Data Science is a study of data; it includes recording, storing, and analyzing data to extract useful information from it. The application of data science is diverse since it is a vast field and encompasses many subfields. It is used in banking, retail, e-commerce, entertainment, internet search, speech recognition, etc.
As a data scientist, you have to collect data, process it, model it, and then apply different algorithms to take useful decisions and set objectives for improvements. All these need a powerful machine and if your machine is not good at crunching numbers, then your client will suffer, and consequently your career as a data scientist. Hence a decent laptop is extremely essential for your data science voyage.
This write-up is focusing on giving you a guide to buying a laptop for data analysis. But before we dive into the list of our picks, we must understand what kind of machine a data scientist needs first.
Specification for a Data Science Laptop
Before grabbing a laptop for data science, there are few things to be taken into account, and first comes RAM:
1. Memory [RAM]
Memory is very crucial for a data scientist laptop. The more is always better. The recommended memory is 16GB. But if your work is cloud-based, then a huge memory module is insignificant. Having a laptop with an expandable memory option would be a plus.
2. Processor [CPU]
Well, data analysis needs a lot of computational power, so prefer the latest and a multi-core processor to fully take advantage of parallel processing. If you are using AWS or other cloud-based services, then having a good processor will be of less importance, but I would still recommend having a good processor as they are no longer expensive.
3. Graphics Card [GPU]:
In data science, many operations depend upon GPUs, such as training the model. The need for a GPU also depends upon the type of data science task. If you are doing deep learning or handling a large amount of data, then you must need a graphics card to accelerate the processing. A GPU has many cores as compared to a normal CPU, so having a GPU will speed up the data analyzing process by many folds.
4. Storage:
Data takes a lot of storage so it is better to have a good storage device. SSDs are a perfect choice as they are quite fast. But they are super expensive at the same time. So, if you are tight on budget, then having a small SSD of 512GB is enough, along with a regular hard disk for storage. Make sure that your laptop has a USB Type C port for faster data transfer.
5. Operating System:
The operating system is your personal choice. Its better to go with laptops that support Linux. I would recommend macOS or any Linux distribution. Windows can be a good choice too, but it needs a lot of extras to do before you set up everything.
It is evident that ordinary machines are not appropriate for data science projects. You need a powerful machine with adequate memory and a pair of robust CPU and GPU units with sufficient storage space to work efficiently. Lets take a look at some laptop that suited best for data science projects:
1. Dell G5:
The first pick is Dell G5 that comes with tenth generation Intel Core i7 CPU with 6 cores and powered by NVIDIA GeForce GTX 1650 Ti graphics card. If you are a professional data scientist and working on modeling or deep learning, then this laptop will handle everything quite effectively. It is a Windows-based laptop that comes with various storage capacities. I would recommend going with 16GB of memory and 512GB of SSD.
G5 comes with 51 watt-hours 3 cell battery and a range of ports including an SD card reader and 1 USB Type C port. Though the display has nothing to do with data science, having a good one is a plus. G5 has 15.6 inches, full HD, LED display with anti-glare coating.
Pros:
- A well-balanced machine
- Solid performance
- Beautiful Looks
Cons:
- Noisy Cooling
- A bit hefty
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2. HP Envy 17t:
HP Envy 17, not the best but a good choice among the latest laptops for data science projects. The installed processing unit is Intel Core i7 and a dedicated NVIDIA GeForce MX330 graphics card. The processor has 4 cores but the presence of a graphics card enhances its overall performance. Envy 17 can effectively handle most of the data science-related tasks.
It comes with 16GB of RAM and dual storage option, which is remarkable. Envy 17t has an SSD of 256GB with a hard disk of 1TB. The 17.3 inches, 4k display is more than enough for a data scientist. You also get 3 USB Type-A ports, 1 USB Type C, HDMI port, and an SD card slot.
Pros:
- Sleek looking design
- Comfortable keyboard
- 4k display
- Good thermal management
Cons:
- Mediocre battery life
- A bit expensive
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3. Macbook Air:
I would highly recommend having a macOS environment for data science. There are multiple pertinent reasons such as the UNIX-like environment and the latest M1 chip. M1 is a quite efficient chip as it has 8 cores and performs a lot better than the latest AMD or Intel processors. M1 is specifically designed to boost machine learning.
The latest models of MacBook air come with 8GB/16GB of RAM configuration with 256GB/5126GB storage capacities. 8GB memory is enough, but I would recommend going with 16GB. Storage depends upon your personal preference, and having 256GB of SSD is adequate if you are buying a separate hard drive.
Pros:
- UNIX like environment
- M1 Chip
- Excellent battery life
Cons:
- Does not support CUDA core applications
Having no CUDA core support could be a huge disappointment, but that does not mean MacBook air should not be on your list. It can still handle a big percentage of data science projects. But if you want parallel processing support, then go for 16 inches MacBook pro.
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4. Acer Swift 3:
Another budget-friendly device with outstanding specifications. This is my second highly recommended pick. Swift 3 is installed with AMD Ryzen 7 4700U, 8 core processing unit integrated with Radeon graphics. An ample choice for any data scientist with cost-effectiveness.
Swift 3 is a thin, lightweight MacBook-inspired design that comes with 8GB of RAM and an SSD of 512GB. Full HD LED display, HD webcam, and backlit keyboard complement the machine.
Pros:
- Affordable
- Sleek design
- Highly portable
- Good battery life
Cons:
- Memory cannot be upgraded
- Average Display
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5. Lenovo ThinkPad E15:
Lenovo ThinkPad E15 is another pick for a data scientist. The machine has different variants. The recommended specification is tenth-generation Intel Core i5 with integrated with Intels UHD 620 graphics.
ThinkPad E15 comes with 16GB RAM, which exceptional for data science-related tasks. Like HP Envy ThinkPad, it does not come with extra storage, so if you need storage, you need to buy it separately. The 15.6 inches display is decent with an anti-glare coating. Additionally, you can also connect an external monitor of 4k resolution via HDMI or USB Type C.
Pros:
- Upgradable storage
- Robust body
Cons:
- Gets really hot under load
- Short battery life
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Conclusion:
Data science is a vast and diverse field, and as a data scientist, your job is to efficiently manage the data. As data is growing, the hardware needs to organize a huge amount of data as well, it is also demanding upgradability. In this write up, we focused to give a brief guide about laptops you should consider for data science-related tasks.
Data analysis demands multi-core processors and GPUs with a good amount of memory. I would recommend going with the latest generation CPUs, especially octa-core and GPUs if you are dealing with deep learning. Nevertheless, having a good GPU boosts collective performance.