Laptop for data Science and machine learning

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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 theres not much you can do about you wont 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.

Im 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, youll get access to on-demand EMR multi-machine clusters at hourly rates. Youll 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. Youll 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 anyones 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 doesnt really tell you what exactly you are going to need in a laptop.

So in this section Im 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 youll probably end up using a combination of the following software/languages:

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

For that youll 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 wont be required is any big-data crunching. Universities have loads of servers and things for that stuff.

Installing modules/extensions

Whats 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 its 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 cant 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.

Id 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 youre 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 youll 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 havent 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 fortechniques 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

Youre only going to need a decent processor for multitasking. Anything from the 8th generation will work wonders even if its 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 , theyre expensive , you dont need them and youll 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 cant upgrade it. If it doesnt have the power you need for your work, youre out of luck!

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

Heres a list of the specs of the most popular & fastest processors out theah:

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. its 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.

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 its 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].

Its 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.

Whats 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 theres 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 816 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. Its good practice to learn these tricks anyways.

16GB RAM The best

However theres 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 youve landed on this post, my guess is that most of you dont 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.

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 Ive heard RAM isnt that important, my laptop can still handle larger sizes than my what my RAM allows

Thats true.

Lets you have a dataset that is about 6GB in size. [For the sake of convenience, Ill 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 dont 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 dont 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. Youd 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. SSDs 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 youd 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 arent used for parallel processing and although AMD makes great products they dont have as much traction in the parallel processing world like NVIDIA.

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

Its great and all that even AWS provides a graphics upgrade for parallel processing with CUDA capability but that doesnt mean that non-parallel applications like ARC can use it. You need to make sure your applications is capable of GPU parallel processing.

Youd 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 wont.

Machine and Deep Learning

There are several books and articles written about it and you could also check the applications 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. Todays 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, youd 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, theyre fine but theyre deprecated for the best performance. Just take a look at this table:

• 10th,20th generation GPUs have way more CUDA cores than their 9th generation counterparts.

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 its allmost nothing to what Ive saved up by not buying dekstops with 128GB RAM.

AWS [Amazon Web Services]

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

Theres 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 youre 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, youre also free to choose any platform. PostgreSQL and MySQL will work on any platform. If youre dealing with a Hadoop cluster, youll be connecting remotely, so any client operating system will work.