In the world of data science, there are many tools that you use, like software, apps, servers, and, most importantly, a powerful computer. When it comes to computers, there are two clear choices: Mac or PC. But which one is better for data science?
A Mac is a go-to choice for many data scientists, but it doesn’t mean a PC is not a good option. The reason for Mac’s advantage is that it is compatible with more apps and tools designed for data science. However, it comes with some disadvantages like cost and longevity.
In this article, we will tell you the advantages and disadvantages of choosing a Mac or PC for data science. We cover all factors like software, RAM, compatibility, usability, and price. So keep reading to learn the pros and cons of these two operative systems.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Do Data Scientists Do?
Data scientists are experts in computer analytics and problem-solving. Using different programs and algorithms, they use collected information to create solutions for any company or business.
A data scientist can recognize trends and patterns as well as analyze large amounts of information. They do this to find new ways of generating profit for a company. They also look for new ways to innovate already known methods for different purposes.
This makes them the newest big thing in the tech industry and professionals with a great starting salary.
If you are a data scientist, you need to choose the right machine and tools to work with.
Pros and Cons of Using a Mac for Data Science
Mac presents many advantages over the PC when it comes to data science. It is a highly capable machine and gets along well with most tools for data science. Both iMacs and Macbooks have many benefits for data scientists.
MacBook Pros are lightweight and show no problems with their WiFi cards, after even years of use. Even the lighter and less powerful model, MacBook Air, will give you no problems when using it for data science.
Even though Macs are strong and durable, they have disadvantages too. The price is the biggest concern for many users, and you will have to invest in other hardware if you choose Mac. They don’t have HDMI or even regular USB ports.
Pros of Using Mac for Data Science
As mentioned before, there are many pros of using Mac for data science. There is a reason why many programmers and data scientists prefer Macs over any other machine.
The main advantages are Wi-Fi card durability and power, the user-friendly operating system (OS), and the compatibility with many data science tools and apps.
Here’s a list of all the advantages of using Mac for data science.
Highly Compatible With Data Science Apps and Software
Data scientists need to use many programs like Anaconda, Tableau, Python, and more. All of these programs are compatible with Mac and run smoothly on the interface.
The reason is that there was a trend with the first data scientists to use Mac for work. This meant that many of these programs started to focus on making their product friendlier to Mac OS than other operative systems.
Therefore, you should consider which programs you need to use in your data science job. If they include the above programs, then they are better suited to Mac.
Its Wi-Fi Card Is Reliable and Durable
One key aspect for data scientists is the machine’s capacity to withstand long periods of working with online servers.
Most of the data mining and science will be done on a server, so choosing a reliable machine for the work will be key. Mac’s wireless card is highly durable and known by many to be long-lasting and powerful.
A Lightweight Option
For many data scientists, work does not mean being in front of your desk all day. Most prefer to work wireless, which means you’ll need a laptop and one that can be transported easily.
MacBook Air will give you no problem when using it for data science, but if you want something more powerful, you can try out a MacBook Pro 13’’ or 15’’.
Very Usable
User-friendless has always been a big priority for Apple. It is one of the reasons many data scientists prefer Mac over Pcs with either Windows or Linux.
Mac is easy to use and work with, and that is important when your work requires you to be in front of a screen for many hours.
Cons of Using Mac for Data Science
When it comes to the disadvantages of the Mac, it can all be summarized with two factors: price tag and end-to-end control.
Keep reading to know more about the disadvantages of using Mac for data science.
Expensive
There is no doubt that for the price of a Mac, one could buy a PC, some new jeans, and a Huawei phone. You could even save some money for a good pair of headphones.
Macs are expensive, and it all comes down to whether you can afford it or not. They are great machines, but they require a bigger investment than many others. You won’t be able to use non-Apple hardware on them, which will make you invest more in Apple items.
End-to-End Control Is an Apple Problem
Steve Jobs focused his strategy on creating what is now known as the end-to-end control strategy. It means that you cannot use non-Apple headphones on an Apple computer unless you buy the correct Apple adapters for it.
You will need to invest in adapters for USB ports as Apple now works with USB C, and HDMI ports are not included either. This increases the original investment and makes Mac less suitable for data scientists that need to attach external devices.
Almost Impossible to Add More RAM to a Mac
Another issue that comes from the end-to-end control of Apple is the incapacity to update your hardware. If at some point you want or need more memory, RAM, or a better graphic card, then you will have to buy a new Mac.
Some Models Are Less User-Friendly Than Others
When you are programming or data mining, you tend to use your keyboard a lot. Now, think about a touch bar instead of proper keys. That became a highly annoying issue for a lot of data scientists that bought the 2018 models of MacBook Pro.
The newer models returned to using a key for certain characters like ESC, but only after the many complaints of users.
If you are going to choose a MacBook or iMac, use one without a touch bar. You’ll be grateful for it.
Pros and Cons of Using PC for Data Science
PC is the most used machine for many industries, and it has also found its way into data science. PC is cheap, compatible with many types of hardware, and can be of great support for data scientists if they use the correct operative system.
With a PC, there are many OS options, but the ones that work better for data scientists are Linux Unbuntu and Windows. The combination of both Windows 10 and Linux Unbuntu makes for an excellent data science tool.
Another OS, like ChromeOS, is not compatible with many major programs and languages. Therefore, we recommend against using it if you are a data scientist.
Keep reading to know the many advantages and disadvantages of using a PC for data science.
Pros of Using PC for Data Science
When compared with the Mac, Windows’ best characteristic is the price. It is cheap and easy to update and offers a lot more options for OS than Mac. A dual boot OS with Linux Ubuntu and Windows 10 will give you similar results to the Mac OS X.
Keep reading to know all the advantages of using a PC if you are a data scientist.
- A lot cheaper. Macs are notoriously expensive. If you cannot afford one, then the best option for you is a PC. It is reliable, and with the minimum investment, you’ll be able to work data science without a problem.
- Easier to update. Data scientists need a strong computer capable of managing multiple tasks at the same time. But, as time moves, you will need to update your computer. PC is the only option for this. There’s no end-to-end control in PC. If you need a bigger RAM or graphic video card, you just need to buy and install it. This is something that cannot be done on a Mac.
- Has a subsystem for Linux. Mac OS X is the best operative system for programming, but there is a cheaper option. By using Linux Ubuntu for Windows, you can replicate the Unix experience of the Mac, meaning your computer will work with two different OS instead of one. This is called a dual boot, and it is a game-changer for Windows. With this dual boot, you will be able to work in almost any language from a PC.
Cons of Using PC for Data Science
Many experts will tell you that a PC with a Windows OS is a big no for data science. They are not wrong. Windows, by itself, is a tricky OS for data science and analytics. If things go wrong at some point, it is a lot more difficult to debug a PC than it is a Mac.
PC presents many disadvantages against Mac, and we will list them all for you.
- Not the most compatible option. As mentioned before, there is a key issue with the PC. Many programs designed for data science and statistics are not compatible with PC’s OS. The reason is that many providers prefer to focus their products on Mac rather than PC because Mac is the most used by programmers.
- Less reliable for programming and data science. Debugging on a PC can become an impossible task. The reason is that PC and WIndows focused their products on the common people, not on programmers. It was a good strategy, but it backfired when it came to data science.
- Requires a lot of maintenance. Contrary to Mac, PC requires constant maintenance to ensure that your computer will be able to manage all the tasks needed for data science. It is cheaper, but with that comes this problem.
Is R Better on Mac or Windows?
R is a programming language and free software environment. It is used by data scientists to perform data mining, statistics, and more. Because R is essential during the data science process, data scientists must choose a computer that supports it.
When it comes to R, both PC and Mac will give you great support, but Mac is the go-to. It is easier to debug. Furthermore, many experts find it better for data mining and programming than a Windows PC or even one with a dual boot like Linux Unbuntu and Windows.
How Much RAM Do You Need for Data Science?
When it comes to RAM, the best option is as much as you can afford. RAM is like the brain of the computer. The amount of RAM determines how quickly your computer can read its programs and processes. Data scientists need a minimum of 8Gb of RAM.
This is because many programs and algorithms need a lot of power to run and you will be running them at the same time in most cases.
The higher your RAM, the more programs and tasks your machine will be able to run. If you can go for 16 or more, then we recommend you to do so.
Mac offers you 8GB and more, but their prices can be a lot higher than a perfect Dell with 16 of RAM. Therefore, choose according to your budget.
Is Mac Better for Programming?
Many programmers choose Mac over any other because of one reason: the OS.
Mac OS X is the favorite Operating System for programmers because it is the only OS to be certified Unix. This is an important feature as Unix allows you to run programs in almost any language. PC users will need to use a specialized integrated development environment (IDE). However, Mac users do not.
Another reason for choosing Mac is that almost every software provider offers a reliable Mac version. This is not the case for other OS in the market. Furthermore, to develop software or apps for OS X or IOS, you must use a Mac. It still allows you to create software for Windows, Linux, or other OS.
Furthermore, Mac requires less maintenance, although it does cost a lot more. So it all comes down to affordability.
Which One Should You Choose?
Macs are the go-to choice for data scientists and programmers. There are many reasons for it, but that doesn’t mean a PC is a bad choice.
If you are going to buy a computer for data science work and programming, then ask yourself the following:
- Do I need to move a lot during work? If the answer is yes, then a PC or iMac should not be your option. Go for the laptops. A MacBook Pro 13’’ is the best option, but a MacBook Air will give you no problems.
- How much RAM do I need? Always go for 8GB or more. This way, you’ll be able to use your computer for many tasks at the same time.
- Should I go for the exotic options like touch bars or flip screens? This is a definitive no. They look fine, they are fashionable, but they are not usable when it comes to programming or doing data mining.
- How reliable is the computer for programming and data science? You need one that can read many languages and work with many programs. So compatibility is a must. Regardless of a Mac or a PC, choose one capable of using R or other languages and programs.
- Which OS should I choose? Do not choose ChromeOS. There is nothing wrong with it, but it is not useful for data science. It is not compatible with most programs and languages, becoming a problem sooner or later.
At the end of the day, most of the work will be done online, on servers that will require you a good WiFi connection. Yet, you need a powerful computer with a good OS and reliable hardware if you are going to work in data science.
Considering all this, there is no option better than Mac. It is the reason why many programmers decide on Mac even with the high price tag.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Mac and PC are both great options, and as said before, it all comes down to affordability. If you can afford a Mac, then go for it. They are great machines and will give you no problem when using them for data science.
Try to choose the ones without a touch bar. Both the laptops and desktops are suitable, but if you won’t need to move it much, then an iMac is a good option.
If you cannot afford one or prefer not to buy one, then a PC with a dual boot OS of Windows and Linux is another good option. You can buy a Dell Inspiron or a Lenovo Thinkpad E15, which are both great options for data scientists.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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