Are you a Data Scientist and need a good laptop for your work? Let’s see the best laptops for Data Science, for professionals or students of Information Sciences, which will make work much more comfortable and productive.
Best Laptop For Data Science
As a data scientist, you must handle a large amount of data, collect it, analyze it, and interpret it in the way that is most beneficial to a business. When handling such large quantities, you also need an efficiently running laptop to make work easier. Statistical analysis requires a lot of computing power, be it a laptop or a PC.
Data Science and Machine Learning
Data Science and Machine Learning are growing at an astronomical rate and companies are now looking for professionals who can examine that gold mine found in data and help them make quick business decisions efficiently. The number of jobs for all US data professionals has increased by an impressive number.
What is Data Science?
People have been trying to define data science for over a decade, Hugh Conway in 2010 offered an answer with this Venn diagram consisting of three circles: mathematics and statistics, subject matter expertise (domain knowledge to abstract and calculate), and hacking skills. Basically, if you can do all three, you already have a great deal of knowledge in the field of data science:
Data science is a concept used to address big data and includes data cleansing, preparation, and analysis. A data scientist collects data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from collected data sets. They understand data from a business point of view and can provide accurate insights and predictions that can be used to drive critical business decisions.
Big data and data science, data analysis needs computing power
As a data scientist, you must deal with a large amount of data, collect it, analyze it and interpret it, in the way that is most beneficial for a company. When handling such large quantities, you also need a laptop that works efficiently, making work easier. Statistical analysis needs a lot of computing power, be it a laptop or a PC.
The software data analysis, such as statistical software IBM SPSS or Statistics, requires a minimum of RAM for smooth operation in a moderate amount of data. The larger the size of the data you handle, the better hardware you will need. A good laptop with the right high-end hardware will help you deal with more data, without hanging up or giving you waiting times.
Best Laptop For Data Science |Comparison Table 2021
Here is a list of highly sophisticated laptops with excellent configurations that you can refer to for a better choice:
Image | Title | Brand | Buy |
---|---|---|---|
Acer Nitro 5 Gaming Laptop, 9th Gen Intel Core i5-9300H, NVIDIA GeForce GTX 1650, 15.6" Full HD IPS Display, 8GB DDR4, 256GB NVMe SSD, Wi-Fi 6, Backlit Keyboard, Alexa Built-in, AN515-54-5812 | Acer | Check Price | |
Dell XPS 13 9360 13.3" Full HD Anti-Glare InfinityEdge Touchscreen Laptop Intel 7th Gen Kaby Lake i5 7200U 8GB RAM 128GB SSD | Dell | Check Price | |
2020 Apple MacBook Air Laptop: Apple M1 Chip, 13” Retina Display, 8GB RAM, 256GB SSD Storage, Backlit Keyboard, FaceTime HD Camera, Touch ID. Works with iPhone/iPad; Gold | Apple | Check Price | |
Lenovo IdeaPad 3 15" Laptop, 15.6" HD (1366 x 768) Display, AMD Ryzen 3 3250U Processor, 4GB DDR4 Onboard RAM, 128GB SSD, AMD Radeon Vega 3 Graphics, Windows 10, 81W10094US, Business Black | Lenovo | Check Price | |
OEM Lenovo ThinkPad T490 Laptop 14” FHD Display 1920x1080, Intel Quad Core i5-8265U, 16GB RAM, 512GB NVMe, GeForce MX250, Fingerprint, W10 Home | Oemgenuine | Check Price | |
MSI GS66 Stealth 10SGS-036 Gaming & Entertainment Laptop (Intel i7-10750H 6-Core, 32GB RAM, 1TB PCIe SSD, RTX 2080 Super Max-Q, 15.6" Full HD (1920x1080), WiFi, Win 10 Pro) (Renewed) | MSI | Check Price | |
2020 Apple MacBook Pro with Apple M1 Chip (13-inch, 8GB RAM, 256GB SSD Storage) - Space Gray | Apple | Check Price | |
ASUS ROG Strix G15 (2021) Gaming Laptop, 15.6” 300Hz IPS Type FHD Display, NVIDIA GeForce RTX 3050 Ti, AMD Ryzen R7-5800H, 16GB DDR4, 1TB PCIe SSD, RGB Keyboard, Windows 10, Black, G513QE-ES76 | ASUS | Check Price | |
ASUS ZenBook 15 Ultra-Slim Laptop, 15”FHD Touch Display, Intel Core i7-10750H, GeForce GTX 1650 Ti, 16GB RAM, 1TB SSD, Innovative ScreenPad 2.0, Thunderbolt 3, Windows 10 Pro, Pine Grey, UX535LI-XH77T | ASUS | Check Price |
Prices and images pulled from the Amazon Product Advertising API on:
Best Laptop For Data Science | 2021 Products Overview
1. Acer Nitro 5
- 9th Generation Intel Core i5-9300H Processor (Up to 4.1 GHz)
- 15.6 inches Full HD Widescreen IPS LED-backlit display; NVIDIA GeForce GTX 1650 Graphics with 4 GB of dedicated GDDR5 VRAM
- 8GB DDR4 2666MHz Memory; 256GB PCIe NVMe SSD (2 x PCIe M.2 slots - 1 slot open for easy upgrades) & 1 - Available hard drive bay
- LAN: 10/100/1000 Gigabit Ethernet LAN (RJ-45 port); Wireless: Intel Wireless Wi-Fi 6 AX200 802.11ax
- Backlit keyboard; Acer Cool Boost technology with twin fans and dual exhaust ports
- The biggest data set this laptop can handle should be about
- The likelyhood a student or someone starting with Data Analysis encounters a data set of this size is very slim
- This is the most basic laptop for pretty much any type of data analysis
2. Dell XPS 13 9360
- This Certified Refurbished product is tested and certified to look and work like new. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, a minimum 90-day warranty, and may arrive in a generic box. Only select sellers who maintain a high performance bar may offer Certified Refurbished products on Amazon.com
- 13.3 FHD (1920 x 1080) Infinityedge Touch Display
- Intel 7th Gen Kaby Lake i5 7200U Dual-Core;
- 8GB of 2133 MHz LPDDR3 SDRAM, 128 GB M.2 SATA SSD;
- Built-in Webcam,USB 3.0, USB-C, Thunderbolt 3 via USB Type_C, Card Reader: SD family, Network: None, Wi-Fi 802.11ac, Bluetooth 4.1;
- The fact is that you can get into the same groove with any laptop as long as you install LINUX on it and at a much affordable price than a MacBook.
- Dell XPS series (along with the Lenovo ThinkPads) give you one of best compatibility out of the Box with all Linux flavors (especially Ubuntu)
- What these can do for you though is test your code on as much data sets these machines can handle for you to later ssh into computer farms where the real processing comes into play
3. Apple MacBook Air Laptop
- All-Day Battery Life – Go longer than ever with up to 18 hours of battery life.
- Powerful Performance – Take on everything from professional-quality editing to action-packed gaming with ease. The Apple M1 chip with an 8-core CPU delivers up to 3.5x faster performance than the previous generation while using way less power.
- Superfast Memory – 8GB of unified memory makes your entire system speedy and responsive. That way it can support tasks like memory-hogging multitab browsing and opening a huge graphic file quickly and easily.
- Stunning Display – With a 13.3” Retina display, images come alive with new levels of realism. Text is sharp and clear, and colors are more vibrant.
- Why Mac – Easy to learn. Easy to set up. Astoundingly powerful. Intuitive. Packed with apps to use right out of the box. Mac is designed to let you work, play, and create like never before.
- Simply Compatible – All your existing apps work, including Adobe Creative Cloud, Microsoft 365, and Google Drive. Plus you can use your favorite iPhone and iPad apps directly on macOS. Altogether you’ll have access to the biggest collection of apps ever for Mac. All available on the App Store.
- Easy to Learn – If you already have an iPhone, MacBook Air feels familiar from the moment you turn it on. And it works perfectly with all your Apple devices. Use your iPad to extend the workspace of your Mac, answer texts and phone calls directly on your Mac, and more.
- Fanless Design – Your MacBook Air stays cool and runs quietly even while tackling intense workloads.
- AppleCare – Every Mac comes with a one-year limited warranty and up to 90 days of complimentary technical support. Get AppleCare+ to extend your coverage and reduce the stress and cost of unexpected repairs.
- Environmentally Friendly – MacBook Air is made with a 100% recycled aluminum enclosure and uses less energy for a smaller carbon footprint.
- The GPU is powerful no doubt and will help by acting as an additional core when processing chunks of data. But it is not supported for CUDA core applications.
4. Lenovo IdeaPad 3 15″ Laptop
- Powered by the latest AMD Ryzen 3 3250U processor with Radeon Vega 3 graphics, the AMD multi-core processing power offers incredible bandwidth for getting more done faster, in several applications at once
- The 15. 6 HD (1366 x 768) screen with narrow side bezels and Dopoundsy Audio deliver great visuals and crystal-clear sound for your entertainment
- 128 GB SSD M.2 NVMe storage and 4 GB DDR4 memory; Windows 10 installed
- Keep your privacy intact with a physical shutter on your webcam for peace of mind when you need it
- Stay connected: 2x2 Wi-Fi 5 (802. 11 ac/ac(LC)) and Bluetooth 4.1; webcam with microphone; 3 USB ports, HDMI and SD card reader
- This is portable, portable is expensive but it’s got battery life and it’s pretty amazing too: ~10 hours.
- Since it’s mostly classes and learning to code/use libraries w/ small samples, most students will not even need the power of the Acer Nitro.
5. OEM Lenovo ThinkPad T490
- Processor: Intel Quad Core i5-8265U (1.6GHz - 3.9GHz, 6MB Cache)
- Features: 16GB RAM, 512GB NVMe, Fingerprint; OS: Windows 10 Home
- Display: 14 FHD (1920x1080); Graphics: NVIDIA GeForce MX250
- Warranty: 1 Year Lenovo Warranty / 1 Year Oemgenuine Limited Warranty
- Memory Upgrade | * - View Product Description for complete details and notes
- Another laptop capable of holding Unix-Like environment seamlessly with all the powerful specs needed to run large data sets are the Lenovo ThinkPads.
- Although for that you just need a basic machine ( and no need to invest on the laptops like the Pro, XPS 15 and high end ThinkPads)
- Although for that you just need a basic machine ( and no need to invest on the laptops like the Pro, XPS 15 and high end ThinkPads)
6. MSI GS75 Stealth-093 17.3″
- Display: 17; 3 inches 144Hz 3ms 5; 2mm 4 sided razor thin Bezel gaming Laptop FHD (1920x1080), IPS Level
- Graphics: NVIDIA GeForce RTX2080 8G Max Q GDDR6 w/New Ray Tracing Technology
- Processor: Intel Core i7 8750H 2; 2: 4; 1GHz
- Memory: 32GB (16G*2) DDR4 2666MHz 2 Sockets; Max Memory 32GB
- Storage: 512GB NV Me SSD
- Cooling: 3 fans, up to 47 blades each, 7 copper heat pipes, 4 exhausts
- Special Features: silky glass Touch pad 10+ gestures 35% larger, Thunderbolt 3, 28% slimmer power adaptor, MSI App player
- Keyboard: customizable per Key RGB backlit Keyboard w/ Glowing key edges, powered by steel Series engine 3
- LAN + Wi Fi: Killer Gaming Network E2500 + Killer N1550i Combo (2*2 ac) (Double Shot Pro), Killer extend (boost your signal)
- Operating System: Win 10 Home
7. Apple MacBook Pro
- Apple-designed M1 chip for a giant leap in CPU, GPU, and machine learning performance
- Get more done with up to 20 hours of battery life, the longest ever in a Mac
- 8-core CPU delivers up to 2.8x faster performance to fly through workflows quicker than ever
- 8-core GPU with up to 5x faster graphics for graphics-intensive apps and games
- 16-core Neural Engine for advanced machine learning
- 8GB of unified memory so everything you do is fast and fluid
- Superfast SSD storage launches apps and opens files in an instant
- Active cooling system sustains incredible performance
- 13.3-inch Retina display with 500 nits of brightness for vibrant colors and incredible image detail
- FaceTime HD camera with advanced image signal processor for clearer, sharper video calls
8. ASUS ROG Strix G17
- NVIDIA GeForce RTX 3070 8GB GDDR6 with ROG Boost
- Latest 5th Gen AMD Ryzen 9 5900HX Processor (16M Cache, up to 4.5 GHz)
- 300Hz 3ms 17.3” Full HD 1920x1080 IPS-Type Display
- 16GB DDR4 3200MHz RAM | 1TB PCIe NVMe M.2 SSD | Windows 10 Home
- ROG Intelligent Cooling thermal system with Thermal Grizzly Liquid Metal Thermal Compound
- Turbo clocking speed of up to 5GHz.
- The unique processor allows you to install Linux Distros onto the device, besides the existing operating platform.
- Efficient keyboard with curved keycaps, a backlit interface, fast actuating tabs, and the N-key rollover technology.
9. ASUS ZenBook 15
- Innovative ScreenPad: 5.65-inch interactive touchscreen trackpad that adapts to your needs for smarter control and multitasking
- App Switcher on ScreenPad: easily move docked windows between your main display and ScreenPad display
- Handwriting on ScreenPad: Take note or jot down your ideas by writing on the ScreenPad
- NumberKey on ScreenPad: Turn ScreenPad into a numeric keypad for easy numeric data entry
- 15.6 inch FHD NanoEdge bezel touch display
- Asus features the Nano-Edge display technology and the 15.6-inch screen comes with a display resolution of 1920 x 1080 pixels.
- keyboard productivity, Asus brings forth a backlit keyboard, followed by a 1.4mm key travel and quicker access keys.
- The ZenBook 15 comes with an ultralight chassis, weighing a mere 3.60 pounds.
- Asus features Gigabit Wi-Fi 6 and advanced Bluetooth connectivity for enabling data scientists to work with remote servers.
Specifications that a laptop for Data Science should have
The general motto for data analysis is:
“With larger data sets, you get more insights.”
Unfortunately, that also translates to a higher demand for hardware resources. So what is a good setup to start with for someone who is working with data science?
RAM memory required
RAM is the most important thing for data science because it is the main bottleneck with large data sets. Things speed up by an order of magnitude when all your processing is in memory or RAM. A 16GB RAM is ideal, but this is not always available on laptops under $ 600.
One piece of advice: Don’t go below 8GB!
Disk or SSD drives
The second factor is the hard drive. An SSD will make a huge difference, a cheap SSD will be 2-3 times faster than a normal hard drive. A good SSD will be 4-5 times faster.
CPU
Processing power is always good, but storage speed or RAM will most likely bog you down.
There is no point in being able to do a million calculations per second if your hard drive can only provide up to 1000 data per second.
After maximizing these, spend the rest of your budget on a “modern” CPU, not necessarily a fast “CPU”, because they are all fast today. Note that, unlike RAM and storage, these cannot be upgraded, so try to get the fastest you can afford.
Graphics card (GPU)
If you work with a deep neural network or just NN (parallel computing), get the graphics card with as many CUDA Cores / Shaders as you can. NVIDIA or AMD, not Intel HD cards.
Keyboard
It is not always possible to get an excellent keyboard with all the mentioned computing advantages. So if you’re going to type a lot, get an external keyboard and mouse/trackball.
My recommendation is to make sure they are ergonomic – RSI and tendonitis are unpleasant.
Display
Minimum 14-15 inches. You’ll probably end up getting into more powerful machines at some point, so the actual status screen/interface becomes very important as well.
Ports
Another advantage is making sure your laptop has a Thunderbolt (USB Type-C) port so you can transfer data to/from external drives at blazing-fast speeds. Most of today’s laptops have one.
OSX
Mac vs. Windows vs. Linux: Depends on the industry/company you work for or your personal preferences. But I would recommend opting for a laptop that can support a Linux-flavored operating system ostensibly like a Lenovo / MacBook.
A Linux-flavored operating system (Windows doesn’t connect well and requires a lot of extras to accommodate a typical workflow that ends up in the cloud) may at some point become your default operating system.
There is no best laptop for data analysis as such. In fact, any laptop would be good for analytics purposes if you do all your computing in the cloud.
Therefore, this section will mainly focus on those who are trying to do as much computing as possible on their new equipment and this, in turn, depends on the type of software they use and also the type of data analysis.
I’ll start with the basics for those just starting out in the field, perhaps using Lynda.com or learning the tools themselves. If you are not a beginner and plan to do all your data analysis at home, just skip to the hardware section.
Doing data analysis
There are two ways to perform data analysis: using the cloud or with your own platform.
A) The cloud: recommended for learning data analysis
Using the cloud means renting IT services from big companies like Amazon. Basically, you are leaving all computing/processing to your huge groups of computers.
If you go for a good cloud environment with an AWS subscription, you’ll get access to EMR multi-machine clusters on-demand at hourly rates. You will also have access to its other data stores like ElasticSearch and Redshift etc.
All you need at home is a basic laptop or desktop PC with 4-8GB of RAM and a decent internet connection (1Mbps). This will not only save you a lot of money but also time.
Other specs to consider when going this route are a long battery life (so you can do this on the go as well), a multi-core CPU (so you can multitask smoothly), and maybe a backlit keyboard for work at night.
B) Building a platform at home
Building an in-house platform for “big data analytics” is quite challenging. Laptops are out of the question. You will need several machines with:
- Multi-core processors (8-core AMDs are cheaper)
- Minimum of 16 GB of RAM per machine
- Storage drives in RAID configurations
On the other hand, if you are on a budget and still would like to build a cluster at home, you can always go for a used server setup:
- Check the listings on Amazon, Ebay or any other e-commerce site
- Post on social media and ask if anyone sells their old server
Software and Specifications
Just saying statistical analysis doesn’t tell you exactly what you’re going to need in a laptop. So in this section, I’m going to briefly go over the most widely used software in Data Analytics and talk about the specs that you should focus on.
Students
If you are a student, you will probably end up using a combination of the following programs/languages:
- R
- Python
- SAS
- SPSS
- Stata
- Tableau
- RStudio
- Rapid Miner
- MatLab
For that, you will only need a laptop with a decent workspace (keyboard + screen), as today’s modern laptops have enough CPU and RAM for all these basic languages and software. Any laptop with + 2.5 GHz and 2 cores + 8GB RAM should make working with all of that a breeze.
Also, what will not be necessary is big data processing. Universities have many servers and things for that.
Installing modules / extensions
What will be a real hassle is having the ecosystem fully installed and running on your machine. Both R and Python have dozens of modules that you can install for Data Science, none of which are easy to install.
There are guides everywhere, but it is also a matter of luck, sometimes it can be easy depending on your operating system and how exactly you install each one.
If you can’t support a Linux system, I would recommend a MacBook, any would be fine, even older models as they still have their software up to date.
Professionals
The software is pretty much the same, perhaps with the addition of RStudio, Rapid Miner, Spotfire, and most importantly, Hadoop. The latter implies, of course, the use of data sets in the GB range.
I would say that there are three types of data scientists depending on the problem they want to solve: volume, speed, or variety.
If you are a data scientist of the volume or speed type, the best laptop platform to go for is a laptop that allows you to easily connect to the cloud environments described above.
If you work frequently in the third V, several problems. You will benefit much more from an expensive laptop (relatively speaking).
Machine learning
And if you work with machine learning algorithms, then, as you probably know, you will get better results with more and more data, this translates to algorithms that need both CPU and memory. If you plan to do your data analysis on your laptop, then focus on CPU and memory.
If you use R and especially the RevoScaleR package, you can go as far as you need with more cores even from your GPU. So pay close attention to the CPU / Memory / GPU sections.
R
Dealing with long data sets with R is also easier with more cores. Getting more cores can help too, but only up to a point. R itself can generally only use one kernel at a time internally.
Also, for many data analysis problems, the bottlenecks are disk I / O and RAM speed, so efficient use of more than 4-8 cores on basic hardware can be difficult.
Hadoop
A common approach is to use a sample from the large data set, a large sample that can fit in memory. With Hadoop, you can now run many exploratory data analysis tasks on entire data sets, without sampling.
Just write a map reduction job, a PIG or HIVE script, run it directly in Hadoop on the full dataset, and get the results right on your laptop.
In many cases, machine learning algorithms perform best when they have more data to learn, particularly for techniques such as clustering, outlier detection, and product recommendations.
Historically, large data sets were either unavailable or too expensive to acquire and store, so machine learning professionals had to find innovative ways to improve models with fairly limited data sets.
With Hadoop as the platform that provides linearly scalable storage and processing power, you can now store ALL your data in RAW format and use the entire data set to create better and more accurate models.
Python / Pandas
Data Analysis – Using pandas to read CSV and Excel files, clean, filter, partition, aggregate and summarize data, and produce simple charts
Similarly, if your application requires joining large tables with billions of rows to create feature vectors for each data object, HIVE or PIG are very useful and efficient for this task.
Training a heavy neural network can be out of the reach of any laptop, as doing a large repeated measures analysis (variance/covariance matrix explodes exponentially)
All the answers are great.
Pay close attention to those sections.
Most machine learning algorithms are CPU intensive and memory intensive. Look for the Intel Core i7 processor, which is currently the best processor and the 4-core is ideal when you have to take advantage of the thread for large data sets. Remember that I am also talking about data manipulation work along with computing.
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