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The field of data science is one of the most demanding in terms of hardware requirements. However, there are many types of data science and each specialization has its own data processing requirements. In other words: The laptop or desktop computer you buy should meet the specific requirements of your field.
For example, high-performance processors are an absolute necessity for those working with code and datasets, but a high-end graphics card is the holy grail of machine learning and deep learning subdomains, as multiple compilations, simulations and executions are required to process different models.
That’s another reason why it’s important to buy the right laptop that meets your individual needs and allows you to learn and work efficiently while collecting and analyzing data.
To help you out, we’ve researched the best laptops for data collection and compiled a list of the most promising options. This list ranges from laptops that offer good performance for computer science students to powerful laptops that can handle a variety of computing parameters, such as tools and frameworks, programming languages, machine learning models, and data sets.
Requirements for portable computers for data acquisition
To make sure you find the laptop or computer that’s right for you, check out this list of minimum hardware requirements for data laptops. This way, you can make an informed decision on which laptop will best meet your needs and requirements.
Processor
If you want the best value for your money, choose an i7 processor core that fits most data science needs. For example, an i7 core or even an i9 core, especially with multi-threaded performance, is ideal for simulations, learning simulations, compilation, dataset analysis and other such tasks.
Graphics card
In addition to the processor, good graphics performance is just as important for data-intensive tasks such as modeling, simulation learning and deep learning. It is therefore ideal to invest in a dedicated graphics card with at least 2 GB of memory.
Read this book, Introduction to GPUs for Data Analysis, to better understand how graphics performance can help you with data analysis.
RAM
If you’re working on data science tasks, you’ll likely multitask with multiple tools, have many tabs open for web searching, and work with multiple programming languages, in which case you’ll need a large amount of RAM bandwidth of at least 16 GB.
Storage
Many large data sets require a lot of storage space, especially if you want to store these files on a laptop. This means investing in a large storage capacity of at least 512 semiconductor hard disk drives (SSDs). Although you can also opt for a 1 terabyte hard drive to store all your important programs, tools, documents and files in a consolidated manner.
Communication
Finally, you need to think about connectivity, because with a good combination of connections, you can configure your installation exactly the way you want it. Make sure your laptop has at least one HDMI port, one USB A port, one Thunderbolt 3 port, and one mini display port.
Our Favorite Data Science Notebooks
Since data science is a resource-intensive field, it is natural to choose the best laptops for data science, whether you are a beginner or an experienced professional. But not all laptops on the market meet the requirements and it can be a challenge to find the right one.
To help you make a decision, here’s an overview of the best options we found for 2021:
ASUS F512JA-AS34 VivoBook
Our first choice is the VivoBook ASUS F512JA-AS34, a lightweight and stylish notebook with many impressive features that make it ideal for high-end tasks. This laptop offers perfection without fail, and the icing on the cake is the sleek, modern design. The best? Everything is customizable and updated, including the screen.
This notebook is equipped with a powerful Intel Core i7-7700HQ (7th generation), a quad-core processor that runs at 2.8 GHz and gives you impressive loading speeds. It also has a rigid keyboard for a great typing experience. In addition, there is a bright backlight with three brightness switches that allow you to adjust the brightness to your needs.
We especially like the high PPI value of the screen, which is excellent for playing and working. The brightness is commendable and does not cause any reflection problems, whether you use the laptop indoors or outdoors. Not to mention that heating is not a problem thanks to the quiet thermal design with two fans.
- 15 Full HD Display
- Intel i3-1005G1 processor
- Intel UHD Graphics GPU
- DRUM 8GB, 128GB SSD
- Windows 10 Home OS
New Apple MacBook Pro
If you like Apple products, the best data analysis laptop we can recommend is the latest Apple MacBook Pro. In fact, it’s one of the best laptops for entertainment, as it offers serious top-level performance for data science tasks with enhanced processing power and graphics performance.
This makes it ideal for effectively using different types of software and distributions from Panda’s, Anaconda and Python. Think about it: MacBooks are generally known for their seamless multitasking, which means you can use multiple tools with intensive applications, libraries and distribution software.
We find the 9th generation Intel Core i9 processor particularly commendable. Generation. It has an eight-core architecture that can efficiently manage various versatile data science tasks such as training, simulation, compilation, etc.
- 16 3072 x 1920 Retina display
- Intel Core i7 CPU of the 9. Generation with 2.6GHz
- 4 GB AMD Radeon Pro 5300M GPU
- 16 G IEM. DRUM, 512 G SSD
Also check : Apple MacBook Air or an older version of Apple MacBook Pro.
Aker Predator Helios 300
Another amazing option when it comes to choosing the best computer for data collection and analysis is the Acer Predator Helios 300, a powerful gaming laptop that is ideal for data collection and analysis. It meets all performance requirements set by the student or data processor.
Even better: All power settings are easily adjustable, making this laptop not only suitable for work, but also for gaming and entertainment. It is also equipped with a 7th generation Intel Core i7 processor. The CPU uses a 2nd generation quad-core processor with a base processing power of 2.8 GHz, which can be overclocked to 3.8 GHz using TurboBoost technology.
When it comes to graphics processing, the NVIDIA GeForce GTX 1060 graphics card features six gigabytes of dedicated DDR5 memory so you can easily and conveniently train, simulate and modify machine learning algorithms. In other words, it is not only ideal for data science, but also as a laptop for computing and other related fields.
- 15 Full HD IPS display
- Intel Core i7 CPU
- GeForce GTX 1060-6GB GPU
- 16GB DDR4 RAM, 256GB SSD
- Windows 10 64-bit operating system
Acer Aspire R 15 2-in-1
We also love the Acer Aspire R 15 2-in-1 laptop, which has a 360-degree hinge that gives you the luxury of working the way you want. It offers great performance in an affordable package, so you can enjoy a fast and easy-to-use device at a great price.
Like all Acer laptops, it is really robust and stable, which is a great achievement considering that most 360-degree laptops suffer from screen vibrations. Moreover, this laptop is lightweight and has a fast, smooth and amazing trackpad.
It also has an IPS display with LED backlight and a multi-touch feature, making it very easy to interact with the data. Even the sound quality and keyboard are excellent on this laptop. And a dedicated graphics card with 2GB of vRAM is a real treat for data analysts and gamers alike.
- 15.6 Full HD touchscreen
- Intel Core i7 processor from 7. Revival
- GeForce 940MX GPU
- 12GB DDR4 FRAME, 256GB SSD
- Operating system Windows 10
Also check : Acer Laptop Aspire 5.
Dell XPS 15
For decades, Dell has successfully and flawlessly combined state-of-the-art hardware configurations with taste and aesthetics in its XPS models, anticipating the next generation of computing power. The Dell XPS 15, for example, has well-calibrated performance making it an excellent solution for data collection and processing.
That’s because it runs on a 9th generation Intel i7 processor. It works with a six-core, 12-wire architecture combined with an overclock speed of 4.5 GHz. This means that you can do your job easily, conveniently and without any hassle.
Additionally, this processor is paired with an NVIDIA GeForce GTX 1060 graphics card with 4GB of dedicated DDR5 vRAM. This ensures that the notebook’s overall processing power matches the graphics-intensive requirements.
- 15.6 4K Ultra HD display
- 9th generation Intel Core i7-9750H processors. Revival
- NVIDIA GeForce GTX 1650 Graphics Processor
- 4GB GDDR5 RAM, 1TB SSD
- Operating system Windows 10
Also check : Dell XPS 15 7590.
Gigabyte AERO 15
The epitome of uncompromising reliability and high performance, the Gigabyte Aero 15 is also worth a look. It comes from a trusted brand that regularly produces high-end notebooks like the Aero 15, which has an ultra-modern look.
The beauty of this notebook is that in addition to its attractive design, it has many useful features, including a robust profile that totally enhances the user experience, a wide range of connectivity options, and a full-size backlit keyboard.
You’ll also find the 9th generation Intel Core i7 processor. We like the 4th generation with 4 cores, a working speed of 2.6 GHz and an overclocking speed of 4.5 GHz. Not to mention the NVIDIA GeForce GTX 1660Ti graphics card with 6GB of dedicated DDR6 memory that can easily handle graphics-intensive tasks.
- 15.6 Ultra HD display with thin frame
- Intel Core i7-9750H CPU
- NVIDIA GeForce GTX1660Ti Graphics Processor
- 16 GB RAM, M.2 PCIe 512 GB SSD
- Operating system Windows 10
Rice knife 15
Last but not least is the Razer Blade 15, an advanced gaming notebook that comes in a lightweight, slim and compact case with a hardware configuration that is unmatched in its class. In fact, it offers desktop-level performance that you simply can’t find in today’s powerful laptops.
Of course, being a gaming laptop, it’s ideal for other hardware-intensive tasks like machine learning, data analysis, and statistical analysis. This may be due to the 10th generation Intel Core i7 processor. Generation, which is calibrated for the highest performance. With Max Turbo, it can even overclock to 5.1 GHz.
Graphics performance is also great with the NVIDIA GeForce RTX 2070 Super Max Q with 8GB of DDR6 vRAM. With 16 gigabytes of DDR4 RAM and 512 gigabytes of solid-state storage, you have a superior, fast laptop for uploading, downloading files and multitasking.
- 17.3 Full HD display
- 2.6 GHz Intel Core i7-9750H CPU
- 6GB NVIDIA RTX 2060 GPU
- 16 G IEM. DRUM, 512 G SSD
Also check : Lenovo Yoga 2021
How to find the best laptop for data science
To be fair, you can take any modern laptop and use it for analysis. It works as long as you do all your calculations in the cloud. Otherwise, if you want to work on a laptop, you need serious specs and equipment.
Again, this largely depends on the type of data analysis and software you use, and the needs of your laptop. For example, if you’re just starting to learn about data analysis tools or how to use Lynda.com, you’ll need something very different from a professional who works with more complex data sets, for example.
Data analysis
One of the first things you need to understand is how and where you want to analyze the data.
Use of the cloud
When you use the cloud, it basically means renting IT services to Amazon and other big companies – in other words, you use their huge clusters of computing equipment to do all your processing and computing. Examples include Amazon Elastic Compute Cloud, an online service for scalable computing power, and CUDA, an alternative computing platform.
If you opt for this solution, all you need is 4 to 8 GB of RAM, a 1mpbs internet connection, a long-lasting battery, a multi-core processor for smooth multitasking, and maybe a backlit keyboard for working at night.
This allows you to use any terminal on which you can write data and do machine learning, while reserving simpler tasks like Hadoop clusters and testing sample code for your own hardware.
Construction of a domestic electrical installation
You can also choose to create your own Big Data analysis system at home, but this will be quite complex as you will need multiple machines that are not laptops. Basic requirements for such an installation are multi-core processors such as cheap 8-core AMDs, disks in RAID configuration and at least 16 gigabytes of RAM per machine.
However, if you are looking for used servers listed on various e-commerce sites like eBay and Amazon, you can build a cluster on a limited budget. You can also get information, post on social media or visit warehouses – whatever you need for the least amount of money.
Here are many guidelines to follow when creating your setup so that you can optimize your processor’s development: AMD Core Developer
Software
There are many different types of data science. Thus, even in statistical analysis, which is already a specialized field, there are still many different programs in use, all with different requirements and specifications. Below is a list of the most common data analysis programs and the hardware you need to run them.
For students
If you’re a student, you won’t be doing any real precision work with Big Data – at least not on a laptop. If something happens, it’s on your department’s servers. Therefore, the most common software and languages you will encounter in the various courses are R, Python, SAS, SPSS, Stata, Tableau, RStudio, Rapid Miner and MatLab.
For all these things, any laptop equipped with a decent workstation, i.e. a screen and keyboard, will work perfectly, as most modern laptops have enough RAM and a processor to handle all these programs and languages. Basically, you can do all of these things with no problem with 2 cores, 8GB of RAM and +2.5 GHz.
Installation of extensions and modules
Installing dozens of Python and R modules is a daunting task, but you need to complete the ecosystem and make this software work on your laptop. Even after a successful implementation of these information modules, it is a challenge to make them all work together.
Of course, you can find many tutorials, but it always depends on how you install each module and the operating system you use. In general, Mac and Linux systems are easier to use, especially compared to OSX and Windows systems. In fact, you don’t even need a new Apple MacBook Pro – you can buy an old one because the software is constantly being updated.
For professionals
As a data specialist, you’ll use most of the same software as an undergraduate, but with a special emphasis on Hadoop, Spotfire, Rapid Miner, and RStudio. Hadoop, for example, means you’re going to be working with gigabytes of data, so you need a facility that can handle that.
Right now, data specialists are struggling with one of three problems: Variety, speed and volume. Scientists studying velocity and volume need a facility that can be easily connected to the cloud, but those studying diversity need a more productive (i.e., more expensive) facility.
Machine learning
Those who work with machine learning algorithms must constantly work with large amounts of data to achieve better results. Therefore, you need a laptop with a pronounced focus on memory and processing power. In addition, if you are using the RevoScaleR software package, you will also need a multi-core GPU in this case.
Click here to view the Machine Learning Forum for beginners. Or, if you’re a seasoned professional who wants to learn more about accelerated data processing, check out NVIDIA’s Deep Learning AI program.
R
A multi-core configuration is also advantageous when working with large data sets via R, but only up to a certain point, as R usually only uses one core at a time internally. In addition, bottlenecks in data analysis problems have to do with the speed of RAM and disk I/O, making it difficult to use 4 or 8 cores on standard devices.
Here’s an article on Microsoft’s Big Data Computing tips for R.
Hadoop
A common approach to data analysis is to use a small sample from a large dataset to input it into the instrument’s memory. The main reason for this is that in the past it was expensive or difficult to obtain/store documents.
But with Hadoop, you can now perform a variety of exploratory data analysis tasks without sampling. This means you can work directly with complete data sets, taking advantage of the processing power and linearly expandable memory to store all RAW data and use complete data sets to create better and more accurate models.
Just write a mapping job, a HIVE or PIG script, run it directly on the Hadoop dataset and get the results on your laptop. This is a great advantage for machine learning, especially for techniques such as product recommendation, outlier detection and grouping.
Python / pandas
One of the best approaches to data analysis is to use Pandas to read Excel and CSV files and clean up, partition, filter, aggregate and finally summarize the data and create simple charts. You can also use PIG and HIVE to create feature vectors for any data object, combining billions of rows with large tables.
Most machine learning algorithms are also memory and computation intensive. Therefore, you should look for better transformers, such as. B. a 4-core Intel i7 processor, which is ideal for large data sets and computational and data processing tasks.
Beginner’s computer services
If you need to work with large data sets that require significant computer resources that your laptop or desktop computer cannot provide, you will need to turn to a server. It is a more powerful computer designed for specific tasks such as analyzing data or running web applications, databases and file systems.
For example, if you are working with a 100 GB dataset, you should remove your laptop and load the data into a database with analysis. The best and fastest way to do this is to use a server with enough RAM.
It works very well, so you don’t need to buy better, more expensive laptops to keep up with Digital Ocean, Microsoft, AWS and Linode all offer cheap computing power – much less than you’d have to spend on a laptop.
AWS (Amazon Web Services)
For example, if you use an AWS subscription, even a free one like AWS Free Tier, you can get free or hourly access to EMR multi-machine on-demand clusters and get hands-on experience with the services, products, and platform itself. Not to mention that you also have access to other data repositories like Redshift and ElasticSearch.
Indeed, AWS is currently the largest leader in cloud computing services. This, and others, allows you to work with cloud systems, which is a core competency in data science, but also a simple necessity due to the increasing amount of real-world data.
Think about it: Even the most powerful desktop graphics cards are not powerful enough to handle data-intensive tasks, such as supporting large data sets with vector machines and neural networks. For more information, see Data Lakes and Analytics on AWS. This is the fastest way to get answers from your data for your users.
Frequently Asked Questions
Are laptops or desktops better for data science?
The answer actually depends on your personal preferences. Laptops and desktops are fine, each with their own advantages and disadvantages. On the one hand, desktops are more powerful but cheaper than laptops, but their design is administrative, while laptops are more portable but also more expensive.
What is the ideal processor for data processing?
If you want to get the most out of data science tasks, such as B. running or compiling code, working on machine learning and deep learning algorithms, or linking datasets, you’ll benefit greatly from the i7 processor core. The multi-threaded architecture on the CPU is also ideal for modeling algorithms.
Why do I need a multi-core processor for data processing?
R, Python, and most other data processing tools will use all available kernels in your configuration to process the data as best they can, even if they are single-wire. Moreover, data arrays are usually multi-threaded and therefore require powerful processors to function perfectly.
How much RAM do I need for my laptop?
The truth is that when it comes to data science, you need as much RAM as possible. 16 gigabytes of DDR4 RAM should be the minimum. However, choose 32GB of RAM if you need to work on data-intensive projects and meet multi-tasking requirements, such as running multiple tools simultaneously, using multiple distributions, or accessing multiple databases.
What is the purpose of GPUs in data science?
Scientists working with deep learning, neural networks, and GPU modeling need high-performance graphics cards. Otherwise you can’t do the job. But if that’s not your concern, you probably don’t need a dedicated GPU.
Do you need a good graphics card for data science?
Again, you need to choose your hardware configuration based on your specific needs and requirements. If the nature of your tasks and your specialization can be handled by integrated graphics, you can save money by forgoing more expensive specialized graphics processors.
Can I use a gaming laptop for data collection?
You will often see gaming laptops in different tests of high-performance laptops in different fields, and the same goes for data science. However, gaming laptops are expensive and only worth it if your work requires the hardware that comes with them. In many cases, creating simple data sets using statistical libraries requires only a reasonable amount of RAM.
Are MacBooks suitable for data science?
MacBooks have become popular with scientists and engineers in recent years because MacOS and OSX are inherently built on the UNIX platform, making them better suited for working with advanced applications and software packages. In other words: MacBooks are ideal for advanced data visualization, testing, and prototyping tools.
What to look for in a data science laptop?
If you are a professional computer specialist, you need more RAM because the data sets are always directly in the memory of your system and once these data sets exceed the size of your RAM, the processing of these data sets will slow down. In addition, a lot of SSD storage is needed to reduce access time. The speed of the processor (including base and turbo clock) must also be taken into account.
Overview
After all, it’s not that hard to find the best data science laptop if you know what to look for and what to prioritize. You can find a decent laptop within your budget, as long as you tailor your search to your specific needs – for example, do you want constant access to your analytics or do you want to experiment at home?
Make sure your laptop has a decent GPU, which is needed for data processing. Gaming laptops are almost always the best option because they have the best specs and hardware. In addition, there are many cloud services like Microsoft Azure and Alibaba Cloud that provide you with a virtual machine to perform your computing tasks.
If you want to work fully in the cloud, all you need is a modern laptop with good Wi-Fi access to use Google Colab and all the free resources that come with it. It’s an incredible tool for working on a small budget and building from scratch. In most cases, gaming laptops are more powerful and robust, but it is much more affordable to work with smaller laptops that use the cloud.
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