Top 10 Essential Laptop Requirements for Data Science Projects

Top 10 Essential Laptop Requirements for Data Science Projects

by April 28, 2022 0 comments

A data scientist has to deal with huge volumes of data – collecting, analysing and interpreting. To handle such a large amount of data, you need a laptop that can work efficiently and has the power to manage all your computing requirements. With the right machine, you can work on your projects smoothly and focus only on your work instead of struggling with fixing the PC for performance issues. To ensure a smooth computing experience, you need to keep in mind certain essential laptop requirements. You need to choose a machine with enough power as the greater amount of data you handle, the better hardware you will require. Here are 10 essential requirements for laptops or desktops for data scientists.

1. A Dedicated GPU with 4 GB VRAM

Being a data scientist, you would not only deal with various data sets, but also multiple software applications where GPU can accelerate the performance. Having a machine equipped with a powerful GPU can decrease the time from days to hours to minutes to seconds. Depending on the software application, or process, for instance, the deep learning library processing requires the CUDA processor. Therefore, you need a dedicated GPU, as it comes with more than 100 cores, which can do parallel processing. A GPU is essential for any data science project.

2. The Right CPU

Being a Data Science professional, you will work with different types of software and the right processor will give you enough compute power to handle large data sets. Not all processors are made to handle Data Science workloads, hence you need to choose a the right CPU.

You need a CPU with a clock speed of 4.2 GHz, 8 cores, 16 threads or higher depending on the amount of data sets you will manage. The recommended CPUs would be Intel Core i9-11950H and Intel Xeon W-11955M. For even higher performance, you can go for a processor with 18 cores or up to 56 cores with 2x Intel Xeon which is scalable as well.

3. How Much Memory?

As a data scientist, you’ll work on vast volumes of data using various tools to find patterns, derive useful insights that will aid in business decision making. More RAM is therefore very much needed. A PC with 8 GB RAM is fine for entry level data science work, but the sweet spot is 16 GB. You could consider 32 GB as well if you have to work with large machine learning models. So, look for a laptop that allows ample memory upgrade.

4. More Data, More Storage

This is a little tricky, as the minimum storage space you need is 512 GB, while recommended is 1 TB. More importantly, you should go for SSD. In case your budget doesn’t allow an SSD with 1 TB or higher capacity, then choose 512 GB. That’s because HDDs (Hard Disk Drives) are much slower. SSDs will reduce boot-up time and program loading time considerably. Also, as HDDs have moving mechanical parts, their reliability and durability are much lower as compared to SSDs. They also consume more power than SSDs.

5. Perfect Eye Companion

Data scientists have to look at huge amounts of data on screen for very long hours. Therefore, you need a PC or laptop with a screen that’s comfortable for the eyes. Modern machines come with high-res displays with features like a blue light filter and flicker-free screens. Most electric devices emit blue light, which can harm your eyes without you even noticing it. Having a filter for it can keep your eyes safe. So, look for this feature and also choose a screen that supports HD (720p) or full-HD (1080p) or even higher resolution like 4k.

6. Keep your Data Secure

Your data is precious, so you need a laptop that can protect your data from various external threats. Laptop manufacturers like HP load their machines with modern security features. Anti-virus software may block cyber threats but have their own limitations. A feature like HP Sure Sense protects you from new malware variants using proprietary deep learning algorithms and advanced neural network technology. Not only this, your PC will be loaded with features like HP Sure Recover, HP Sure Click, HP Sure Start, HP Sure Run and HP Sure View, etc. All of these ensure that the PC is protected from malware as well as prying eyes.

7. Built for Durability

Having a laptop gives you the freedom to work from anywhere and even while travelling. You should get a laptop that has Military-grade quality and reliability. This will protect your PC in case of any accidental drop and liquid spill, etc. Laptops like HP Elite series business notebooks are built from high-quality materials and certified to rigorous MIL-STD-810G. This means your laptop is protected from accidental drop, physical shock, sand/dust, high/low temperature, humidity and altitude, etc.

8. Lightweight and Portable

If your machine is loaded with powerful hardware and lots of features, this doesn’t mean it should be heavier as well. There are laptops which are not only equipped with the latest and most powerful hardware components and features, but are also lightweight and have a thin profile. So you should choose a machine that is ultra-portable as well.

9. Day-long Battery Life

You will be working for longer hours, so your machine should also come with a battery that can last for more than 8 hours. No one likes to have the pain of charging the device multiple times in a day. Look at a laptop that can deliver over 8 hours of battery life.

10. Which OS?

While Windows is the best choice for data scientists, there might be times when you need to use Linux. There are options available today, like select Z by HP desktops and mobile workstations that provide Windows Sub-system for Linux 2. This allows you to run Ubuntu Linux directly in Windows. It doesn’t require dual-boot, which increases your productivity as you don’t have to waste time in restarting the system. Moreover, it’s a complete Linux kernel that comes pre-loaded with most popular tools that are critical for data science work.

These are the 10 essential points to consider when choosing a system for your data science requirements. There could of course be other aspects to consider as well, like audio, noise cancellation mics, fingerprint reader, etc. These are all features that you should look for as per your requirement.

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