. MX family from Nvidia or GTX 1050.

Memory (RAM)

The second most important parameter to focus on is the memory. The more RAM we have, the less we have to care about our data size.

Working with the out-of-memory data set is less convenient. Of course, we can process the data in batches or use libraries which allow us to work with the bigger data — hiding under the hood exchanging the data between RAM and disk. However, it limits the tools which we can use and adds extra overhead to our code.

When Data Scientist look for the laptop, he should consider only notebooks with at least 16 GB RAM. However, I recommend to stick with 32 GB.

Usually, taking more than 32 GB is pointless as there is very limited choice. If 32 GB is not enough, we can always use the cloud resources.Photo by Isaac Smith on Unsplash


If we want more power and we are going to use fully multiple cores then Intel i7 or AMD Ryzen 7 are the good option. However, we can save a bit using i5 or Ryzen 5 and they will also do a good job.


We should choose laptop with at least 480 or 512 GB. A quite interesting option for a Data Scientist are computers with 2 disks — SSD and bigger HDD. Such notebooks allow us to have a fast SSD disk for operating system, installed software and libraries, and also keep projects on which we actively work. While the HDD disk can be an archive and a place to store bigger data sets.


As an operating system, we should choose the one with which we feel the most comfortable. Personally, I prefer and recommend Linux as it support the most of the Data Scientist tool and Machine Learning libraries among all OSes.

Other parameters

The other parameters are not so important particularly from a Data Science perspective so choose what suits us the most.

The screen size and weight, we can adjust depending on how often we travel with the computer and what type of transport do we use — public, car, bike, walking. And also taking into account if we use external monitor or rely solely on the built-in display.

Similarly with ports, think about all devices which you have or going to use and make sure that all needed ports are present.

Frequently Asked Questions

Why should you consider the MacBook as a Data Scientist?

OS X or the macOS is inherently built on the UNIX platform which makes it better equipped towards handling demanding software sets and applications. Therefore, some of the more intricate tools for data prototyping, testing, and visualization work the best on MacBooks. Some of these include the Power Query and the Power Pivot that rely on dataset sharing.

Why a Multi-Core Processor is required for better data processing?

Most data processing tools like Python and R use all the available cores for processing data in the best possible manner, despite being single-threaded. Moreover, the likes of R.Data Table are multi-threaded in nature and require high-end processors to work in the desired manner.

Are Gaming laptops sufficient for Data Scientists?

The processing power of the device for a data scientist actually depends on the tasks he or she is looking to indulge in. For instance, if you are interested in building simple data models using the likes of statistical libraries like Keras and Tensorflow, a decent chunk of RAM is the only requirement, provided the device already shelters a powerful processor.

What is the role of GPU for a Data Scientist?

A powerful GPU is necessary only if the data scientist is looking to work with deep learning tools. Besides that, high-end GPUs are necessary if GPU-Modeling and neural networking are concerned.

What are the most important aspects to look for in a laptop for a professional data scientist?

To start with, a professional would first require loads of RAM, as datasets are directly processed into the memory and once the size exceeds the RAM, processing slows down. The next requirement is a sizeable SSD storage, precisely to lower the access time. Processor speed, including the base and turbo clocking frequencies, is necessary to consider, especially when working on the Azure Platform is concerned.


Although you won’t need a very powerful machine if you end to use the remote server for your work, however, to make the best use of the mentioned tools, the concerned device must feature a powerful, multi-core processor, decent and fast SSD based storage, and a sizable RAM. In addition to these, the device must also feature a decent display followed by an ergonomic keyboard and a cohesive collection of usable ports