Data science is at the epicenter of most decision-making processes in business. Turning raw data into meaningful information helps businesses and other organizations make informed decisions. As a data scientist, you play an important role in collecting, analyzing, and helping other people make sense of your findings. However, all this would not be possible without a good laptop. We have specifically prepared this guide to help you find the best laptops for data science this year. We did extensive research and tested these laptops based on features that are important to a data scientist.
- Our Top Laptops for Data Science
- Best Overall Laptop for Data Science: Asus ROG Strix Scar III (2019)
- Best Laptop for Data Science for the Money: ASUS TUF (2019)
- Best Cheap Laptop for Data Science: Acer Aspire E5
- Best Laptop for Data Science for Battery Life: MacBook Air
- Best Designed Laptop for Data Science: Asus VivoBook S15
- Best Laptop for Data Science for Linux Distributions: Dell XPS 13 9630
- Best Durable Laptop for Data Science: Acer Predator Helios 300
- Features to Consider in Good Data Science Laptops
Our Top Laptops for Data Science
Data science is fairly different from other professions. Whether you are a professional data scientist or a student, these laptops will make your work easier. All of them are compatible with popular data science tools such as SAS, Apache Spark, Microsoft Excel, MATLAB, Python, Tensorflow, and BigML.
Best Overall Laptop for Data Science: Asus ROG Strix Scar III (2019)
The Asus ROG Strix Scar III is a big name in the gaming industry because of its powerful computing power and incredible hardware. This is the perfect choice to use for big data sets if you are willing to look beyond the staggering price tag.
Most gaming laptops are undoubtedly good for data science. This is because they offer more computing power and come in handy when running different data analytics software concurrently.
It comes with less than inspiring aesthetics and weighs over 5 pounds which might be too heavy to carry around. However, if you are looking for a laptop that will quickly help you make sense of complicated data, this laptop is your best option. It is no surprise that an ASUS model is our recommendation.
- Nvidia GeForce RTX 2060 6GB GDDR6 (base: 1110MHz, Boost: 1335MHz, TDP: 80W)
- Latest 9th Gen Intel Core i7-9750h Processor.Bluetooth 5.0
- 240Hz 15.6” Full HD 1920x1080 IPS Type Display
This brand has a reputation for powerful performance and while the high-end laptops are very pricey, they are worth every penny.
We mention in the last section of this guide that a processor is very important when it comes to data analytics. The Asus ROG Strix Scar III offers one of the fastest in the market.
It is the 9th generation Intel Core i7 with a clock speed of 4.1 Gigahertz. This coupled with 16GB of RAM and 1TB PCIe NVMe SSD storage, makes this laptop the best option for data science.
This is especially true if you are dealing with data sets that require a tremendous amount of storage and processing power. The NVIDIA GeForce RTX 2060 6GB VRAM GPU solidifies the ROG Strix Scar III’s credibility as a stellar choice for advanced computation, deep learning, and analytics. This GPU is great for image analysis and parallel computing in machine learning.
Like most powerful computers, the Asus ROG Strix Scar III doesn’t have the blessing of long battery life. This is a compromise to expect with such power internals. On the brighter side, this laptop offers great versatility since it can be used for any other purpose apart from data science.
For instance, there are features such as high refresh rates and G-sync which you might not need for data analytics but come in handy during gaming.
What We Like
- 1TB PCIe NVMe SSD storage for faster performance and securely storing all your data
- 16GB of DDR4 RAM will ensure faster performance when running advanced analytics software
- Powerful NVIDIA GeForce RTX 2060 GPU for image analysis, parallel processing, and gaming
- Impressive 4.1GHz Intel Core i7-9750H for faster data processing especially with huge data sets
- A huge 15-inch display for clearly viewing and working with data sets and graphical representations
What We Don’t Like
- It is heavy and therefore not a good option for data scientists who work on the go
- It is very expensive
- Mediocre battery life may be inconvenient when working away from a power source
Best Laptop for Data Science for the Money: ASUS TUF (2019)
The Asus TUF is a slight step back from the Asus ROG Strix Scar III. However, if you are looking for a lighter option, with a less scary price tag and longer battery life, this laptop will be a good fit. While its features are not as robust as those of the former, it still has a lot to be admired for.
We recommend this laptop for data scientists who are looking for a laptop that offers the best balance between functionality and affordability. Just like the ROG Strix Scar III, the Asus has built a name for itself in the gaming industry and although it still lags behind top competitors, it is a great choice for data scientists.
- Nvidia GeForce GTX 1660 Ti 6GB graphics (base: 1455MHz, Boost: 1590MHz, TDP: 80W)
- Quad core AMD Ryzen 7 3750H Processor. Battery-48WHrs
- 15.6” full HD (1920x1080) 120Hz IPS type display
Although it comes with a friendlier price, this has a few things in common with the pricier Asus ROG Strix Scar III. These include RAM, display size and support for PCIe NVMe SSD. The storage is not as generous as that of our top recommendation for still sufficient enough for this price point.
We love that the Asus TF combines SSD and HDD storage which offers both convenience and faster performance. There is 256GB of SSD storage with 1TB of HDD storage.
This means that your laptop will be able to run fluidly while providing enough space to store big data sets. The AMD Ryzen R7-3750 processor and 16GB DDR RAM will effortlessly take care of hardcore processing and computation.
While this processor is not as powerful as that on the Asus ROG Strix Scar III, it will comfortably handle complex data sets and advanced data science tools such as Python, Jupyter, SAS and Adobe Spark.
The GeForce GTX 1660 Ti GPU with 6GB VRAM will comfortably take care of image analysis and creation of graphical representations of data such as charts, histograms, bar graphs, line plots, and frequency tables.
Although the 15.6-inch Asus TF is lighter than Asus ROG, it is still fairly heavy. You might, therefore, need a laptop bag if you intend to carry it around.
What We Like
- 256GB SSD, plus 1TB HDD for amply storing your data locally
- 16GB RAM ensures faster performance when retrieving data sets or running heavyweight analytic tools
- Powerful AMD Ryzen R7-3750 processor for effortlessly running analytics software and quickly retrieving and analyzing large data sets.
- Top-notch GeForce GTX 1660 Ti GPU for image analysis and creating graphical representations.
- Big 15.6-inch display with decent viewing angles for clear data output
What We Don’t Like
- Doesn’t come with a USB Type-C port which is important for fast data transfer between devices
- Still expensive
Best Cheap Laptop for Data Science: Acer Aspire E5
The Acer Aspire E5 is a good option for people who are just starting on data science such as students. It has less powerful internals with a forgiving price. However, it is still competitive for basic to medium data science projects.
If you are a student currently doing your research and need a laptop to help you make sense of the numbers, this laptop will be a good fit. Although it doesn’t pack powerful features like our first two recommendations, the Acer Aspire E5 can breeze through basic statistical and ML/DL models when dealing with smaller data sets.
- 8th Generation Intel Core i5 8265U Processor (Up to 3.9GHz) | 8GB DDR4 Memory | 512GB PCIe NVMe SSD
- 15.6" Full HD (1920 x 1080) widescreen LED backlit IPS display | NVIDIA GeForce MX250 with 2 GB of...
- 1 USB 3.1 Type C Gen 1 port, 2 USB 3.1 Gen 1 ports (one with power off charging), 1 USB 2.0 port & 1...
This means you can run most data analysis tools such as SAS, R, and MatLab without bottlenecking the laptop’s performance. Small data set in a case means anything that occupies around 300MB or not more than 300,000 rows with 4 variables each.
Since this laptop comes with 8GB of RAM you will be left with plenty of memory after subtracting about 3GB used by the operating system and other applications. If you are looking to push the laptop to its limits, make sure not to exceed a data set of more than 6 million rows with 4 variables each.
The good news is, it is unlikely that you will come across such a big data set if you are a student or just getting your feet wet with data analysis. However, in case you do, we recommend that you use Cloud-based analytic software for faster processing. This way, you can do your analysis remotely and have the files stored in the cloud.
You can alternatively upgrade the RAM to 16GB if you encounter bigger data sets of up to 12million rows with 4 variables. The 512GB SSD should help speed up processing when dealing with manageable data size.
What We Like
- A decent Intel Core i5-8265U CPU for entry-level data processing
- It’s affordable and a good option for new data scientists or data science students
- The 8GB RAM is sufficient for small data sizes and light analytics tools
- Sufficient 512GB SSD PCIe NVMe for storing small data files
- High quality 15-inch Full HD display with IPS panel
- Decent battery life for those working away from power sources
- Portable and easy to carry and use on the go, especially by data science students
What We Don’t Like
- Not suitable for bigger data sets
- Cannot handle advanced statistical software
Best Laptop for Data Science for Battery Life: MacBook Air
While the Acer Aspire E5 above is recommended for students and beginners, the MacBook Air is a good fit for both beginners and veteran data scientists. This laptop makes it very easy to install data science tools such as python and libraries.
We recommend this laptop for data scientists who need long battery life. The MacBook Air can last up to 13 hours making it a great choice for people who don’t like walking around with their laptop chargers. For instance, students who already have stuff in their backpack will find the additional weight of a charger bothersome thus the need to leave it behind.
- Apple-designed M1 chip for a giant leap in CPU, GPU, and machine learning performance
- Go longer than ever with up to 18 hours of battery life
- 8-core CPU delivers up to 3.5x faster performance to tackle projects faster than ever
The MacBook Air is also a good option for students since it is very lightweight at only 3 pounds. This makes it easy to carry around the school when quickly moving from one point to the other. The same goes for data scientists who love to work on the go.
There is sufficient RAM for managing sizable data sets. However, it goes as far as 16GB. So, if you want more you can upgrade to the 16-inch MacBook Pro which goes up to 32GB with the more power AMD CPU for parallel processing
What We Like
- The 8GB of RAM will ensure effortless processing of medium data sets
- It has up to 13 hours of battery life which is good for doing data analysis away from the data source
- It weighs only 3 pounds which makes it a good option for students and data scientists that move around a lot
- The 256GB SSD storage is enough for storage big data files
- A 2.9GHz Intel Core i5 processor for seamlessly running medium-level statistical software and big data sets
What We Don’t Like
- The 13-inch display is a bit small for data science work
- Integrated Intel HD graphics may not be good enough for graphical analysis of data
Best Designed Laptop for Data Science: Asus VivoBook S15
Most powerful laptops tend to come with uninspiring aesthetics. That is why we don’t automatically associate sleek designs with powerful performance. However, the Asus Vivobook S15 brings a surprising balance between performance and design.
Right off the bat, the VivoBook S15 is attention-grabbing. It packs a durable and stylish metal chassis that is not only easy to notice but also love. This is probably not the first laptop that comes to mind when looking for the best options for data science.
- 15.6 inch Full HD 4 Way NanoEdge bezel Display with stunning 87% screen-to-body ratio
- Powerful Intel Core i7-8565u Processor (8M Cache, up to 4.6 GHz)
- 8GB DDR4 RAM and 256GB PCIe NVMe SSD + 1TB HDD and Windows 10 Home
Its design is more suitable for the office or remote works. However, looking at the features, you will easily understand why we included it in our list of the best laptops for data science.
The 15.6-inch comes with a powerful Intel Core i7-8565U Processor for handling gigantic data sets and advanced analytical tools. The processor is complemented with 8GB of RAM for effortless data processing and computing. With these two, you can comfortably handle medium to large data sets without strangling the laptop’s performance.
If you choose to use cloud storage, that would be perfect. However, for local storage, you have 256GB PCIe SSD plus 1TB HDD. This is enough to securely store all your data files.
The SSD storage will also boost the laptop’s performance by making data retrieval and processing faster. On the other hand, the 1TB HDD will provide sufficient space to store sizable data sets locally.
We also found the keyboard an interesting choice for data scientists. It is backlit with three brightness levels providing a fulfilling typing experience even in dim conditions. The keys are soft, well-spaced with decent travel.
What is more, the Keyboard uses the ErgoLift design to provide a more comfortable typing position. This is a good choice for professionals who love speedy data entry and computation.
For the security-conscious data scientists, there is a responsive fingerprint sensor that is activated through Windows Hello. This will help provide extra protection for your data aside from the passwords.
The 15.6-inch Full HD display provides ample real estate for viewing and working with your data. It utilizes the NanoEdge innovation which makes the bezels incredibly thin providing an 87% screen-to-body ratio.
What We Like
- 15.6-inch Full HD display for clear viewing and working on your analysis
- A comfortable backlit keyboard for speedy typing while entering data
- Sufficient space for secure data storage and faster performance
- Powerful processor and 8GB RAM to handle big data sets and data analysis software
- Sleek design and lightweight making it easy to work with on the go
What We Don’t Like
- A bit pricey making it unfit for data science students and professionals on a budget
- Becomes hot when used for long hours
Best Laptop for Data Science for Linux Distributions: Dell XPS 13 9630
Many data scientists are big fans of the Linux OS because of its flexibility and variety of data science tools to use. The Linux distributions system will give you access to the niceties of a full UNIX environment for less as compared to what other operating systems offer.
This is why it is a good thing to have a laptop that is well compatible with Linux Distributions. In this case, we recommend the Dell XPS 13. You can easily install Linux Distributions on the XPS and use some of these tools for data analysis. These include Ubuntu, Fedora, CentOS, and Mint.
- Intel 8th Generation Quad-Core i5-8250U 1.60 GHz (Turbo 3.40 GHz, 4 Cores 8 Threads, 6MB SmartCache)
- 13.3" InfinityEdge Touchscreen FHD (1920x1080) Display
- 128GB M.2 SSD | 8GB 1866MHz LPDDR3 RAM
Having access to the sea of Linux Distributions will give you more flexibility when working with your data. You won’t have to install many different tools to perform different aspects of your data analysis.
The 8th Generation Quad-Core i5-8250U processor on the Dell XPS 13 should be able to comfortably handle any Linux Distro you choose. It has a clock speed of 1.6GHz which can be increased to 3.40GHz with Turbo Boost.
It is also quad-core meaning it can handle parallel processing for faster data analysis and computation. The 8GB LPDDR3 RAM will help boost performance and make sure the processor has easy access to data sets and other important files.
While the 13.3-inch Full HD display is not as big as we would have love, it is still very sharp with impressive color accuracy making it a good option for image analysis. The 128GB M.2 SSD storage is insufficient if you are dealing with big data sets.
In this case, we recommend that you either invest in a hard drive or use cloud storage so that the SSD storage can be utilized for performance purposes.
One other downside is that the XPS 13 is not fully compatible with NVIDIA’s CUDA platform for parallel computing since it uses Intel HD graphics. So, if you want to take advantage of this technology you will have to upgrade to the Dell XPS 15 which uses the NVIDIA graphics card.
What We Like
- Easy compatibility with Data Science Linux Distributions
- The 8th Generation Quad-Core i5-8250U processor will ensure seamless data analysis and smooth running of data science tools
- High-quality display for creating graphical representations of data
- Decent battery life makes it ideal for working on the go
- Weighs only 3 pounds thus it’s lightweight and easy to carry around
What We Don’t Like
- The 128GB SSD storage is insufficient especially if you are storing big data sets locally
- While it’s of high quality, the 13.3-inch may be too small for expansive distribution tables and graphs
Best Durable Laptop for Data Science: Acer Predator Helios 300
If you are looking for a data science laptop that you can use for a long time without having to replace, we recommend the Acer Predator Helios 300. Just like the ASUS ROG and ASUS TUF, the Predator Helios 300 is a gaming powerhouse that will easily feel at home in a data science room.
Although this laptop carries a huge price tag, it offers great value for money. If you consider what you are getting, it is easy to look beyond the price and invest in a laptop that you will be using for longer than most of our recommendations. Although the Acer Predator Helios 300 has many selling points, its durability and strong build quality got our attention more.
- 8th Generation Intel Core i7-8750H 6-Core Processor (Up to 4.1GHz) with Windows 10 Home 64 Bit
- NVIDIA GeForce GTX 1060 Overclockable Graphics with 6 GB of dedicated GDDR5 VRAM
- 15.6" Full HD (1920 x 1080) widescreen LED-backlit IPS display (144Hz Refresh Rate, 300nit...
It has an all-metal chassis and feels strong and sturdy. This is a good laptop for environments that are prone to accidents. On top of that, the Predator Helios 300 also offers unmatched performance.
It combines an 8th Gen Intel Core i7 processor with high-performance NVIDIA GeForce GTX 1060 graphics to breeze through advanced data science software and handle gigantic data sets.
The 15.6-inch display is another major selling point, especially for games and data scientists. It has a 144Hz refresh rate with Full HD resolution for high-quality motion pictures and graph designs.
What We Like
- Powerful 8th Generation Intel Core i7-8750H 6-Core processor for seamlessly running modern data science tools and handling large data files
- 16GB of RAM to make data analysis faster
- 256GB SSD for improving performance and storing big data files
- An Impressive NVIDIA GeForce GTX 1060 GPU for better display quality when designing graphics for data representation
What We Don’t Like
- Short battery life due to the powerful CPU and GPU
- Too expensive for data science students and new professionals
- Weighs 5.51 pounds which makes it heavy and unsuitable for use on the go
Features to Consider in Good Data Science Laptops
Below are some of the features you should consider when buying a good laptop for data science.
For a data scientist, storage is very crucial. There is a lot of important information to be stored. Losing some of this data would be disastrous both for you and your company. That is why it is important that you buy a laptop with enough storage to store all of your work.
When it comes to storage, you have the option of either storing your data on the cloud or locally on your laptop. Both of these options have their pros and cons. For instance, with cloud storage, you can access the data anywhere using any device. This also helps to save storage on your computer.
However, this data is more vulnerable to breaches and unauthorized people could have access to them. You will always need an internet connection to access them.
On the other hand, data stored locally on your computer is more secure and you don’t need an internet connection to access it. The only problem is that you can only access this data with the specific computer it is saved in.
Assuming that you will be saving the data locally, you have two options; Solid State Drive (SSD) or Hard Drive Storage (HDD). While an SSD is faster, it is often expensive and limited. HDD is slower but provides more storage and is much cheaper.
A good option for data scientists is to get both SSD and HDD storage. SSD will help boost the performance of the laptop while you use HDD to store the data.
We recommend at least 256GB of SSD storage and not less than 1TB HDD storage. This will enable you to efficiently store all your data and effortlessly run important software and the operating system.
If you need more storage, feel free to use a flash disk.
Operating systems usually come down to Windows, macOS or Linux. The kind you choose depends on software compatibility, personal preference, type of laptop and your company.
Most veteran data scientists are against using Windows or Mac for serious data analytics.
Instead, they recommend Linux which is free, offers faster speeds and is much more flexible with a host of software and applications to choose from.
However, in most cases, the Operating System is not that important for data scientists. What is important is the kind of software and tools you are using for your work.
This is what will decide that kind of OS to use. So, if you are working for a company, they will probably have their own preferences of what OS to use based on past experiences.
A competent graphics card is a necessity for any serious data scientist. Remember that there is a lot of graphical work involved which is why good display graphics are a must.
With the popularity of parallel computing rising, many data analytics software and processes such as neural networks depend on the GPU for top-hole performance.
Any search for the best-dedicated GPUs invariably boils down to three options; Intel, NVIDIA and AMD.
If you are looking to leverage on parallel processing, NVIDIA graphic chips are more than Intel and AMD. So, in this case, we recommend the NVIDIA graphic cards for serious data analysis and presentation.
Most data analysis software and platforms utilize NVIDIA’s discrete graphics especially those that use the CUDA Core processing technology. These include Azure ARC and AWS Outposts.
Additionally, most data algorithms are designed to take advantage of the GPU instead of the CPU especially in cases where machine learning is involved.
When it comes to display, we usually look at two things; size and resolution. For data science, you definitely need a bigger screen. In this case, you should avoid anything below 15 inches.
In terms of resolution, you should aim for a Full HD or 4K depending on your budget. Higher resolution will help you see data clearly especially where graphs and charts are used.
If you need an even bigger display than what a standard laptop can afford, you can use an external monitor. Most modern laptops support peripheral monitors which should enable you to upgrade the display size.
Being the brain and heart of the computer, the Central Processing Unit is at the epicenter of any data analysis task. It is, therefore, important that as a data scientist, you look for a laptop with a powerful CPU to handle substantial data sets.
In this case, we recommend either an Intel Core i5 or Core i7 depending on the size of your data, analytics software and whether you are using cloud or local storage.
Before we dive into which processor to choose for given data analysis, let’s have a quick refresher.
When looking for a CPU for data analysis, you probably need the latest generation with higher numbers. More cores mean the processor is faster. The problem is, the way processors are named is a bit confusing. However, there is a super-easy way to tell what generation a processor is and the number of cores it has.
The generation is simply the first digit of the CPU’s model number after the dash (-). For example, i7-6700HQ is a 6th generation CPU while i7- 8750H is an 8th generation CPU. The number of cores is indicated by the last letter(s) of the model number. Usually, dual cores end with U while those with four or six cores end with H or HQ.
Based on the number of cores, for small data sets, you can choose a CPU that ends with a U and H/HQ series for chunkier data.
If you are storing data on the cloud, the processor will come in handy during multitasking. In this case, 7th and 8th generation CPUs will be fine. If you are working with small data sets, you will want to avoid Core i7s since they are expensive, hard on the battery and really necessary in this case. However, for bigger data, it is recommended if you need first processing and optimum overall performance.
Core i7s are better since they mostly allow you to upgrade the RAM and SSD storage for faster performance. Even better, the latest generations support PCIe NVMe SSD which is more than 15 times faster than HDDs regardless of the storage amount.
If you are doing parallel data processing (a common thing with most data scientists), you should focus more on CPU cores than clock speed.
Running data analytics software with low RAM can be a nightmare. Luckily for you getting a good laptop with sufficient RAM these days is less expensive. While we recommend 16GB and 8GB for those on budget, you should go as high as you can depending on your data set or analytics software.
Some data analytics software and applications have advanced functionalities that will require more RAM than others. On the other hand, complicated or bigger datasets will need more RAM storage than smaller ones.
So, why is RAM important for a data scientist? Here is what happens.
When a computer is doing data analysis or computations, the data set is temporarily held on the Random Access Memory for faster processing. Although the SSD or HDD storage on its own can perform this task, it takes much more time for the processor to access the data making the whole process slower.
So, how much RAM do you need for data analysis? It depends on the platform and size of the data set.
If you are using cloud storage, we recommend you get at least 8GB. However, if you are storing everything locally on your computer, the amount of RAM you need will depend on the size of the data set.
For instance, for a small data set of up to 200,000 records, 200 variables and size of 300MB, 4GB will be sufficient. However, if you are working for a large company or with data sets that are, say 30 times bigger than the above, you will need more.
In this case, you should look for at least 8GB of RAM. Of course with more RAM, you should be able to do the data analysis faster and work with chunks of data without crushing your laptop.
We hope that this guide will help you buy the best laptops for data science. Remember to look out for features that will make your work as a data scientist much easier. All our recommendations above pack powerful hardware and software for any data scientist, so you should be able to find your pick without much hassle.