Dear Mehdi Azizmohammad Looha,

Personally, If you know the size of your typical dataset and the type of data analysis you do, finding the best laptop for data science for you is pretty straightforward.

For example:

(1) If you, like most data scientists,  work with Python, R & Pandas to run data sets that can fit in memory (4GB-16GB) to fit non-deep learning models. Any modern laptop that can be upgraded to 16GB RAM will do. Optionally, you can speed up the process by choosing the fastest CPU (the M2 & M1 CPU chips are the fastest as of 2023).

(2) If you work with parallel processing libraries that make use of GPU cores. Ex: deep learning. You want a laptop with 6GB vRAM for NLP (text data) and as much vRAM as possible if working with CV (image data). Not gonna lie a desktop with a 3090Ti would be a better choice for the latter.

(3) Optionally, computer clusters (see featured image) allow you to train or process any data  (deep learning, neural networks, machine learning, etc) regardless of size and complexity hundreds of times faster than on any computer you can buy.  You can use ANY laptop of your choice to connect to these services.

Now, before we get to the best laptops for data science….

I will summarize the last section and discuss the ideal hardware specs for data science.

Greater data sets will also require more hardware…usually RAM memory.

1. RAM: As data sets grow larger, RAM becomes the first bottleneck. If you have 2x RAM of your biggest data set, things can speed up an order of magnitude because all your processing is in-memory(RAM).

16GB RAM: bare minimum for data scientists. Not going to find it out of the box on budget (350-600 dollar) laptops but you can always upgrade RAM (some can be upgraded up to 32-48GB).

2. CPU: Faster CPUs are always good but since most CPUs are way too fast, RAM will become the main bottleneck long before CPU comes into play.

Here’s what I mean, if a CPU can process 10**5 pieces of data sets per second and if 32GB RAM can only serve 10**4 pieces of data per second, what’s the point of buying a faster CPU?

Assuming you have the luxury to choose a fast CPU AND you have maxed out on RAM memory:

If working with R & Python, choose the CPU with the highest clock speed (algorithms are mostly single-threaded). Otherwise, grab a CPU with 8 cores as the limit (this is the maximum on laptops anyways).

The fastest CPU for most  CPU intensive algorithms and libraries is the M2 Max MacBook Pro. Any M1 & M2 chip beat Intel & AMDs on benchmark tests.

3. GPU: NVIDIA CUDA: If you WANT to work with deep neural networks or parallel.NN (parallell computing) algorithms that have process IMAGES then you want a dedicated GPU with as many shaders or cores you can afford & as much vRAM as possible. On laptops, this is the 3080Ti (16GB vRAM) but you’ll get your best bang of your buck with a desktop that has a 4090Ti. Note that the data set must fit into vRAM rather than RAM in this scenario.

If you can’t get a dedicated GPU (or can’t afford it) and you’re getting started, don’t sweat it. Most data scientists use cloud services for these kind of processing and you should too.

4. SSD: Storage speed (SSD Type) has little impact, if any, on the data crunching process but if you want to maximize speed when transfering files from drive to drive: the fastest is PCIe NVMe 4.0.

5. Keyboard Good keyboards on laptops are not easy to find and usually expensive. If you don’t feel comfortable with the built-in keyboards no worries, just get an external keyboard. External mouse or ball trackpad is a MUST, you dont want RSI and tendonitis.

6. Display Min FHD 15” screen: Chances are you’ll be either ssh’ing into a more powerful machine or using the cloud later at some point so the extra screen space becomes super useful to see longer pieces of commands at a time.

OSX Mac vs. Windows vs. Linux – the best OS for data science is either a Linux-based machine or an OSX apple computer.

Thus if you can afford, pick a laptop that can support a Linux Flavored OS seemlessly like a Lenovo ThinkPad if going for windows laptops since you may use linux entirely (though most will be fine with a Linux virtual machine on a Windows-based laptop).

The following are some of the choices:

1. HP Victus Gaming 3

2. M2 MacBook Pro

3. Lenovo ThinkPad T15p

4. Lenovo Ideapad i3

5. Acer Predator Triton 500 SE

Hope this helps

Wasswa Shafik 's answer above is one of the best answers I've ever seen on Researchgate. I suggest you thank him with a 'best answer'. The only small point I would add is to use a desktop rather than laptop, as laptops can suffer from heat choking no matter how high the RAM and processor spec.

Thank you so much Christopher C Kelly, for your contribution above. The response so humbles me.

To handle big data analysis using Python programming, a laptop with high computing power and sufficient RAM is recommended. According to the search results, the recommended specifications for a laptop to handle big data analysis using Python programming are as follows:

• CPU with the highest clock speed (for R & Python)1
• 8 to 16 GB of RAM23
• 32 GB of RAM (for large complex models)4
• At least 4 GB of RAM for virtual operating systems5

It is also important to consider other factors such as storage capacity, graphics card, and battery life when choosing a laptop for data analysis. Some recommended laptops for data science and data analysis include MSI GS65, Dell XPS 15, HP Spectre x360, and Lenovo ThinkPad X1 Carbon