which processor is best for data science

Laptops

6 Best Laptops For Data Science and Data Analysis in 2021

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Without knowing exactly the type and mount of data and more importantly the type of data analysis you’re doing, it is very difficult to just go ahead and tell you this is the “best laptop for data analysis”.

For example…

If you are using R, having more memory is always a good thing but for something distributed like map-reduce, even a simple set of commodity machines would suffice.

Having more RAM memory will also help when setting up a pseudo-distributed cluster on a single machine to some extent, but it will still be very limited.

Lastly if you are into GPU computing for parallel programming (e.g. using NVIDIA CUDA architecture for the optimizations of machine learning algorithms) having a better graphics card (more specifically shaders or cores) will be very helpful.

If you did not understand anything I just said…

Don’t worry that’s fine.

Like every other post on this website, it’s meant to be read by everyone.

So this is what we’re doing with this post:

  1. Go briefly over what Data Analysis means for computer hardware.
  2. Explain why some hardware requirements are important for certain types of data analysis
  3. List the current best laptops for the most common types of data analysis being done today
  4. List laptops for those who are just trying to get started in the field.

There’s one problem though, going over data analysis and explaining how each hardware component helps making data analysis faster can take a few pages of discussion.

For now I’ll quickly summarize the specs you need for data analysis and leave all the details to the last section (you should bookmark this post and give it a read later if you are serious about data analysis).

Recommended Specs for Data Analysis

The general motto for data analysis is:

“With greater data sets comes greater insights”.

Unfortunately, that also translates to a bigger demand for hardware resources.

So what’s a good configuration to get started for someone who wants to be a data scientist?

A KDnuggets poll indicates a 3-4 cores w/ 5-16GB Windows systems.
A StackExchange thread recommends a 16GB RAM, 1TB SSD Linux OS with a GPU.
A Quora thread nudges converges around 16GB RAM….

….

Enough jokes…

RAM
My experience is that RAM is the most important thing for data science because it is the biggest bottleneck with large datasets. Things speed up an order of magnitude when all your processing is in-memory or in-RAM. A 16GB RAM is ideal but this isn’t always available on laptops ~600 bucks but you can always upgrade a cheap 350$ to 16GB though. 

Do not go below 8GB! I warn you.

SSD
Second is storage. An SSD is going to make an enormous difference, a budget SSD is going to be 2-3 times faster than a regular a hard-drive. A good SSD is going to be 4-5 times faster, an NVMe SSD found in a Macbook Pros and the newest laptops can make reading data x17 faster compared to regular old fashioned hard drives (HDDs).

CPU
Processing power is always good but you’ll more than likely be bottlenecked by RAM and then by your storage’s reading/writing speeds.

No point in having a CPU that can  do a million calculations per second if your storage drive can only serve up 1000 pieces of data per second.

After you max out on RAM size & Storage speed, you can then invest the rest of your budget on a “modern” CPU, not necessarily a fast “CPU”, because they’re all fast today. But if you can afford the fastest ones (it’s more important to get the ones with more cores though), go for them.

Note that unlike RAM and storage, these are not upgradeable so try to get the fastest you can afford.

GPU
If you work with deep neural network or just NN (parallell computing), get a graphics card with as many CUDA cores/Shaders you can afford.  NVIDIA or AMD, no Intel HD cards.

Keyboard
Getting a superb keyboard with all the computer goodness mentioned isn’t always possible. So if you’re going to be doing a lot of typing  get an external keyboard and mouse/trackball.

My recommendation is to make sure they’re ergonomic:
RSI and tendonitis is nasty.

Display
Minimum 15 inches. You will probably end up ssh’ing into more powerful machines at some point so the interface/real state screen becomes super important .

Ports
Another plus it’s to make sure your laptop has a thunderbolt port (USB Type C) so you can transfer data to/from external drives at lightning fast speeds. Most laptops today have that so as long as you avoid old laptops, you should get one automatically.

OSX
Mac vs. Windows vs. Linux – it depends on industry\company you work for or your personal preference. But I’d still recommend going for a laptop that can support a Linux Flavored OS seemlessly like a Lenovo ThinkPad/MacBook/Dell XPS.

A linux flavoured OS may at some point become your default OS.  (windows does not connect well and requires a lot of extras to fit in with a typical workflow that will end up on the cloud) 

Summary:

The best rule of thumb when choosing a laptop is to get as many cores from a processor as possible and make sure your laptop can support up to 16GB of RAM and an SSD storage.

So if these come too weak in terms of Storage or RAM you can always do the upgrade. 

*Graphics Cards are not upgradeable so if you think you’re going to need one get the one with as many CUDA Cores as you can afford too.  More details on the last section.


Contents [hide]

Top 6 Best Laptops for Data Analysis

In this list I’ve included laptops for beginners, students and every type of Data Scientist (those into parallel programming, machine learning, deep learning and those Using AWS/Cloud Services,etc.)

Just keep scrolling down and read the descriptions carefully and you’ll be sure to find your pick.


1. Acer Nitro 5

Best Budget Laptop For Interior Design

 Intel Core i5 9300H

 8GB RAM

  GeForce GTX 1650 

 256 PCIe SSD

 15” FHD 1080p IPS

 5.07lb

 5 hours

This is the most basic laptop for pretty much any type of data analysis and it’s the ideal for those getting starting with Data Analysis this includes students doing research or taking classes w/ Data Analysis. 

RAM siting at 8GB is enough simple statistical and ML/DL models of small data sets.  Although the GPU is definitely too much for anything too simple.

Obviously, you’ll also be able to run any Data Analysis package/software (R/MatLab/SAS,etc).

The question then becomes what can be considered a small data set?

I consider small anything around 300k rows with 4 variables each or anything that takes 300MB of space. Since windows itself eats 2GB,  background programs 1GB , your still have 4.7G of RAM left.

The biggest data set this laptop can handle should be about*

7000MB/300MB~20*300k rows=6000k rows with 4 variables. 

The likelyhood a student or someone starting with Data Analysis encounters a data set of this size is very slim

And if you ever do you can always learn how to use the Cloud for faster processing. The truth of the matter is that TODAY it is far easier now to start doing data analysis and data science with a really basic laptop because as long as you can pay for a cloud-based service to host your files you can do computation remotely on a cluster.

However,  you can still run MUCH bigger data sets on a laptop like this because you also have the option to upgrade it to 16GB. All it takes to do the upgrade is removing one screw.

What about much much bigger data sets?

You can punch in bigger data sets but it’ll be processed much slower because your laptop will resort to use your disk storage “as RAM memory”.

In this scenario(~12000k rows w/ 4 variables) however,  models like this one will not be THAT slow because it has a Solid State Drive and when your computer starts using it, it’s going to be about 5 times faster than it would’ve been with a traditional laptop with an HDD. 

Lastly, before resorting to cheaper or similar models from other brands with the same CPU+RAM+SSD, please be aware that this laptop also comes with a mid- range late generation GPU (GTX 1650,) w/ a lot more CUDA cores than the 1050Ti/MX150/MX250 laptops which will cost you, weirdly enough,  about the same).

So if you want to start tinkle around with parallel processing tasks (which you should start doing as this is an essential tool in the industry  growing and expanding across many libraries) a model with a dGPU like the 1650GTX becomes even more important. Chances are this model won’t always be on stock or its price may go up for some reason (it’s usually 650-700$), check out these mother models which have the exact same hardware

Amazon LinkCPUGPUDisplayPrice
Acer Aspire 5i5 1053G1MX35060Hz649$
ASUS ZenBookR5 4500UMX35060Hz699$
ASUS VivoBookR5 3500U1050GTX60Hz650$
HP PavilionR5 3550H1050GTX60Hz665$
HP Pavilion i5 9300H1650GTX60Hz686$
HP Pavilion Gamingi5 9300H1050 60Hz 
Acer Nitroi5 9300H1650GTX60Hz700$
HP PavilionR5 3550H1650GTX60Hz700$
HP Pavilioni5 9300H1650GTX60Hz700$
ASUS TUFR5 3500U1650GTX120Hz737$
ASUS TUFR5 3550H1650GTX60Hz750$
Lenovo 3i5 10300H1650GTX120Hz750$
Dell G3i5 10300H1650GTX60Hz750$
Lenovo L340i5 9300H1650GTX60Hz628$
HP PavilionR5 4600H1650GTX60Hz659$
MSI GF63 Thin 9SCXi5 9300H1650GTX60Hz699$

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2. Lenovo IdeaPad 3

Best Cheap Laptop For Data Analysis

Best Laptop For Minecraft - Lenovo Ideapad 3

 AMD Ryzen 3 3200U

 4GB RAM

  Intel UHD

 256GB SSD NVMe PCIe

 15” HD 

 4.07 lbs

 8 hours

This is one is for students.

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. What will be required is portability and battery life. Lecture halls… coffee bars…. libraries will all have their sockets taken.

Although at first glance, the price may suggest that it’s a weak laptop (and the fact that most people have no clue about Ryzen CPUs), this laptop has plenty of power to use Data Analysis Software/learn libraries, etc (not for medium to large data sets though).

This is portable, portable is expensive but it’s got battery life and it’s pretty amazing too: ~10 hours.

The downside is that it lacks a FHD resolution which helps a lot if you want to have way more space to work with(this will help you having two windows open side by side: one for documentation, another for your IDE/Visualize Data). 

If you want a model with an FHD you probably need to invest +100$ dollars with one of the options below.

But if you are looking for a laptop to lug everywhere and still feel like you are just carrying a book, you need to get one weighing 3lbs and you don’t mind the lack of resolution, this is the cheapest AND fastest laptop with windows 10 Home in it you will find.

Note: this laptop doesn’t have a dedicated GPU for which any parallel processing will be restricted to using the number of threads given by the CPU which is 4. This table has cheaper variants than this Acer Aspire Slim laptop, if you have a copy of Windows 10 you can save yourself 100$ bucks

LinkCPURAMOSPrice
ASUS ImagineBookCore M3-8100Y4GBWindows 10S349$
Acer Aspire 5Ryzen 3 3200U4GBWindows 10S349$
Acer Aspire 5Core i3 1005G14GBWindows 10S399$
ASUS VivoBook 15 Ryzen 3 3200U8GBWindows 10 HOME450$
HP 15.6″Core i3 1005G14GBWindows 10 HOME424$
Lenovo IdeaPad 3 Ryzen 5 3500U 8GBWindows 10 HOME449$

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3. MacBook Air

Best Mac Laptop For Data Analysis

 Intel Core i5 2.9GHz

 8GB RAM LPDDR3

  Intel HD

 256GB SSD

 13” 1440×900 TN

 ~3lb

 +13 hours

The release of the 2020-2021 MacBooks with the M1 chips have paved the way for the MacBooks to be considered one of the most intelligent choices for anyone dealing with Data Science.

There are several reasons for this:

  • The UNIX-like environment. As we will detail in our guide below, is the most  work-efficient OS for data science. It’s not just about how python just works best on a MAC but the all the software packages and languages readily available.
  • The portability and battery life: If you are always shh’ing your way into a sever, this is super handy because you can move all over the place and still run huge chunks of data anywhere.

And lastly the M1 CHIP:

  • Completely blows any “mobile” CPU from Intel/AMD out of the water, performing faster than the latest Core i7 10th gen processor.
  • The fact that it has EIGHT cores and it’s multicore performance is far better than Intel/AMD
  • The code behind the architecture is said to be optimized for “machine learning”.

There’s one big catch though:

  • 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.

So if you really need CUDA core or Open CL technology, this is not going to be for you.

Remember that you will be stuck with whatever configuration you choose at the time of purchase. Right now the Air is still limited to 16GB RAM.

Luckily this may be sufficient for 85% of data scientists out who plan on processing data on it.

MacBook Pro:

The remaining 15% of data scientists who still want all the perks of the Air and the UNIX-like environment should consider the 13” MacBook Pro which has the option to up the RAM to 32GB!

If you want to take advantage of parallel processing, then get 16” MacBook Pro. Although it doesn’t have a NVIDIA GPU on it, AMD GPUs use Open CL for parallel processing and work nearly as good.

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4. Dell XPS 13 9360

Best Windows UltraBook For Data Science

 Core i5-10210U

 8GB RAM DDR3

  Intel UHD

 256GB SSD M.2

 13” full HD 1080p IPS

 2.7lb

 10 hours

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.

However, I’d recommend you carefully reconsider buying a premium laptop like a Dell XPS 13 for several reasons:

  • Dell XPS series (along with the Lenovo ThinkPads) give you one of best compatibility out of the Box with all Linux flavors (especially Ubuntu)
  • You will get almost the same design of the Air (thinness weight battery life).

Dell XPS 15:

If you have to use CUDA for parallel computing at some point, you can buy the Dell XPS 15, which has the same power as the weaker version of the 16” MacBook Pro but again it does have a NVIDIA GPU instead of a AMD GPU on it.

Why you keep pushing for a Unix-Like Environment? Why should I bother with it? What’s wrong with Windows?

I’m aware that most of the statistics platforms like R, scikit-learn, or the many many others, are relatively independent of the big OSs.

But from my experience the most important thing for me was to have a unix terminal readily available and this is only available on Linux and MAC systems.

I’ve found this super useful ever since I was a grad student and postdoc and had to use a Dell (not this one) with Ubuntu on it to be able to connect to a computing infrastructure.

Having an Unix-Like environment just made it so much easier (AND natural) to work with larga data sets.

MacBooks , which I was always provided with for these tasks, will accomplish the same thing (though in a little bit fancier way) but I know there’s still a big stigma against Apple and of course they’re expensive too.

This is why I’m listing some Windows options that can can accomplish the same tasks as long as you get Linux on it.

Obviously, you can’t just download all the data from big infrastructures and use the Dell XPS or MacBooks to process it.

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. Check videos or courses on it if you don’t know this You will have to learn it at some point.

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5. 2021 Lenovo ThinkPad T490 

Best Windows Laptop for Data Analysis

 Core i5-8265U Up to 3.9 GHz

 8-40GB DDR4 RAM 

  Intel HD 620

 256GB-2TB SSD

 14” TN FHD

 3.41lb

 10 hours

Another laptop capable of holding Unix-Like environment seamlessly with all the powerful specs needed to run large data sets are the Lenovo ThinkPads.

In fact, they are the de-facto choice for anyone who wants to install Linux Distros on a Windows laptop.

You can configure the ThinkPads to whatever RAM size you think you are going to need (8-40GB) and the processor too.

Note that the thinkpads do not have a dedicated GPU so you won’t be able to take of advantage of parallel computing for Deep Learning/Machine Learning/Neural networks.

Just like the MacBook Pro and the Dell XPS 15, you can use the ThinkPads to test your code with as much data you can fit into its hardware resources and even play with variations of that same data before shh’ing into computer farms.

Why am I still mentioning Cloud Computing?

Well to be honest (and I discuss this in the last section tool) , real modern data (although they may fit into laptops and desktop’s RAM) will be done several times (I mean hours as opposed to days) using AWS or any other computer farm service.

And if you decide to go into industry, that will help you stand out as a candidate (not just the experience, but rather the self drive and the initiative to do everything).

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) I’m aware that most people are not really ready to switch from a laptop  to a cozy local environment to a remote AWS machine for analytics and will rather test as much data as possible on their little workhorses.

The Lenovo Yogas:

These are basic machines that have the sleek design and portability of the MacBook Air and Dell XPS 13 . These ones will serve you just as well if you plan to do most of your work on the Cloud rather than your laptop.

If you are not ready for that yet, then the ThinkPads like this one are your best cheapest choice if you plan to run heavy calculations on a Windows Machine. Provided there is no parallel processing that requires dGPUs, if you require that, then the Dell XPS 15 is a better “portable choice”.

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6. MSI GS66 Stealth 10SGS-036

Best Laptop Large Data Analysis

 Intel Core i7-10750H

 32GB DDR4

  NVIDIA GeForce RTX 2080 Super

 512GB PCIe NVMe SSD

 15” full HD 300Hz IPS

 Very Heavy

 1 hour

Lastly, If you  want to get as much computing goodness as you can to  process all of your data on it(no matter how big the data set is), your best bet is to go for high end gaming laptops. These are the ones that have all their specs nearly maxed out: CPU, GPU, RAM and Storage. Workstation laptops are not really that much more useful and powerful unless their GPUs have way more CUDA cores but they can get really really expensive.

I personally like the MSI brand due to specs/money ratio they offer and the cooling systems that keeps their laptops up and running for several years without having you to downclock CPU/GPU.

The GPU here is the most powerful (after its desktop version and a workstation GPU that sells for like 5000$) so you can speed up any GPU process like Image Analysis or parallel computing (Deep Learning, Machine Learning,etc).

Unfortunately, the battery life and weight are pretty bad and you also end up with a few specs that are just useless for a data scientist but useful for gaming : G-sync, 240Hz refresh rates but there’s not much you can do about you won’t find a laptop without those. Try to find cheaper laptops by looking for low refresh rates and no G-sync though.

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How To Buy the Best Laptops For Data Analysis

Like I mentioned before there is no best laptop for Data Analysis out there.

In fact, any laptop would be good for analysis purposes if you do all the computing in the cloud.

So this section will be mainly focused for those trying to do as much computing as possible on their new rigs and this in turn depends on the kind of software they use and the type of data analysis as well.

I’m going to start with the basics for those who are just getting started into the field, perhaps using Lynda.com, or teaching themselves the tools along the way. If you are not a beginner and plan to do all your data analysis back at home just skip over to the hardware section.

Doing Data Analysis

There are two ways to do Data Analysis: using the cloud or with your own rig.

A) The Cloud – Recommended for Learning Data Analysis

Using the Cloud means renting computing services from big companies like Amazon. You are basically leaving all the computing/processing to their huge clusters of computers. 

If you opt for a good cloud environment with an AWS subscription, you’ll get access to on-demand EMR multi-machine clusters at hourly rates. You’ll also get access to their other data stores like ElasticSearch and Redshift and so on.

All you need at home is a basic laptop or desktop with 4-8GB RAM and just a decent internet connection (1mbps). Not only will this save you a ton of money but time as well.

Another specs to consider when going this route is a long battery life (so you can do this away from home too), multiple core CPU (so you can smoothly multitask) and perhaps a backlit keyboard to work at night.

As for me during my past role as a data scientist, I worked a lot with hadoop clusters but the real machine learning and data munging was done on computer farms where I just needed a terminal to ssh into.

On my personal laptop I might download a small sample to test my code before going to the big machines and this is what I suggest you do.

B) Building a Rig at Home

Building a rig back home for “big data analysis” is quite challeging. Laptops are out of the question. You’ll need multiple machines with:

  • Multi-core processors (8-core AMDs are cheaper)
  • Minimum of 16GB of RAM per machine.
  • Storage Drives in RAID configurations

On the other hand if you are on a shoestring budget and still would like to build a cluster back at home, you can always go for an used server set up:

  • Go through listings on Amazon, Ebay or any other e-commerce site.
  • Make a post on social media and ask if anyone’s selling their old server

When I got started  I set up a 32-core and 64 GIGs Linux  server  for about 400 bucks. 

Software & Specs

Just saying statistical analysis doesn’t really tell you what exactly you are going to need in a laptop. 

So in this section I’m going to go briefly go over the most used software in Data Analysis and talk about the specs you should be focusing on.

These will be highlighted as well.

Students

If you are a student you’ll probably end up using a combination of the following software/languages:

  • R
  • Python
  • SAS
  • SPSS
  • Stata
  • Tableu
  • RStudio
  • Rapid Miner
  • MatLab

For that you’ll just need a laptop with a decent workspace (keyboard + display) since modern laptops today have enough CPU & RAM for all these silly languages and software. Any Laptop with +2.5GHz and 2 cores + 8GB RAM should make working with all of that a breeze.

Besides, What won’t be required is any big-data crunching. Universities have loads of servers and things for that stuff.

Installing modules/extensions

What’s going to be a real pain is to get the ecosystem fully installed and working in your machine. Both R and Python have dozens of modules you can install for Data Science, none of them are easy to install. The first time I had to install these I spend a whole week trying all of them to work with each other.

There are guides everywhere but it’s also a matter of luck sometimes it may be easy dependong your OS and depending how exactly you install each of these.

I found them much much easier to install on Linux Systems than MacBooks, although I did manage to install it on a OSX.

If you can’t stand a Linux system I would recommend a MacBook any would do fine even the old models since they still have their software updated.

Professionals

The software is pretty much the same, perhaps with the adittion of RStudio, Rapid Miner,Spotfire and most importantly Hadoop.

The latter implies of course using data sets in the range of GB.

I’d say there are three types of data scientists depending on the problem they wish to solve: volume, velocity or variety.

If you are a volume or velocity type of Data Scientist, the best laptop rig you should get is a laptop that allows you to easily connect to the cloud environments described before. 

If you’re frequently working on the third V, variety problems. You will benefit much more from an  expensive laptop (relatively speaking).

Machine Learning

And If you deal with machine-learning algorithms then as you probably know you’ll have better results with more and more data, this translates to algorithms that are both CPU & memory hungry. If you plan to do your data analysis on your laptop, then focus CPU and Memory. 

If you use R and especially the RevoScaleR package, you might go as far as need more cores even from your GPU. So pay close attention to the CPU/Memory/GPU sections.

R

Dealing with larga data sets  with R is also easier with more cores.

Getting more cores can also help, but only up to a point.

R itself can generally only use one core at a time internally.

In addition, for many data analysis problems the bottlenecks are disk I/O and the speed of RAM, so efficiently using more than 4 or 8 cores on commodity hardware can be difficult.

data set sizes will range into the GB. Maybe a few others, but my lecturers haven’t returned my emails yet.

Hadoop

A common approach is to use a sample of the large dataset, a large a sample as can fit in memory. With Hadoop, you can now run many exploratory data analysis tasks on full datasets, without sampling.

Just write a map-reduce job, PIG or HIVE script, launch it directly on Hadoop over the full dataset, and get the results right back to your laptop.

In many cases, machine-learning algorithms achieve better results when they have more data to learn from, particularly for techniques such as clustering, outlier detection and product recommenders.

Historically, large datasets were not available or too expensive to acquire and store, and so machine-learning practitioners had to find innovative ways to improve models with rather limited datasets.

With Hadoop as a platform that provides linearly scalable storage and processing power, you can now store ALL of the data in RAW format, and use the full dataset to build better, more accurate models.

Python/Pandas

Data analysis: using pandas to read CSV and Excel files, to clean, filter, partition, aggregate and summarise data, and to 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 might be out of reach for any laptop, as doing a way to big repeated measurements analysis (the variance/covariance matrix explodes exponentially)

All the answers are great.

Pay close attention to those sections.

Most of the Machine Learning algorithms are CPU Intense and Memory hungry. Look out for Intel Core i7 processor which is currently best processor and 4 core is ideal when you have to take advantage of threading for big data sets. Remember I am also talking about Data munging work along with computation.

Hardware Requirements

CPU

Quick Lesson

CPU generation is the first digit of the 4 digit model number of the CPU, e.g: i7- 8750H is a 8th gen whereas i7-6700HQ is a 6th gen CPU. Also there are two variants of i7 , i5 chips out there: the low voltage dual core CPUs (Model number ends with a U, mostly used for ultrabooks) and the performance oriented four to six core chips (Model number ends with either HQ or H, mostly used in high end gaming and performance oriented computers).

If crunching large datasets , your main focus is obviously to  get one with an H/HQ series CPU.

Using the Cloud

You’re only going to need a decent processor for multitasking. Anything from the 8th generation will work wonders even if it’s a Core i3. Ex: Core i3 – 8100U, Core i3-8145U Core i5-8250U, etc.

Even 7th generation CPUs which are far cheaper will  do for testing code, before uploading the work up in the cloud.

Just avoid Core i7 processors altogether , they’re expensive , you don’t need them and you’ll get less battery out of your laptop unless of course that particular model has perks/hardware that no other model offers.

Using your Laptop

You need to get the best fastest processor your budget can afford. You will be stuck with whatever processor you decide on because you can’t upgrade it. If it doesn’t have the power you need for your work, you’re out of luck!

If your laptop needs more memory or a faster storage, you can easily do the upgrade and it’s not too expensive.

Here’s a list of the specs of the most popular & fastest processors out theah:

CPUBaseTurboCores
i5 8250U1.6GHz3.4GHz4
i5 8265U1.6GHz3.9GHz4
i5 8300H2.3GHz4GHz4
i7 7700HQ2.8GHz3.8GHz4
i7 8565U1.8GHz4.6Ghz4
i7 8550U1.8GHz4.0GHz4
i7 8750H2.2GHz4.1GHz6
i7 9750H2.6GHz4.5GHz6
i9 8950HK2.9GHz4.8GHz6
i9 9980H2.3GHz4.8GHz8
i9 9900K3.6GHz5.0GHz8
i7 10210U1.8GHz4.8GHz4
i5 10201U1.6GHz4.2GHz4

Note that having a core i7 not only gives you an edge in computing power but also in upgradeability since most core i7 laptops are bulky enough to make insertion of a new RAM or SSD relatively simple. 

Opting for a late generation processor (6th 7th or 8th 9th 10th) will also you give the possibility of PCIe NVMe SSD support which is x17 faster than HDDs.

As a data scientist you also will run multiple applications in parallel and/or run data analysis apps which can support parallel processing.

So the number of cores is more essential than the clock speed.

Although two cores will run faster on day to day use, the four cores pays for itself when running algorithms.

Two cores will save you money, but four-six cores should be preferred.

BEWARE: You may have to dig into the technical specs on the processor to see how many cores it offers. it’s not an easy science for you to figure it out just from the label.

Machine Learning

Depending on your specialization you may need to work for real with Hadoop Stack, Sols or other tools that require a AWS subscription or other cloud SaSS providers.

For now you should get a reasonably fast processor (Core i5 or Core i7).

Core i5: only take from the 8th generation onwards. These have 4 cores and easily go past +3GHz.
Core i7: can be taken from the 7th or 8th generation onwards. All of them have +4 coures (the latter has 6) and go up as much as 4GHz.  Ex: Core i5-8250, 8300-H,Core i5 8265U   Core i7-8750H, Core i7-9750H Core i7-7700HQ, Core i7-8250U, Core i7 8565U,

If you use MapR SanBox, then a four core CPU is really a priority. Beware that most Core i3 and Core 5 processors even 7th generation Core i7 only give you 2 cores.

RAM

Probably the most important component luckily the easiest/cheapest to get on laptops. So this section is going to be quite lengthy to try and convince to get as much RAM goodness as you can.

You can say that the computer turns over the data set on it’s head when doing data analysis or simply that the data set is loaded from the storage device to the RAM when doing any kind of computation (though the storage device can also be used for this but several times slower).

It’s hard to say how much RAM you may need as this depends on the size of the data set.

A) Using the Cloud

Get a minimum 8GB of RAM (so you can load/create a reasonable amount of test data then use the Cloud) plus 8GB also allows for smooth multitasking.

B) Using your Laptop

We need to figure out how data sets relates to RAM.

What’s a small data set?

A data set of 100,000 to 200,000 records with about 200 variables each will be around -300 MB. Assuming you are not doing anything memory intensive(like visualizing this data) 4GB in this case may be enough(as you can see there’s plenty left).

When is 8GB a minimum?

If you are working with large datasets(x30 bigger than the above).  8GB might just barely make it.

Why? Most software like R will usually load everything in to memory.

In general if working on R or Python 8–16 GB should be enough.

Note that you can always reduce the need for so much RAM memory if your  data analysis/scripting/programming skills are so you can leverage more cores/threads in your programming. There are a few tutorials on how to do this, for example Microsoft has written one here. It’s good practice to learn these tricks anyways.

16GB RAM – The best 

However there’s a general good rule of thumb.

A data scientist can do amazing things with about twice the RAM as their largest chunk of data.

Not the whole data set, just some complete chunk.

My experience leads me to believe that 75% would be happy at 8GB, and 85% at 16GB and 95% at 32GB.

Getting more memory will not only let you finish your data analysis but speed things up several times fold.

As an example, Algorithms on large data sets that can take 4 hours with 8GB to run can take 20 minutes with 16GB. Talk about savings!

16GB RAM for small-medium large data sets?

Even if you will use your laptop for small data analysis (e.g. less than a GB), but the more RAM you have the less you have to think about using a new local variable to store some permutation of your data.

Another reason is that having memory to have multiple versions of the same thing around during experimentation is really useful.

How to find out how much exactly will I need

If you’ve landed on this post, my guess is that most of you don’t know what a large data set looks like and if the memory you currently have may be enough for that.

Press CTRL+ALT+DEL and .. look at the “memory” and “virtual memory” columns to get a sense of the memory footprint your computer uses when you open up large datasets. This will give you some idea.

You should write that number anbut you should aim for 2x the memory footprint plus OS overhead in case the application tries to copy the dataset.

Data Preparation vs In-Memory Analytics

Another aspect of the memory issue is the data preparation step. Today data scientists need  two set of skills — preparing “big data” (usually in-disk processing using Unix grep, awk, Python, Apache Spark in standalone mode etc..) and in-memory analytics (R, Python scipy).

However, if you have a large amount of memory you may not need need the first skill because you can prepare data in R or Python directly.

This is even more important for text analytics where the amount of input data is naturally huge.

So, data processing becomes simplified with the large amount of memory in your machine.

Why is my system running so slow under large data sets?

This is because your operating system starts to “thrash” when it gets low on memory, removing some things from memory to let others continue to run. This can slow your system to a crawl.

C) Machine Learning

Machine learning is the most dependent on RAM memory size – more memory is always better for machine learning. 

Note that real life ML models generally involve cluster compute time, with in-memory datasets, spread over tens or hundreds of machines, each with 32, 64 or 128 GB of RAM. 

For Machine learning in your own rig I suggest you get 16GB and possibly check if your laptop has another slot for a future 32GB upgrade.

But I’ve heard RAM isn’t that important, my laptop can still handle larger sizes than my what my RAM allows

That’s true.

Let’s you have a dataset that is about 6GB in size. (For the sake of convenience, I’ll assume that you only need mem allocation for the dataset to analyze)

You can still run it in a laptop with 4GB RAM, if you divide the dataset into reasonable sized partitions and process separately.

Then, you can combine the results later to gain the full view.

On the other hand, you can run it in a laptop with 8GB RAM, which will have enough RAM space to handle the data as a whole and process it all in one go. The latter will be faster, but the former is still doable but slow.

Storage

First of all, data is always stored on your storage drive and then transfered onto the RAM for computing. This process can be done is not necessarily faster with Solid State Drives.

A) Using the Cloud

If you are using the Cloud, you don’t need to learn about hard drive type/speed. Just get the largest capacity you can afford.

Most laptops today offer you with 1 TB of storage. This should be enough.

B) Using your Rig

On the other hand, if your data takes much more space than what your RAM can fit (even if you have 32GB), then your analysis will be I/O bound. In this particular scenario, you will benefit quite a lot from a Solid State Drive.

On the other hand, if your data fits in memory, then most data access is sequential and you don’t need to worry about it. Just make sure to have a 1TB for space, this is enough for the avg. data scientist.

Should I still get an SSD?

You should. You’d benefit from a Solid State Drive with decent space ~256GB just to launch your software, operating system and everything else run/open in a flash. Although not necessarily your analysis, your computer will fly with on-board.

As for “me laptop”: I used to have 2 disks, 1 SSD for the operative system and 1 old slow HDD for the info.

You want the first one at least 64 GB ad the second 1TB HDD.

The first one for OS/software and the second to carry data with you.

Keep an encrypted partition for security reasons.

Note that you can get two SSDs on a laptop. I have currently upgrade to this set up. You can see in this tutorial.

An SSD has no moving parts and is therefore much quieter. SSD’s are usually more expensive and often store less. 

Display

Size & Resolution

It is a no brainer that you need a large display. Staring at large data sets on a laptop is not easy. Besides you will need to:

  • SSH into more poweful machines
  • Use graphis, visuzalitions

So anything below 14 is really asking for trouble. Ideally you’d want 15 + full HD and above. Note that resolution also plays a huge role in being able to see more data at once. Do not settle for HD or HD+ resolutions, only look at full HD models.

External Monitor

All laptops have conectors for an external display. A second screen may be useful if you have to develop software (or scripts) as well as writing reports.

GPU

Today with parallel processing finding its way in nearly application, it is not uncommon to have data analytics apps make use of GPUs. For example neural networks, most of the time do benefit from dGPUs.

AMD vs Intel vs NVIDIA

By dedicated GPUs I specifically mean NVIDIAs line of GPUs that have “CUDA Core” technology.

Intel HD Chips aren’t used for parallel processing and although AMD makes great products they don’t have as much traction in the parallel processing world like NVIDIA.

Which Software/Apps/Type of Data Analysis make use of it?

It’s great and all that even AWS provides a graphics upgrade for parallel processing with CUDA capability but that doesn’t mean that non-parallel applications like ARC can use it. You need to make sure your applications is capable of GPU parallel processing.

You’d be suprised to find out how many think that plugging a GPU into their desktop/AWS package will somehow bring parallel computation capability into some old non parallel legacy software. It won’t.

Machine and Deep Learning

There are several books and articles written about it and you could also check the application’s website.  But most of the deep learning libraries and machine learning libraries (tensorflow, torch) are now using CUDA from NVIDIA processors. In fact, for the case of deep learning, most algorithms are optimized to run on GPU instead of the CPU. An algorithm that takes a week for CPU to run will take one day with a GPU.

It goes without saying learning deep learning with a CPU is going to be a strugle these days.

Image Analysis is also making use of CUDA cores.

Which GPU to use?

While some may say that GPUs from laptops are useless, whatever you read was probably written several years ago. Today’s laptop GPUs are nearly on par with the performance of their desktop counterparts. This is more true for the 10th generation (the latest and current) released by NVIDIA.

If your app does make use of it, you’d be surprised to know that even the low end graphics card found on laptops can easily give you 50-100 times speed improvement for data analysis.

My recommendations:

  • At least GTX1060. Not the 9 series, they’re fine but they’re deprecated for the best performance. Just take a look at this table:
 CUDA coresvRAM
MX1503842GB
940M3842-4GB
940MX3842GB
960M6402-4GB
10506402GB
1050Ti7684GB
980M15364GB
106012806GB
107019208GB
108025608GB
206019206GB
207023048GB
2080294411GB
  • 10th,20th generation GPUs have way more CUDA cores than their 9th generation counterparts.

 Try to choose algorithms/apps that make use of a GPU in order to get familiar/practice GPU implementation. It is a very useful skill for some aspects of the job

Cloud Services (For Newbies)

When a data scientist is working with a larger set of data which requires more computational resources than their desktop or laptop, we use a more powerful computer called a server.

A server is generally a very powerful computer which is dedicated to a specific task (for example running a file system, running a database, doing data analysis, running a web application or even all of the above!).

For example if you are dealing with a set of data which is 100 GB, one option for a computer with not enough RAM would be to load the data in a database and database analytics.

A faster (and arguably better) option would be use a server with enough ram (more than 100 Gb) and all of the analytics in RAM like is done with smaller sets of data.

The benefit always works out against buying a “better laptop” and that will hold true for a very long time with Linode, AWS, Microsoft, and Digital Ocean selling incredibly cheap compute power. As of today, I have a subscription on two of these : Digital Ocean and AWS and it’s allmost nothing to what I’ve saved up by not buying dekstops with 128GB RAM.

AWS (Amazon Web Services)

AWS is the biggest dog in the Cloud Service Market. Sooner or later you’re going to have end up using AWS or another cloud service. It’s not just that real data is too big to fit on a local machine now,  it’s also a crucial skill in the market place right now.

Moreover to get the real flavor of being a data scientist you actually have to work with cloud systems sooner or later.

If you planned on doing more intense stuff (Neural Networks, Support Vector Machines on alot of data) even the most powerful desktop GPU will not cut it and you will be better off renting out an AWS instance.

Note that AWS has a free tier for you to get stated with so you’ve got nothing to lose at this point.


Using a VNC (Virtual Network Computing)

Oh I tried it. I already had built my ideal (i.e. powerful) data analytics computer about a year prior, but it was a desktop.

I figured I could actually just buy a really cheap laptop, keep my desktop running all the time, and then use RDP*, Teamviewer*, or a VNC* programme to connect to it whenever I needed to do some data analysis.

So I bought a cheap 350$ windows laptop and started trying to set up a VNC.

I got it working. But not only did it mean that I had to always leave my desktop running: it was fairly laggy too.

Amazon AWS EC2

That’s how I fed up with it and discovered Amazon AWS EC2.

This service actually does something similar. It lets you create virtual computers with any operating system you want and customize how you access them.

I set up one of these (Linux), then taught myself how to use Linux.

The most useful thing about it is that I installed a web based IDE for R on it (Rstudio), which allowed me to go to a website hosted by my EC2 server and use R as if I was sitting at that computer.

Now, whenever I want to do some work, I can do it from any computer in the world with an internet connection, simply by visiting a website, and all the processing is done on the Amazon server.

Cost: You have to pay for the server, but they are inexpensive, and you pay different amounts based on the (virtual) processor, RAM, GPU etc of the server.

Also, there is a 1yr free trial which let’s you use the least powerful virtual server at no cost.

I understand that R may not be the only language you wish to use, but given that you can install anything you want on your server, it seems like a viable option for pretty much any data scientist.

Advantages

Can access server from any device with the internet
Files are always accessible. Don’t even need to download them (like you would with drop box), just view on the server
Costs much less than powerful laptop
Server can be programmatically designed to scale depending on analysis needs using an API

Disadvantages:

Laptop screen is quite small, but I now find I access the server mostly from other desktops or a 17” laptop.
Requires internet connection to use
Can take some time to learn how to use Elastic Computing Cloud (C2)

OS: Mac vs Windows vs Linux

Although it may look like Mac and Linux are the way to go. These days come down to preference at the end of the day. Most packages one will need for data exploration and analysis work on all platforms – Octave and R are great examples and are very widely used.

MAC

I have to admit that working with Python on Mac is much easier than Windows and even Linux due to better package management. Python being the most widely used language for Data Scientis then may imply that you have to go Mac.

In a way tat’s true, if you go Linux or Mac, it also means you’ll have access to the latest libraries, while using it on Windows means you’ll often have to wait for libraries to be compiled as binaries. 

Windows

A Windows machine will require far more tweaks to successfully run code than the Apple machines. Most of the (sporadic and poorly written) documentation available for cutting edge data science tools assumes you are working on an Apple machine.

Or at least Unix. 

However the advantage of going windows will never change: you can get the same hardware for half the price in the Windows world. And a choice of style, format and features.

There’s also the issue of upgradeability: If you need a bigger hard drive, you can fit one yourself. More memory – no problem.

You can repair PCs, while Macs are a sealed box – you are stuck with the hardware you thought you needed (or could afford) at the time you bought it.

There’s also the issue of NVIDIA GPUs for parallel processing, these are still only available in Macs.

Lastly, Excel, which still in my mind one of the most effective tools for data analysis works best on Windows, and works in most cases okayish OSX. If you’re going deep into pivot tables and more complex models that involve macros, use a PC or run a virtualized copy of Windows on your Mac; Office Mac can still be somewhat frustrating.

Either is fine when…

Using MatLab, S-Plus and SPSS.

In terms of the databases, you’re also free to choose any platform. PostgreSQL and MySQL will work on any platform. If you’re dealing with a Hadoop cluster, you’ll be connecting remotely, so any client operating system will work.

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