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As a data scientist you will need to collect them, analyze them, and interpret them and then make a conclusion that will benefit a business the most.
Therefore, when it comes to handling such a large number of data, it is evident that you will need a laptop for it.
However, it is vital to make sure that the laptop is and works as efficiently as you. This will make the work smoother, faster, and better for you.
The laptop needs to be powerful because statistical analysis needs plenty of time as well as computation power and therefore will consume a lot of battery life.
Therefore, it is mandatory that you choose a laptop with high-end specs.
It should also be reliable in performance and durable so that you can concentrate on your work more and are not caught up in fixing issues in the device most of the time.
It will hamper your productivity and accuracy in your job as well.
The laptop also needs high-end specs because as a data scientist you will need to use a lot of heavy and complex software for data analysis such as the Statistix and the IBM SPSS Statistics Software.
The specs should support running these software programs easily with no lags or glitches, which, once again will hamper your productivity.
For example, if you choose to use a laptop with 4 GB of RAM, it may be good and operate smoothly while dealing with a moderate amount of data, but the greater the size of the data you need to handle, the higher the specs should be.
It is only a good laptop with proper, high-end, and quality hardware components that will help you to deal with larger amounts of data, without hanging.
However, in order to choose such a laptop, you will need to spend a lot of time researching them on the internet.
You must also know the things that you need to look for while you shop for such a laptop to use for data science and data analysis.
If you want an easy way out, this article will surely be of great help. Here is a list of five most sophisticated laptops that come with high configurations that you can refer to make your best pick.
The buying guide that follows the list will come to your rescue while you shop for one. Read on to be better informed.
- Laptops for Data Science and Data Analysis – Price
- What is Data Science and Its Essential Analytical Skills?
- Minimum & Recommended System Requirements for Data Science and Data Analysis
- 5 Best Laptops for Data Science and Data Analysis:
- How to Choose Laptop for Data Science and Data Analysis?
- Is it feasible to consider buying a MacBook for data science and data analysis?
- Is it true that a multi-core processor is mandatory in a laptop for data science and data analysis?
- Are gaming laptops sufficient for data science and data analysis?
- What is the role of the GPU in a laptop for data science and data analysis?
Laptops for Data Science and Data Analysis – Price
What is Data Science and Its Essential Analytical Skills?
Data science is the study of a large number of data of a specific kind to analyze and interpret them.
This study helps in arriving at a specific decision based on the analytical report that may involve forecasting a particular trend or an event.
Data science and analysis helps businesses to plan their future moves as well as other professionals and even government institutions and organizations in doing their job properly, being well informed beforehand. This is a very important field and is in high demand.
If you want to pursue your career in data science, there is no doubt that your previous experience or skills will matter significantly, but that is not all.
You will need to develop a specific set of skills in addition to that and also follow a specific path in data science.
Next, you need to develop your skill set to deal with other higher data analysis programs and software such as Python, R, and more, to handle your data projects.
Finally, to become an experienced data analyst or programmer, you need to develop your technical skills.
Here are the 8 data science and analytical skills that you should possess to excel as a data scientist.
First, you will need to have a fair amount of programming skills along with the knowledge of using the different tools of the trade.
This includes statistical programming languages such as Python and database query languages such as SQL.
Next, you should have some statistical skills which are vital for a data scientist. You must be knowledgeable about statistical tests, maximum probability estimators, distribution, and more.
This will also help you in machine learning to determine the different techniques for a valid approach.
Machine Learning skills are also required by a data scientist as that will help you to deal effectively with large amounts of data and data-driven matters.
These methods include K Nearest Neighbors, ensemble methods, random decision forests, and more.
You will also need to understand linear Algebra and multivariable Calculus as this forms the basis of most of the techniques.
Such knowledge will help you to make small improvements in predictive performance as well as in algorithm optimization.
Data wrangling is a skill required while analyzing a set of difficult and messy data.
This will help you to know the ways in which you can deal with the imperfections in data which may include and not limited to inconsistent string formats, missing values, and more.
Data visualization and communication skills are also essential for a data scientist as that will help in making data-driven decisions by describing your findings.
Both these skills will help in communicating with other people both technical and non-technical.
Data visualization and dashboarding tools like matplotlib, d3.js, ggplot, and tableau will help in encoding data and to communicate information.
A software engineering skill set is also very useful to a data scientist. It will help you to handle a lot of data logging and developing a number of data-driven products.
Finally, your data intuition will help you in problem solving, which is a very common need while dealing with data-driven products. it will help you to differentiate between the things that are important and those that are not, know the methods to use, how to interact with the product managers and engineers, and know when to make approximations.
Minimum & Recommended System Requirements for Data Science and Data Analysis
The minimum system requirement for the basics of data science and data analysis is a modern Intel Core i5 processor or an AMD equivalent.
In addition to that, your laptop should also have at least 8 GB of RAM along with 256 GB of SSD storage.
However, if you want to put your system under real pressure and analyze more complex sets of data in quick time, you will not be able to achieve that with a set of minimum specs in your laptop.
The best and ideal way is to go with a device that comes with the recommended specs mentioned below.
Minimum system requirements: 7th generation Intel Quad Core i5 or dual core AMD Ryzen 3 processor| 8 GB of RAM | 256 GB SSD or 1 TB HDD storage | Integrated Intel HD Graphics 620 or AMD Radeon Vega 3 graphics | 13 inches screen or bigger | 1280 x 720 pixels display resolution
Recommended system requirements: 8th generation Intel Quad Core i5 or higher i7 processor| 16 GB of RAM or higher | 512 GB SSD or larger | Nvidia GeForce MX 150 or higher GTX graphics | 15.6 inches screen or larger | 1920 x 1080 pixels or higher screen resolution
5 Best Laptops for Data Science and Data Analysis:
1. HP Envy 17T – Best Overall
Bottom Line: The 8th generation Intel Core i7 processor working in unison with the dedicated Nvidia graphics card in this will be able to handle all complex needs for data science and data analysis most efficiently. All other specs in it, including the larger screen and storage will offer the best overall performance.
- Screen: 17.3 inches
- Storage: 1 TB HDD
- RAM: 16 GB DDR4
- Processor: 8th generation Intel Core i7 8550U
- OS: Windows 10
- GPU: Nvidia GeForce MX150
- The system comes with a DVD writer.
- The screen is good and delivers crisp and clear images that are easy to view.
- The full-size keyboard of the laptop is backlit and has a separate numeric keypad.
- The storage is HDD and not an SSD.
- The laptop is very heavy, weighing more than 6 pounds.
- The trackpad is not at the center but shifted to the left side which may cause difficulty while typing.
You can use this laptop for data science and data analysis jobs because it will be able to support all your needs with its components and features.
The large 14.3-inch screen delivers clearer images with its WLED-backlit and IPS technology in full HD resolution.
There is an HP Wide Vision HD infrared webcam with integrated digital mics that will help in video conferences and taking pictures.
The keyboard of the system is full-size and unique due to its island-style design. The back lights allow easy and accurate typing in the dark. The best part is that it comes with a separate numeric keypad.
The 2-DIMM DDR4 RAM, the Windows 10 operating system, the SATA hard drive that operates at 7200 rpm, the DVD writer that also writes to CD, and the Bluetooth and wireless AC WLAN connectivity, all play a significant role in the performance of the machine.
2. Lenovo ThinkPad X1 Carbon – Runner Up
Bottom Line: The integrated GPU, which is not a mean performer, makes this system the runner up in this list for doing your data science and data analysis jobs. Otherwise, the touchscreen, processor, RAM, storage and operating system are all good enough with the fingerprint reader adding to its security.
- Screen: 14 inches
- Storage: 512 GB PCIe NVMe M.2 Solid State Drive
- RAM: 16 GB DDR3 SDRAM
- Processor: 8th generation Intel Quad Core i7 8565U
- OS: Windows 10
- GPU: Integrated Intel HD Graphics 620
- The laptop comes with a touchscreen.
- The backlit keyboard allows easy typing in the dark.
- The fingerprint reader of the system ensures a safer login.
- The graphics card is integrated and not dedicated.
- The trackpad is not very sensitive.
- The battery life could have been more.
The system is pretty fast in processing data using the Intel Core i7 processor that can operate at a base clock speed of 1.8 GHz and up to 4.6 GHz Turbo speed making the best use of the four cores in it.
The 14-inch touchscreen comes with anti-glare properties to ensure that the images are softer to your eyes.
The LED backlit screen saves energy and the IPS technology of this full HD display makes it more attractive.
The DDR3 SDRAM comes with 2133 MHz bandwidth and the nature of the PCIe NVMe M.2 SSD storage enhances the speed and performance of the system.
The backlit keyboard ensures comfortable and accurate typing even in lowly lit settings. The fingerprint reader of the system allows faster and safer login as well.
The Windows 10 operating system provides all its usual benefits and the HDMI port and 802.11 wireless AC connectivity makes this system more reliable for data science and data analysis. Check out Lenovo ThinkPad X1 Carbon Review.
3. Acer Aspire 5 – Value for Money
Bottom Line: You will get the best value for money in return if you buy this laptop while pursuing your data science and data analysis jobs. All its specs along with the Windows 10 Pro operating system will provide you with all the assistance that you need for data analysis irrespective of the complexities in it.
- Screen: 15.6 inches
- Storage: 256 GB PCIe NVMe SSD
- RAM: 8 GB DDR4 SDRAM
- Processor: AMD Ryzen 3 3200U Dual Core
- OS: Windows 10 Pro
- GPU: AMD Radeon Vega 3 Mobile Graphics
- The keyboard of the laptop is backlit allowing easy typing in the dark.
- The system comes with extensive connectivity options.
- The system design allows upgrading both RAM and storage.
- The touchpad is shifted to the left which may cause inconvenience while typing.
- There is no optical drive in the system.
- The battery life could have been more.
This is one of the best and cheap laptops for data analysis that comes with an AMD Ryzen dual core processor that performs at a high speed and the 8 GB DDR4 SDRAM and the PCIe NVMe SSD are both upgradable to enhance the speed and performance of the laptop on the whole.
The large enough screen is full HD and it comes with widescreen and IPS display technology.
The LED backlit feature saves the energy and the AMD Radeon Vega 3 mobile graphics produces images without any lags or screen tears.
The backlit keyboard allows comfortable typing in low-lit settings. The Windows 10 Pro operating system will provide you with all the assistance that you need for data analysis. Check out Acer Aspire 5 Laptop Review.
4. Dell Inspiron i5577-5335BLK-PUS – Better Visuals and Audio Output
Bottom Line: This stylish laptop is good for data science and data analysis jobs and entertainment for its better visuals and audio output. The dedicated Nvidia GTX graphics card is superior to the others in the list to offer a captivating view and the Waves MaxxAudio technology ensures superior sound quality.
- Screen: 15.6 inches
- Storage: 256 GB SSD
- RAM: 8 GB DDR4 SDRAM
- Processor: 7th generation Intel Quad Core i5 7300HQ
- OS: Windows 10
- GPU: Nvidia GeForce GTX 1050
- The SSD of the system is fast and boots up in seconds.
- The battery life of the laptop is good enough for a day’s work.
- The device comes with a separate numeric keypad.
- The bezel is pretty thick all around the screen.
- The trackpad is shifted to the left side which may cause problems while typing.
- The laptop does not come with any optical drive.
The features and functionality of the system will allow you to be productive all day with your data science and data analysis job with its 7-hour long battery life and the 7th generation Intel quad core processor that comes with 6 MB cache and can operate at a high speed of 3.5 GHz.
This thin laptop is easy to use and the stylish, brushed hairline palm rest adds to its beauty and appeal. The keyboard is quite large and the key travel is quite good to add comfort while typing.
5. Huawei MateBook X Pro – Better Features and Functionality
Bottom Line: This laptop is a good one for data science and data analysis for its better features and functionality. It has a 3K touchscreen with 91% body to screen ratio, Dolby Atmos for better sound, one touch power button with Windows Hello, Thunderbolt, long battery life, and pop-up webcam for privacy.
- Screen: 13.9 inches
- Storage: 256 GB M.2 NVMe SSD
- RAM: 8 GB LPDDR3 SDRAM
- Processor: 8th generation Intel Quad Core i5 8250U
- OS: Windows 10 Signature Edition
- GPU: Integrated Intel UHD Graphics 620
- The battery life of this system is excellent and lasts up to 12 hours.
- There is a privacy pop-up camera in the system design.
- The ultra-thin bezel provides a high body to screen ratio and viewing angle.
- There is no numeric keypad in the keyboard design.
- The system comes with an integrated graphics card.
- The number of ports and its selection are limited.
This laptop comes with a full view display. The 3K touchscreen has an ultra-thin bezel of 4.4 mm that provides a 91% screen to body ratio and a 178-degree viewing angle.
This thin and light laptop weighs only 2.93 pounds making it ultra-portable for your on-the-go computing needs for data science and data analysis.
The Windows 10 Home Signature Edition is preinstalled in the system and it comes with virtually no bloatware that will slow you and the system down.
The performance of the system is consistently high with the 8th generation Intel Core i5 processor.
The 3000 x 2000 resolution of the screen, its ability to deliver images at 1500:1 contrast ratio and 260 PPI, and the 2nd generation Dolby Atmos audio system ensures that you have an immersive viewing and audio experience.
The one touch power button is very useful as well because it not only enables you to switch on the device but also complete the identity authentication using the Windows Hello feature in less than 8 seconds. Check out Huawei MateBook X Pro Review.
How to Choose Laptop for Data Science and Data Analysis?
Before you start looking for a laptop that is best for data science and data analysis, it is important to know that there is nothing like that labeled in a device.
Therefore, you will need to be very careful about your selection.
For that matter you will need to know the different factors to consider and the specs to look for.
It will all depend on the kind of software you will need to use as well as the type of data analysis.
Requirements based on software
Before you step into the store, you must know the software programs that are commonly used in data science and data analysis.
If you are a student, you will need to use a combination of software that will include R, Python, SAS, SPSS, Stata, Tableau, RStudio, Rapid Miner, and MatLab.
For this you will simply need a decent laptop with a good keyboard and display. All modern laptops have enough CPU and RAM to run these languages and software.
You may choose any laptop with a 2.5 GHz processor with 2 cores and 8 GB of RAM to run these in a breeze. A Linux system or a MacBook will be a better choice.
If you are a professional, the software will be pretty much the same with the inclusion of Spotfire, and, most importantly, Hadoop. You will need more GB and GHz in your laptop for that matter.
The choice of your laptop will then depend on the type of data scientist you are.
If you are a velocity or volume type of data scientist, the best laptop for you will be the one that will allow cloud computing by connecting you easily with the different cloud environments.
If you are a data scientist dealing with a variety of problems you will be better off if you invest in a comparatively more expensive laptop that comes with latest i7 or i9 processors belonging to the 7th generation and above, 1 TB storage, and higher RAM of 32 GB or more.
If you have to deal with machine learning algorithms a lot then you will be dealing with more and more data. These algorithms that are both CPU and memory hungry, will consume a lot of power.
In that case you should plan to buy a laptop with a powerful CPU, more RAM, and, most importantly, a long battery life, preferably in excess of 8 hours.
If you use R too much, you will need a laptop that comes with more cores and threads. In addition to that, you will need a powerful GPU as well.
However, do not go overboard about the number of cores in the processor as it will only help you up to a certain point.
This is because there may be several data analysis problems, bottlenecks in the disk I/O, and the speed of the RAM when you run 6 or 8 cores on commodity hardware.
Now, follow the hardware requirements very carefully that you should have in your laptop to use it for data science and data analysis.
As for the CPU, you should preferably go for an i5 or i7 chip. If it is of the 8th generation, even an i3 chip will do your work.
You can choose a low voltage dual core processor or a quad core CPU for better performance.
If you deal with large datasets mostly, you should focus on quad core processors only. Well, affordability will always matter.
Core i5 of 8th generation onwards will have 4 cores and can easily operate beyond 3 GHz, and Core i7 processor from the 7th or 8th generation onwards will also have 4 and 6 cores respectively, and will operate at a base clock speed as high as 4 GHz.
The most important component of your laptop after the CPU is the RAM but it is difficult to say how much RAM you will actually need in your system because it depends on the size of the data science and data analysis set.
If you use the cloud mostly, you should get at least 8 GB of RAM. This will allow smoother multitasking as well as in creating and loading a reasonable amount of data to test and then use the cloud.
If you use the laptop only, a 4 GB of RAM is good for dealing with a small data set of 100,000 to 200,000 records.
8 GB of RAM is the minimum requirement for working with large datasets which is thirty times the above number of records.
The specific reason behind this is that most of the data analysis software like R will typically load every data into the memory.
In order to make your laptop future proof and work with any sort of complex datasets as and when required, you should ideally go for a laptop with 16 GB of RAM.
Of course, you can always reduce the RAM memory if you do not need that much for data analysis, scripting or programming.
This will allow you to leverage more cores and threads for programming and processing.
All data is always stored on the internal storage drive and is then relocated to the RAM for computing.
This transfer rate can be significantly high with the Solid-State Drives in comparison to the traditional Hard Disk Drives.
If you use the cloud for storage, you do not need to worry about the drive type or speed.
Simply get the largest capacity of storage you can afford in your laptop. Most of the laptops today come with 1 TB of storage, which will be more than enough in this case.
On the other hand, if you use the internal storage of your laptop and if you need more space to store your data, you should get an SSD storage of as high capacity as possible. It will help you even if your data analysis is I/O bound.
However, in the scenario wherein your data fits well within the memory, you do not need to worry much about storage because then you will be able to access most of your data in a sequential manner.
In short, a 1 TB storage space will be enough for any average or professional data science and data analysis.
However, if you go for a combo storage of a faster SSD and a slower HDD, it should be at least 64 GB SSD and 1TB HDD.
The SSD will allow storing the software and operating system and the HDD can be used to store all your data. Just make sure that the partition is encrypted for security reasons.
It is all about size and resolution when it comes to choosing a laptop for data science and analysis.
There is no doubt that you will need a large display because it is not easy to stare at large data sets on a laptop with a smaller screen.
In addition to that, you will also need to use graphics for visualization and for that using a screen that is less than 14 inches in size is asking for trouble and discomfort.
As for the resolution, ideally, you will need a screen with full HD and above instead of an HD or HD+ resolution.
This will play a huge role in enabling you to see more data at once.
Most of the data analytical software and apps need a powerful GPU for parallel processing during data science and data analysis.
Moreover, if you work on neural networks, you will be benefited if you use a d-GPU most of the time.
You will also need a dedicated GPU, whether it is AMD or Nvidia. It is even better if it comes with the CUDA Core technology.
However, the Intel HD chips will not do because these do not have the desired power in the parallel processing.
Finally, the operating system required for data science and data analysis.
However, you should not look down upon the Windows operating system.
It is all about personal preference and your comfort in using it. Therefore, it will be better if you know a few facts before choosing an operating system.
Apart from being able to use Python, the most commonly used language for data science, using Linux or Mac operating systems will also allow easy access to the latest libraries.
When you use Windows operating system, you will often have to wait till the time when these libraries are compiled as binaries.
A Windows laptop will also need far more tweaks than the Apple machines to run the codes successfully.
However, there are a few specific advantages of going for Windows operating systems and laptops. It will cost you less and you will get a wider option, features, and formats to choose from.
There will also be no issues with repairs and upgradeability. The most significant advantage of Windows is that you can use Excel, which is the most effective tool for data analysis.
It works best on Windows and you can choose any platform to work on your databases using Windows operating system.
Is it feasible to consider buying a MacBook for data science and data analysis?
Yes, it is quite feasible because macOS or OS X is characteristically built on the UNIX platform. This means that it will be more capable of handling most of the demanding apps and software sets. It will allow you to use more complicated tools for data testing, prototyping, and visualization work. The MacBook will enable you to handle large volumes of data and allow dataset sharing.
Is it true that a multi-core processor is mandatory in a laptop for data science and data analysis?
Yes, a multi-core processor is necessary for better data processing and handling. Most of the data processing tools such as Python and R work best when there are multiple cores available for processing the data in the best way possible, even if it is single-threaded. Also, there are also a lot of multi-threaded elements in data science that need high-end and multi-core processors to work in the way desired.
Are gaming laptops sufficient for data science and data analysis?
Yes, without any doubt. This is because these machines are very powerful and come with hardware of high quality. However, it is not mandatory to buy such expensive models. It all depends on the type of work you want to indulge in. If you use simple data models such as Keras and TensorFlow, a decent amount of RAM and a good enough processor is all that you will need.
What is the role of the GPU in a laptop for data science and data analysis?
A powerful GPU is only necessary when you intend to work with deep learning tools. Apart from that, you will also need a powerful GPU in your laptop if you work with neural networking and high-end GPU Modeling. For this, you will need an Nvidia graphics card to handle all of those complex algorithms with ease, comfort, and in quick time.