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A Mac is a PC in the literal sense of the term, as PC stands for personal computer.
In common usage, however, in this article, the word PC would refer to a computer that runs on the Windows operating system, the Apple operating system.
Because Macs are UNIX-based platforms, they are popular among data scientists.
This isn’t to say that PCs aren’t a viable option.
In comparison to PCs, Mac computers are relatively costly.
The Mac also has the advantage of being compatible with more data science software and tools.
In this post, we’ll look at the differences between Macs and PCs, as well as the features that stand out as disadvantages or benefits of each operating system.
What to consider before purchasing any of the above systems
What OS is more suited to your specialty
Mac runs on a Linux/Unix-based OS that supports every data science language you can think of. Although, PC users need a specialized integrated developed environment to use certain data languages.
What system would be most appropriate for the niche you’d be exploring
A clear picture of what you would be handling, kinds of apps, and data to process.
What you like best: personal preferences
- What OS do you personally prefer?
- Which do you deem easier to use?
- What UX/UX appeals more to you?
These are personal preferences that you should consider too.
Upgrading Plans
Most users make plans to upgrade their systems over time. Mac computers do not support or allow a lot of upgrades to be carried out. With windows computers, you can build your own PC, and gradually add better hardware.
Should You Buy A Mac?
If you desire the best learning and working experience in data science, then you should go for a Mac.
Macs are excellently built and to a degree offer more for a data scientist than a PC would.
It is quite a popular fact that most data scientists prefer a Mac even with the expensive prices.
A factor that is also considered is its compatibility with other tools used in data science.
Macs are highly capable machines, strong and durable.
The Pros
High compatibility with data science softwares
This is a key reason why data scientists prefer Macs. It allows a lot of tools and software, such as tableau, anaconda, and more.
Reliable wi-fi
Mac OS computers are also known for their highly durable and powerful Wi-Fi card.
Lightweight
A Mac system is known for its lightweight, which makes it easier to work with. Easily transported and moved, you do not have to sit in front of a system for long hours without motion.
Highly Usable
A Mac system is user friendly. Most data scientists spend long hours in front of a computer. The UI and UX make it bearable.
The Cons
With all of the reasons above for why Mac is a great system for data science, it also has a flip side.
There are a couple of cons, some of which are:
Apple Products Are More Expensive
A Mac is known to be pretty expensive, even more so is the compatibility with hardware.
The Mac system does not readily support hardware that is not from Apple, and it is relatively costly to purchase compared to a PC.
No HDMI Port
Mac systems do not contain HDMI ports, which might not be much of a problem for some individuals, but it does become a problem when a presentation is to be done and you’re to use a projection.
Most data scientists would find this a little difficult to cope with.
Upgrading Is Not An Option
Almost impossible to add more RAM to a Mac.
Best Mac for Data Science
The M1 chip Macbook Air is the most recommended for data science due to its features.
This laptop comes with the M1 chip, excellent with GPU, CPU, and machine learning performance.
This M1 chip compared with an Intel or AMD processor is simply not close, edges them completely out with its great speed.
With up to 8 cores that can deliver 3.5x faster than the previous generation, and 16 core neural engines to handle projects faster coupled with sufficient battery power of 18 hours capacity.
The Macbook Air is not without flaws.
Standard improvements are not possible.
It only comes in an 8GB configuration as normal, and while it can be upgraded to a 16GB model, there’s not much space for improvement with an Apple laptop.
The storage choices for the Macbook Air are limited. You may certainly upgrade to a larger storage capacity drive at the time of purchase, but this is a very expensive option.
Should You Buy a PC?
PCs are the most used system in the world, in offices and generally, in most areas, a PC is most likely to be seen.
One of the reasons for this is due to its easy compatibility with hardware.
The Pros
Relatively Cheap
A PC is by far a cheaper system than a Mac, it is easily affordable.
Supports upgrade
PCs support a lot of upgrades, a Mac system does not allow most hardware upgrades.
When purchasing a system and considering carrying out an upgrade in the future, purchasing a PC would be better.
Has a subsystem for Linux
One of the reasons why a lot of people choose to use Mac computers is because of the UNIX experience.
Since PCs can use Linux Ubuntu for Windows, you can replicate UNIX for PC.
The Cons
Requires constant maintenance and checks to ensure your PC is up to date and can carry out tasks appropriately.
Slower speed with Data Science Tools
Data Scientists use many programs like Tableau, Anaconda, Python, and R. These programs run smoothly on Mac, but you might face issues of speed and compatibility when used on a PC.
Debugging is harder on PC
Debugging on a PC is quite a hard task and much easier in an OS. A PC is designed mostly for common people and not programmers.
Tableau on Mac or Windows
Generally, all data science software is more compatible with the Mac system.
This tells us that Tableau works and runs better on Mac as well.
Is it Better to Learn Python on PC or Mac?
No difference between the Python that runs on a PC and one that runs on Mac.
It most times boils down to personal preference.
According to Stack Overflow’s 2020 survey, 45.8% develop using Windows while 27.5% work on macOS, and 26.6% work on Linux.
What Mac is Best for Deep Learning
Apple Macbook Pro is the most recommended option as it contains amazing features and specs.
If you like Mac OS and don’t need to rely on the GPU substantially, this is the laptop for you.
Because of their Thunderbolt 3 connectors, Macbooks are capable workstations.
Out of the dedicated GPU laptops, their battery life is also a clear winner.
Can Tensorflow Run on Mac?
If you use a Mac, you’re likely running Apple Silicon on one of the most recent devices.
On M1 Macs, you’ll need to install Apple’s hardware-accelerated TensorFlow and TensorFlow Addons for macOS 11.0+ to use Apple’s ML Compute framework with native hardware acceleration.
Machine Learning on Mac
Mac offers avenues for a better experience with Machine learning using their products.
TensorFlow 2.4’s new TensorFlow macOS branch uses ML Compute to allow machine learning libraries to make full use of both the CPU and GPU in both M1 and Intel-powered Macs for considerably better training performance.