9+ Best Laptop For Data Science 2021 | Detailed Guide & Products Overview

Are you a Data Scientist and need a good laptop for your work? Let’s see the best laptops for Data Science, for professionals or students of Information Sciences, which will make work much more comfortable and productive.

Best Laptop For Data Science

As a data scientist, you must handle a large amount of data, collect it, analyze it, and interpret it in the way that is most beneficial to a business. When handling such large quantities, you also need an efficiently running laptop to make work easier. Statistical analysis requires a lot of computing power, be it a laptop or a PC.

Data Science and Machine Learning

Data Science and Machine Learning are growing at an astronomical rate and companies are now looking for professionals who can examine that gold mine found in data and help them make quick business decisions efficiently. The number of jobs for all US data professionals has increased by an impressive number.

What is Data Science?

People have been trying to define data science for over a decade, Hugh Conway in 2010 offered an answer with this Venn diagram consisting of three circles: mathematics and statistics, subject matter expertise (domain knowledge to abstract and calculate), and hacking skills. Basically, if you can do all three, you already have a great deal of knowledge in the field of data science:

Data science is a concept used to address big data and includes data cleansing, preparation, and analysis. A data scientist collects data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from collected data sets. They understand data from a business point of view and can provide accurate insights and predictions that can be used to drive critical business decisions.

Big data and data science, data analysis needs computing power

As a data scientist, you must deal with a large amount of data, collect it, analyze it and interpret it, in the way that is most beneficial for a company. When handling such large quantities, you also need a laptop that works efficiently, making work easier. Statistical analysis needs a lot of computing power, be it a laptop or a PC.

The software data analysis, such as statistical software IBM SPSS or Statistics, requires a minimum of RAM for smooth operation in a moderate amount of data. The larger the size of the data you handle, the better hardware you will need. A good laptop with the right high-end hardware will help you deal with more data, without hanging up or giving you waiting times.

Best Laptop For Data Science |Comparison Table 2021

Here is a list of highly sophisticated laptops with excellent configurations that you can refer to for a better choice:

Prices and images pulled from the Amazon Product Advertising API on:

Best Laptop For Data Science | 2021 Products Overview

Specifications that a laptop for Data Science should have

The general motto for data analysis is:

“With larger data sets, you get more insights.”

Unfortunately, that also translates to a higher demand for hardware resources. So what is a good setup to start with for someone who is working with data science?

RAM memory required

RAM is the most important thing for data science because it is the main bottleneck with large data sets. Things speed up by an order of magnitude when all your processing is in memory or RAM. A 16GB RAM is ideal, but this is not always available on laptops under $ 600.

One piece of advice: Don’t go below 8GB!

Disk or SSD drives

The second factor is the hard drive. An SSD will make a huge difference, a cheap SSD will be 2-3 times faster than a normal hard drive. A good SSD will be 4-5 times faster.

CPU

Processing power is always good, but storage speed or RAM will most likely bog you down.

There is no point in being able to do a million calculations per second if your hard drive can only provide up to 1000 data per second.

After maximizing these, spend the rest of your budget on a “modern” CPU, not necessarily a fast “CPU”, because they are all fast today. Note that, unlike RAM and storage, these cannot be upgraded, so try to get the fastest you can afford.

Graphics card (GPU)

If you work with a deep neural network or just NN (parallel computing), get the graphics card with as many CUDA Cores / Shaders as you can. NVIDIA or AMD, not Intel HD cards.

Keyboard

It is not always possible to get an excellent keyboard with all the mentioned computing advantages. So if you’re going to type a lot, get an external keyboard and mouse/trackball.

My recommendation is to make sure they are ergonomic – RSI and tendonitis are unpleasant.

Display

Minimum 14-15 inches. You’ll probably end up getting into more powerful machines at some point, so the actual status screen/interface becomes very important as well.

Ports

Another advantage is making sure your laptop has a Thunderbolt (USB Type-C) port so you can transfer data to/from external drives at blazing-fast speeds. Most of today’s laptops have one.

OSX

Mac vs. Windows vs. Linux: Depends on the industry/company you work for or your personal preferences. But I would recommend opting for a laptop that can support a Linux-flavored operating system ostensibly like a Lenovo / MacBook.

A Linux-flavored operating system (Windows doesn’t connect well and requires a lot of extras to accommodate a typical workflow that ends up in the cloud) may at some point become your default operating system.

There is no best laptop for data analysis as such. In fact, any laptop would be good for analytics purposes if you do all your computing in the cloud.

Therefore, this section will mainly focus on those who are trying to do as much computing as possible on their new equipment and this, in turn, depends on the type of software they use and also the type of data analysis.

I’ll start with the basics for those just starting out in the field, perhaps using Lynda.com or learning the tools themselves. If you are not a beginner and plan to do all your data analysis at home, just skip to the hardware section.

Doing data analysis
There are two ways to perform data analysis: using the cloud or with your own platform.

A) The cloud: recommended for learning data analysis

Using the cloud means renting IT services from big companies like Amazon. Basically, you are leaving all computing/processing to your huge groups of computers.

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

All you need at home is a basic laptop or desktop PC with 4-8GB of RAM and a decent internet connection (1Mbps). This will not only save you a lot of money but also time.

Other specs to consider when going this route are a long battery life (so you can do this on the go as well), a multi-core CPU (so you can multitask smoothly), and maybe a backlit keyboard for work at night.

B) Building a platform at home

Building an in-house platform for “big data analytics” is quite challenging. Laptops are out of the question. You will need several machines with:

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

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

• Check the listings on Amazon, Ebay or any other e-commerce site
• Post on social media and ask if anyone sells their old server

Software and Specifications

Just saying statistical analysis doesn’t tell you exactly what you’re going to need in a laptop. So in this section, I’m going to briefly go over the most widely used software in Data Analytics and talk about the specs that you should focus on.

Students

If you are a student, you will probably end up using a combination of the following programs/languages:

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

For that, you will only need a laptop with a decent workspace (keyboard + screen), as today’s modern laptops have enough CPU and RAM for all these basic languages ​​and software. Any laptop with + 2.5 GHz and 2 cores + 8GB RAM should make working with all of that a breeze.

Also, what will not be necessary is big data processing. Universities have many servers and things for that.

Installing modules / extensions

What will be a real hassle is having the ecosystem fully installed and running on your machine. Both R and Python have dozens of modules that you can install for Data Science, none of which are easy to install.

There are guides everywhere, but it is also a matter of luck, sometimes it can be easy depending on your operating system and how exactly you install each one.

If you can’t support a Linux system, I would recommend a MacBook, any would be fine, even older models as they still have their software up to date.

Professionals

The software is pretty much the same, perhaps with the addition of RStudio, Rapid Miner, Spotfire, and most importantly, Hadoop. The latter implies, of course, the use of data sets in the GB range.

I would say that there are three types of data scientists depending on the problem they want to solve: volume, speed, or variety.

If you are a data scientist of the volume or speed type, the best laptop platform to go for is a laptop that allows you to easily connect to the cloud environments described above.

If you work frequently in the third V, several problems. You will benefit much more from an expensive laptop (relatively speaking).

Machine learning

And if you work with machine learning algorithms, then, as you probably know, you will get better results with more and more data, this translates to algorithms that need both CPU and memory. If you plan to do your data analysis on your laptop, then focus on CPU and memory.

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

R

Dealing with long data sets with R is also easier with more cores. Getting more cores can help too, but only up to a point. R itself can generally only use one kernel at a time internally.

Also, for many data analysis problems, the bottlenecks are disk I / O and RAM speed, so efficient use of more than 4-8 cores on basic hardware can be difficult.

Hadoop

A common approach is to use a sample from the large data set, a large sample that can fit in memory. With Hadoop, you can now run many exploratory data analysis tasks on entire data sets, without sampling.

Just write a map reduction job, a PIG or HIVE script, run it directly in Hadoop on the full dataset, and get the results right on your laptop.

In many cases, machine learning algorithms perform best when they have more data to learn, particularly for techniques such as clustering, outlier detection, and product recommendations.

Historically, large data sets were either unavailable or too expensive to acquire and store, so machine learning professionals had to find innovative ways to improve models with fairly limited data sets.

With Hadoop as the platform that provides linearly scalable storage and processing power, you can now store ALL your data in RAW format and use the entire data set to create better and more accurate models.

Python / Pandas

Data Analysis – Using pandas to read CSV and Excel files, clean, filter, partition, aggregate and summarize data, and produce simple charts

Similarly, if your application requires joining large tables with billions of rows to create feature vectors for each data object, HIVE or PIG are very useful and efficient for this task.

Training a heavy neural network can be out of the reach of any laptop, as doing a large repeated measures analysis (variance/covariance matrix explodes exponentially)

All the answers are great.

Pay close attention to those sections.

Most machine learning algorithms are CPU intensive and memory intensive. Look for the Intel Core i7 processor, which is currently the best processor and the 4-core is ideal when you have to take advantage of the thread for large data sets. Remember that I am also talking about data manipulation work along with computing.