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Both R and Python continue to make substantial contributions to data science.

Python and R are often regarded as crucial programming languages for data scientists. You should ideally master both for a well-rounded programming foundation, but where should you begin if you’re new to data science?


According to Stack Overflow data, Python is the world’s fastest-growing programming language. It’s easy to use for beginners, but it also gives web developers the freedom they need to create sites like Spotify, Instagram, Reddit, Dropbox, and the Washington Post. Do you have any idea what a regression is or how to use a caret? Python will provide you with a more welcoming environment, to begin with. Python, like Javascript or C++, is an object-oriented programming language that provides stability and modularity to projects of any size. It gives a flexible approach to web development and data science that appears intuitive, even if you’ve never learned a programming language before. Python training equips programmers with the skills they need to work in a wide range of sectors.

R language

R is a computer language for data analysis and statistics that is domain-specific. It is an important part of the research and academic data science field since it uses statisticians’ specialized syntax. The procedural development paradigm is used in R. Rather than grouping data and code into groups as object-oriented programming does, it splits programming jobs into a series of phases and subroutines. These strategies help visualize how complex procedures will be carried out. R has a sizable user base, similar to Python, but with a focus on data analysis. R, unlike Python, does not offer general-purpose program creation, but it excels at addressing specialized data science problems since that is all it does.

What is the difference between python and R language?

Python is a general-purpose programming language developed for a range of use cases, whereas R is more specialized. Python code may be easier to understand and more widely applicable if this is your first excursion into programming. The R language, on the other hand, maybe more suited to your needs if you already have some experience with programming languages or have specific professional aspirations focusing on data analysis.

There are many parallels between the Python and R languages, so knowledge of one can help with the other. Python and R, for example, are prominent open-source programming languages with active communities. Both may be practiced in the language-agnostic Jupyter Notebooks environment, as well as in other programming languages like Julia, Scala, and Java.

Let’s see how R and Python stack up against the criteria data scientists use to make decisions:

Statistics: R outperforms Python in terms of statistical support, with more statistical packages available than Python.

Ease of use — Python is thought to be simple to learn and use, but R is thought to have a high learning curve. The readability of Python is said to be much better than that of R. Python’s native object support is a significant point in its favor.

Speed — Because R is a low-level language, it is eclipsed by Python, which is a high-level language that produces results quicker while consuming less memory.

Data analytics- R successfully handles massive data sets, with a plethora of packages to choose from, making implementation a breeze. While Python is still improving, with new packages being introduced regularly,

Deep learning- Python outperforms R in terms of deep learning, with smooth integration for TensorFlow, Keras, and other frameworks. With the addition of new packages, R’s capabilities continue to grow. However, it still has a long way to go.

Visualization – R’s visualization features are one of the reasons for its appeal. R offers powerful graphical features that are accessible through packages, but Python visualization may be time-consuming and untidy.

Community support — The Python community continues to grow and strengthen, with fewer migrations to the R community.

Which programming language is better to learn?

Python is the way to go if you want to learn more about computer programming in general. If you only want to work with statistics and data, R could be the better choice. It depends on you which you want to learn and go for. Ask yourself a few questions to help you decide whether to study Python or R initially:

What are your professional objectives? Choosing between business and academia, for example, might help you figure out which will be more beneficial to you in the beginning. It might also assist to consider how much you’d like to keep your choices open or which initiatives are most essential to you.

What do you think you’ll be doing with most of your time? R may win out over Python if you expect to stick with statistical analysis in most research endeavours. However, if you want to design systems that are ready for production, you may need greater flexibility.

What are your plans for disseminating your findings? Examining the many ways Python and R may help you visualize data can also help you narrow down your initial step.

Is Python or R more user-friendly?

Python is far more user-friendly, with syntax that is more akin to that of written English. If you have experience with other languages, though, R makes it easier to display and handle data. Because it’s based on statistics, the syntax is clearer for analysis. R may need more initial effort than Python. R, on the other hand, may make some sorts of chores considerably easier after you’ve mastered the syntax. The more programming languages you’ve learned, the easier it is to learn another.

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Steps For Effective Text Data Cleaning (With Case Study Using Python)


One of the first steps in working with text data is to pre-process it. It is an essential step before the data is ready for analysis. Majority of available text data is highly unstructured and noisy in nature – to achieve better insights or to build better algorithms, it is necessary to play with clean data. For example, social media data is highly unstructured – it is an informal communication – typos, bad grammar, usage of slang, presence of unwanted content like URLs, Stopwords, Expressions etc. are the usual suspects.

In this blog, therefore I discuss about these possible noise elements and how you could clean them step by step. I am providing ways to clean data using Python.

As a typical business problem, assume you are interested in finding:  which are the features of an iPhone which are more popular among the fans. You have extracted consumer opinions related to iPhone and here is a tweet you extracted:

[stextbox id = “grey”] [/stextbox]

Steps for data cleaning:


Here is what you do:

Escaping HTML characters: Data obtained from web usually contains a lot of html entities like &lt; &gt; &amp; which gets embedded in the original data. It is thus necessary to get rid of these entities. One approach is to directly remove them by the use of specific regular expressions. Another approach is to use appropriate packages and modules (for example htmlparser of Python), which can convert these entities to standard html tags. For example: &lt; is converted to “<” and &amp; is converted to “&”.

Decoding data: Thisis the process of transforming information from complex symbols to simple and easier to understand characters. Text data may be subject to different forms of decoding like “Latin”, “UTF8” etc. Therefore, for better analysis, it is necessary to keep the complete data in standard encoding format. UTF-8 encoding is widely accepted and is recommended to use.

[stextbox id = “grey”]


tweet = original_tweet.decode("utf8").encode(‘ascii’,’ignore’)



Apostrophe Lookup: To avoid any word sense disambiguation in text, it is recommended to maintain proper structure in it and to abide by the rules of context free grammar. When apostrophes are used, chances of disambiguation increases.

For example “it’s is a contraction for it is or it has”.

All the apostrophes should be converted into standard lexicons. One can use a lookup table of all possible keys to get rid of disambiguates.

[stextbox id = “grey”]


APPOSTOPHES = {“'s" : " is", "'re" : " are", ...} ## Need a huge dictionary words = tweet.split() reformed = [APPOSTOPHES[word] if word in APPOSTOPHES else word for word in words] reformed = " ".join(reformed)



Removal of Stop-words: When data analysis needs to be data driven at the word level, the commonly occurring words (stop-words) should be removed. One can either create a long list of stop-words or one can use predefined language specific libraries.

Removal of Punctuations: All the punctuation marks according to the priorities should be dealt with. For example: “.”, “,”,”?” are important punctuations that should be retained while others need to be removed.

Removal of Expressions: Textual data (usually speech transcripts) may contain human expressions like [laughing], [Crying], [Audience paused]. These expressions are usually non relevant to content of the speech and hence need to be removed. Simple regular expression can be useful in this case.

Split Attached Words: We humans in the social forums generate text data, which is completely informal in nature. Most of the tweets are accompanied with multiple attached words like RainyDay, PlayingInTheCold etc. These entities can be split into their normal forms using simple rules and regex.

[stextbox id = “grey”]


cleaned = “ ”.join(re.findall(‘[A-Z][^A-Z]*’, original_tweet))



Slangs lookup: Again, social media comprises of a majority of slang words. These words should be transformed into standard words to make free text. The words like luv will be converted to love, Helo to Hello. The similar approach of apostrophe look up can be used to convert slangs to standard words. A number of sources are available on the web, which provides lists of all possible slangs, this would be your holy grail and you could use them as lookup dictionaries for conversion purposes.

[stextbox id = “grey”]


            tweet = _slang_loopup(tweet)



Standardizing words: Sometimes words are not in proper formats. For example: “I looooveee you” should be “I love you”. Simple rules and regular expressions can help solve these cases.

[stextbox id = “grey”]


tweet = ''.join(''.join(s)[:2] for _, s in itertools.groupby(tweet))



[stextbox id = “grey”]

[stextbox id = “grey”]

Final cleaned tweet:



Advanced data cleaning:

Grammar checking: Grammar checking is majorly learning based, huge amount of proper text data is learned and models are created for the purpose of grammar correction. There are many online tools that are available for grammar correction purposes.

Spelling correction: In natural language, misspelled errors are encountered. Companies like Google and Microsoft have achieved a decent accuracy level in automated spell correction. One can use algorithms like the Levenshtein Distances, Dictionary Lookup etc. or other modules and packages to fix these errors.

End Notes:

Go Hack 🙂

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Hspa+ Vs Lte: Which One Is Better?

Bottom Line

T-Mobile’s HSPA+42 network performs admirably against, what is supposed to be, far superior LTE networks. The high speeds offered by this network should be more than enough for most users.

LTE is fast! While HSPA+ is definitely good enough, the LTE networks(in their current state) are 20-30% faster. A big plus for all the speed demons out there.

Unrelated to the topic, but is anyone as surprised as I am at how poorly Sprint’s Wi-Max network performed?

Granted these results for HSPA+ aren’t standard the world over, with most networks featuring 21Mbps download capabilities. But all these network carriers are planning to upgrade to 42Mbps and even 84 Mbps networks, so HSPA+ still has a lot of potential, and is certainly “good enough” for now.

While a lot of countries are marked “red” indicating countries with commercial LTE services, it is slightly misleading. For example, while India as a whole is marked, only one carrier(Airtel) offers 4G LTE services in only one city(Kolkata) thus far. Of course, plans are in the works to rollout the network eventually, it will take quite a lot of time before complete coverage is achieved.

The above point is true for most countries, with none boasting a full coverage LTE network. Full coverage in some regions will be achieved at the earliest by late-2013 to early-2014, with most others much later.

On the other hand, HSPA+ is more along the lines of a software enhancement that elevates 3G data network performance. Of course, the process isn’t as simple as it sounds, but it is definitely easier than building a completely new LTE supported network. As such, any carrier that has an established 3G network, have upgraded to an HSPA+ network. With over 100 network carriers worldwide featuring HSPA+ networks with most boasting over 80% coverage. To keep up with current LTE speeds, carrier networks are also upgrading the their “slower” 21Mbps networks to 42Mbps or even 84Mbps (theoretical) download speeds.


AT&T and Verizon have standard data rates of $50 for 5GB regardless of whether you have access to 3G, HSPA+, or 4G LTE networks.

 T-Mobile, which currently features a 42Mbps HSPA+ networks, also features the same data rates.

In India, while available only in Kolkata so far, 4G LTE costs Rs 1399(~$28) for 9GB, with HSPA+ being Rs 1250(~$25) for 10GB.

Device availability

Most smartphones and tablets (3G versions) released in the last 2 years or so can access the faster speeds offered by HSPA+ networks.

On the other hand, accessing a LTE network requires a significant hardware change, i.e. the need for an LTE radio.

Options for LTE capable devices are comparatively limited and generally range towards the higher end of the price spectrum.

There has been an incompatibility issue with latest NVIDIA quad-core Tegra 3 processor and LTE radios, as seen with the HTCOne X where the international version features the quad-core processor, and the US releases with LTE radios “falling back” on Qualcomm dual-core Snapdragon S4 processors. Whether this issue will be prevalent in other quad-core processors such as the Samsung Exynos 4412, is yet to be seen.

LTE radios are also infamous for being a huge drain on battery life.

The Future


Backward compatibility with existing WCDMA and HSPA networks. This provided the possibility of an easy transition to LTHE as opposed to a network upgrade to LTE.

Theoretical download speeds up to a whopping 672 Mbps.

Carriers and hardware companies claimed that LTHE could have been ready for deployment by 2013.

Unfortunately, almost every network around the world has decided to move onto LTE as their network for the future. HSPA+ networks will likely be upgraded to the 42Mbps or even 84Mbps download capability, but now, it seems like that is as far as this evolutionary technology will be pushed.

 TD-LTE:  TD-LTE or Time-Division LTE was developed by China Mobile over the last few years. Unlike LTE networks which carries two separate signals for data traveling in either direction, TD-LTE features a single channel and allocated upload and download bandwidth depending on your usage. This accounts for higher data speeds. TD-LTE is also compatible 4G WiMax and it will easier to upgrade from WiMax to TD-LTE than to LTE.

LTE Advanced: LTE Advanced is a further evolution of current LTE networks which brings with it theoretical peaks of 1GBps download speeds, increased spectrum efficiency(upto 3 times more bandwidth), and reduced latency. Like the upgrade from HSPA to HSPA+, a move from LTE to LTE-Advanced is also a software deployment upgrade.


Advantages of LTE over HSPA+

Much better spectrum efficiency

Far lower latency

LTE has a simpler architecture compared to an HSPA+ network

Advantages of HSPA+ over LTE

HSPA+ is an already established network, whereas complete LTE coverage still has a while to go

HSPA to HSPA+ evolution required much less investment in infrastructure and was less costly to upgrade as opposed to LTE which needs a completely new network built from the ground up.

LTE requires specific LTE radio featured devices, whereas HSPA+ is available to any user with a 3G enabled phone.

Which Ubuntu Flavor Should You Choose

If you are a fan of Ubuntu but not a fan of Gnome, what can you do? You should know that you are not stuck using the Gnome version of Ubuntu. You can install another desktop environment or simply use another “flavor” of Ubuntu that can be another desktop manager by default. Let’s see how they differ and which Ubuntu flavor would be better for you.

What Is Ubuntu Flavor?

Ubuntu flavors are generally Ubuntu running with a different desktop environment. The default desktop environment used in Ubuntu is Gnome, but not everyone is a fan of Gnome. Some may be a fan of KDE, while others are more used to the older Mate desktop. The purpose of the various Ubuntu flavors is to cater to these groups of people. There are several official Ubuntu flavors that are recognized and supported by Canonical, the company behind Ubuntu. The different flavors are:



Ubuntu Budgie

Ubuntu Kylin


Ubuntu Mate

Ubuntu Studio


Ubuntu Cinnamon

Ubuntu Unity

Good to know: learn more about Linux by checking out some of the best Arch-based distros available today.


Kubuntu 23.04 comes with the KDE Plasma desktop environment. KDE is much more customizable than Gnome, making Kubuntu the perfect choice for those who demand a modern, ultra-customizable desktop and aren’t afraid they’ll get lost among the dozens of options.


Kubuntu swaps all gnome-related applications for KDE alternatives. KDE, though, also has a broader variety of applications tied to it.

If you were using previous versions of KDE, you may also notice that the default music player has switched from Cantata to Elisa.

Plasma 5.27

Plasma 5.27 has a Global Edit mode that replaces the customization menu on the top right of the screen with a bar at the top center of the screen. From there, you can add widgets to the desktop, create extra workspaces, or access the desktop configuration options.

KDE supports a “Do Not Disturb” mode that suppresses notifications. It goes excellent with KDE’s support for Night Color, which tweaks the screen’s color temperature.

Like Gnome, KDE comes with three versions of its Breeze theme: light, dark, and Kubuntu’s default, that looks like a hybrid of the other two.

To assist in its customization when tweaking its settings, KDE now presents a preview of the results arranged in a grid view. This grid view is also used when downloading new themes, helping to appreciate the differences more.


LXQt 1.2.0 is front and center in Lubuntu 23.04. If you need a lightweight but functional Ubuntu flavor, you should give Lubuntu a try.

Quick but Basic Desktop

LXQt works like KDE, presenting a default taskbar with a primary menu, a task-juggling section, and an additional tray. Unlike KDE, though, LXQt trades vast configurability and visual effects for a more lightweight and straightforward desktop experience.

A Ton of Themes

Lubuntu comes with many different LXQt and OpenBox themes that you can mix and match.


Since it’s based on Qt, Lubuntu uses KDE’s Discover application instead of Ubuntu’s default store for finding and installing new software.

As far as daily use, Lubuntu feels like a “Kubuntu Lite” and is an excellent option for everyone seeking a less resource-heavy alternative to both Ubuntu and Kubuntu.


Unlike the Gnome and KDE flavors, Lubuntu 23.04 uses the Calamares installer. That means no support for installing the OS itself in a ZFS partition through the default initial setup.

Ubuntu Budgie

Ubuntu Budgie uses the Budgie desktop environment that was initially found in the Solus project. Budgie is based on GTK+ and, in many ways, feels like Gnome 3 from an alternative planet. It seems Gnome’s developers decided to stick with the way Gnome 2 worked.

Ubuntu Budgie is made for everyone who seeks a beautiful but straightforward desktop, which will work as expected but isn’t lacking in modern features and aesthetics.

Great Welcome Window

The Budgie flavor comes with a stellar Welcome Window that links to all the options anyone may need to tweak after installing a new operating system.

Friendly, Modern Desktop

By default, it presents a bar at the top of the screen where you can access the primary menu, see the time and jump to related settings (and the calendar) as well as a group of icons on the right side. From there, you can access QuickNote that runs by default, jump to folders in your home directory or check the contents of removable devices, check out and control the network and audio, and access the usual logout/shutdown menu.

Instead of including a task panel in its main bar, Ubuntu Budgie relies on the Plank launcher for access to favorite apps and the juggling of active ones.

Budgie desktop offers nine different themes that you can either apply instantly or install. What’s even better is that it also offers different Desktop Layout themes, with two that will probably look more friendly to people coming from Windows or Mac.

Budgie desktop bundles together its notifications with a group of applets. They are accessible from individual icons displayed in the tray we described above and presented as two tabs in the same panel on the right side of the screen. Those applets consist of a mini-calendar and audio controls – global, application, and device-based.

Ubuntu Kylin

Unlike the other flavors of Ubuntu that target the whole world, Ubuntu Kylin is made for the Chinese audience. Although its beautiful UKUI desktop environment might render it enticing to everyone outside China, it ends up feeling restrictive, like you have to jump through hoops to use it.

Original Desktop

Ubuntu Kylin’s UKUI desktop doesn’t try to reinvent the wheel. It presents the classic taskbar on the bottom of the screen with a primary menu button on the left, followed by links to favorite apps, a list of active windows, and finally, a tray with icons on the right side of the screen.


Ubuntu Kylin offers its own software center, and this is where people outside China may start looking for a different distribution.

Unfortunately, everything in Kylin Software Center is in Chinese, with the occasional English program name. That includes its interface, all category names, buttons, and menu entries. And there doesn’t seem to be an option to change the language.

Ubuntu MATE

Ubuntu MATE is closer to Kubuntu in that, based on the MATE desktop environment, it presents a modern take on classic desktop tropes. As a true evolution of the Gnome 2 desktop environment, MATE is familiar and easy to use but doesn’t lack polish and shine.

Like Ubuntu Budgie, this is as close to a stable but modern Gnome 2 distribution as anyone can get. In direct comparison, MATE leans more toward classic Gnome 2 compared to the more modern Budgie.

Friendly and Useful Welcome Window

On the first bootup, Ubuntu MATE shows a Welcome window that contains useful options.

A “Getting Started” section presents links to all the options that are useful after a new installation. These allow you to:

Download updates and drivers

Change the language and input

Set up backups

Configure network shares

Configure the firewall

Set up users

Install new software

Install new color themes and swap between their “default,” light, and dark variants

Change the Desktop Layout between four choices: the default MATE setup with two bars at the top and bottom of the screen, one that mimics Unity with a bar on top of the screen and a launcher on the left side, and the two expected options that work like Windows or Mac OS X.

Install more browsers and choose which one you want as the default.

You can configure the most critical aspects of your desktop from this window, then start using your computer without having to hunt down more settings.

A Desktop for Everyone

Ubuntu MATE offers eight layout styles, and you’ll find at least one that feels familiar and friendly.

There is also an updated notification center that allows the user to define the number of visible notifications, automatically discard notifications by specific applications, and toggle a “Do Not Disturb” mode.


The installation of new software is done through MATE’s Software Boutique, which feels more polished than the default Ubuntu store and KDE’s Discover app. There doesn’t seem to be a preference to snap versions of applications, but at the same time, it looks like the Software Boutique gives access to a somewhat limited selection of software.


Xubuntu comes with the XFCE desktop environment that skips glossy graphics and useless fluff to offer a light and breezy desktop experience. Although it’s fully featured as a desktop, it’s also resource-friendly enough to use on older or lower-end PCs.

Xubuntu is probably the only relatively “resource-lite” version of Ubuntu that’s best suited for old and underpowered PCs.

Straightforward Desktop Experience

XFCE comes with a “dark” spin on its default “Greybird” theme, and four other styles that change how the visual elements look (toolbars, buttons, menus, windows, etc.). Unfortunately, for optimal results, you have to tweak the visual settings at two different spots.


Xubuntu uses the same software store as Ubuntu. If you need to install more applications, they’re only a snap away.

Ubuntu Studio

The new version of this media-centric flavor gets all the benefits of the new kernel but is more of an evolution from the previous releases. It comes bundled with multimedia applications for every need, from audio to DTP. Theoretically, after its installation, you already have everything you need to make your own movie from scratch, from writing the first draft of its scenario to color-correcting and compressing the final cut.

It’s worth noting that its maintainers decided to jump ship from XFCE to KDE since version 20.10 due to its “better tools for graphic artists and photographers.” Thus, if you are upgrading from an older version of Ubuntu Studio, you may experience software breakage.


Unlike the previous entries, Edubuntu is an Ubuntu flavor that does not stray away from the default Ubuntu look. Instead, it aims to provide a complete and comprehensive collection of educational software. This makes Edubuntu the perfect Ubuntu flavor if you are looking for an all-in-one kid-friendly Linux distribution.

For Learning and Play

While Edubuntu is primarily focused on providing a learning environment for students, it also comes with a bunch of high quality and informative games. For example, the default install comes with both the GCompris and gbrainy out of the box.

Good to know: education-focused programs come in all shapes and sizes. Learn more about some of the best kid-friendly software in Linux today.


Similar to a regular Ubuntu desktop, Edubuntu also comes with the Ubuntu Software Center. This means that you can install almost any type of software on top of the flavor’s carefully curated selection.

Aside from that, another key feature of Edubuntu is its ability to quickly customize and manage the software available in the system. It does this through the “Edubuntu Installer” and “Edubuntu Menu Administration” utilities.

Ubuntu Cinnamon

Ubuntu Cinnamon is a relatively new flavor that aims to provide an official, Canonical-endorsed Ubuntu variant that uses Linux Mint’s Cinnamon desktop environment. It works by pulling the sources for Cinnamon straight from Linux Mint and only including the most essential packages.

Desktop Flexibility with Spices

For example, you can install the QRedshift applet that provides an easy-to-use interface for controlling your screen’s color temperature. Meanwhile, you can also install larger extensions, such as gTile, that can convert your desktop into a tiling window manager.

FYI: learn how to take your Linux desktop to the next level by installing bspwm and sxhkd.


Ubuntu Cinnamon also comes with the default Ubuntu Software Center. It is possible to install both traditional software packages and modern web apps without any additional configurations.

Ubuntu Unity

Ubuntu Unity is an elegant flavor that aims to provide an accessible yet powerful distribution centered around the classic Unity desktop. It does this by using the original Unity7 environment and modifying it to use the latest MATE software suite.

Spearheading Unity Development

The developers of Ubuntu Unity also took the mantle and started to maintain the legacy Unity7 environment. This includes updating dependency requirements as well as fixing outstanding bugs in the desktop code. As a result of that, the Unity desktop is now faster and works on other Linux distributions, such as Manjaro and Gentoo.

Aside from maintenance, Ubuntu Unity also pushes the boundaries of the Unity7 desktop. For example, a recent update introduced a new menu interface as well as a built-in notification applet that allowed you to see new system events.


One of the biggest downsides of using Ubuntu Unity is that it does not have a graphical software store. You need to be familiar with basic APT and Snap commands to install, modify and remove programs.

Tip: Learn how to use your distro’s package manager effectively through our cheatsheet.

Frequently Asked Questions Is it possible to switch to a different Ubuntu flavor?

Yes and no. It is possible to install the desktop environment and software packages that come with an Ubuntu flavor. However, an Ubuntu flavor often comes with specific package versions and configurations that a software package might not cover.

Can you use snap packages on Kubuntu?

Yes. You can do this by running the snap command from your command line. For example, running sudo snap install firefox will bypass the Firefox APT package and install its Snap version.

Is it possible to upgrade from Ubuntu 17.04 to Ubuntu Unity?

No. While Ubuntu Unity uses a slightly modified version of the Unity7 desktop, it does not support any direct upgrades from either Ubuntu 16.04 or 17.04.

Image credit: Unsplash (Background), Wikimedia Commons (Logo). All alterations and screenshots by Ramces Red.

Ramces Red

Ramces is a technology writer that lived with computers all his life. A prolific reader and a student of Anthropology, he is an eccentric character that writes articles about Linux and anything *nix.

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7200 Vs 5400 Rpm – Which One Is Better?

Today’s users prefer SSD over a mechanical hard drive for better performance, speed, and higher bandwidth. Nonetheless, if you’re looking for a larger storage capacity at an affordable price, it’s best to get an HDD.

Although 12000 to 15000 RPM hard drives are readily available, those with 5400 and 7200 are more popular among home users. While rotational speed is an essential factor, you should also consider areal density (amount of storage per unit area) if you’re planning to install a new HDD.

Moreover, if you’re already utilizing a 5400, is it worth switching to a 7200? Certainly, there’s much more difference than just the 1800 RPM difference, and that’s exactly what we will cover in this article.

Hard Drive Platters Can Rotate at Hundreds to Thousands of Revolutions

Well, a circular platter has several tiny areas that can be magnetized or demagnetized. While smaller drives usually come with a single platter, the bigger ones can have multiple ones stacked on the motor spindle.

Interestingly, the platters can rotate at several hundred to thousands of revolutions (low as 1200 and high as 15000) in a single minute. That’s exactly what Revolutions Per Minute (RPM) means, and this also helps determine the access time, meaning the higher the number of revolutions, the faster the data retrieval.

Firstly, 5400 RPM means that the platter on a hard drive rotates 5400 times in a minute to perform read/write operations. We recommend using them if you want to run an OS, store huge files, and execute low-end programs.

Moreover, 5400 hard drives are considered best if you’re trying to add more storage to your PC, build a low-power server, or NAS (Network Attached Storage). This is mainly because a comparatively slower drive (than 7200 RPM) likely consumes less power and produces less heat. Likewise, they are mostly preferred on laptops due to their portability and heat dissipation requirements. 

Furthermore, the current 5400 RPM drives that use PMR (Perpendicular Magnetic Recording) have become much faster than the older 7200 mechanical disks using LMR (Longitudinal Magnetic Recording). This is simply because the former achieves areal density higher than the latter, ensuring quicker head movements.


Consumes less power

Produces less heat

Produces less noise


Longer lifespan

Reduced battery’s operating time


Slower file transfer

Slower read/write speed

Greater rotational latency time

Weaker performance

7200 RPM hard drives are those where the platter rotates 7200 times in a minute to perform read/write operations. Usually, they are preferred by professionals for incredible speed and performance. Although 7200 is usually installed on desktop computers, you can still use them on laptops having good cooling performance.

Well, normal users may not find much difference in the performance between 5400 and 7200 RPMs. Nonetheless, if you’re a professional or an avid gamer, you know that the latter slightly edges past in many aspects. For example, if you try loading games on both, you’ll likely notice that the waiting time in 7200 is way shorter.

Since these hard drives have faster rotational speeds, the data transfer rate is quicker. Likewise, the read/write speed is also better, meaning data can be read from and written faster from a storage device than the 5400 mechanical disks. In fact, the data delivery in 7200 is approximately 15% to 33% faster, even when the areal densities of both HDDs are identical.


Greater performance

Faster rotational access

Faster read/write speed

Better data transfer speed


Expensive for normal users

Excessive heat generation

More power consumption


Shorter lifespan

Moving back to 7200 and 5400 RPMs, they differ in terms of data throughput, rotational latency, power consumption, heat production, noise production, lifespan, and cost. Now, without further delay, let’s jump into each of these factors. 

As mentioned earlier, 7200 RPM is approximately 15% to 33% faster than 5400 RPM hard drives. Well, the average read/write speed of the former is 100 MB/s, while the latter is slightly higher (120 MB/s).

Therefore, if you’re getting an HDD for better performance and speed, we recommend getting 7200 RPM instead of 5400.

An HDD head must be positioned correctly whenever a read/write operation is performed. This process is called seeking, and the delayed waiting time for the disk sector to get to the read/write head is the rotational latency, also referred to as rotational delay. Moreover, this is measured in ms (milliseconds).

From the above mathematical illustration, it’s pretty clear that the rotational latency of 7200 RPM is less and thus has better performance.

Like any computer peripheral, hard drives require power to run and perform read/write operations. In fact, they need to run constantly and consume power even when they stay idle. However, this can vary depending on the RPM type.

Generally, a 5400 hard drive consumes 0.18 Watts when idle, and 1.4 Watts during read/write operation. On the other hand, a 7200 mechanical disk can draw approximately 4 Watts when idle and at least 8 Watts at load.

Hence, if you’re looking for a storage device that consumes less power, we recommend getting a 5400 RPM. 

Excessive heating has been a common problem among both PC and laptop users. Thus, choosing the right hard drive can help prevent your system from overheating in the long run.

As mentioned earlier, hard drives have motor spindles for rotating circular platters. Hence, heat production is an obvious thing, and the temperature can range anywhere from 0 to 70 degrees Celsius. However, this varies on various factors – model, type, and, surprisingly, RPM too.

Since 7200 revolves much quicker and consumes more power, the heat production is also high here. Thus, if you’re not a big fan of a constantly heating PC or laptop, it’s best to opt for 5400 RPM.

Basically, the faster the rotations, the greater will be the noise production. This simply means that a 7200 RPM hard drive produces comparatively more noise than a 5400.

Every hardware component has a lifespan and is bound to die one day. As such, most mechanical hard drives will also fail after three to five years. Regarding the same, we have prepared a separate guide for you on the six signs to know if your hard drive is failing. 

Theoretically, a 5400 RPM mechanical drive should last longer than a 7200 one if built using the same materials and techniques. This is primarily because the latter spins much faster and thus would be prone to more stress and heat.

Since 7200 RPM drives are better in terms of performance and speed, they are comparatively more expensive than 5400 ones.

However, the cost can vary depending on the brand, model, and amount of cache. Well, the popular hard drive manufacturers include WD, Toshiba, Seagate, SanDisk, etc. Interestingly, you may notice that the price of one brand’s 7200 RPM drive is almost similar to another brand’s 5400 RPM. For example, the Seagate Barracuda 2TB at 7200RPM and the Western Digital Blue 2TB 5400RPM are of the exact cost.

Switching from 5400 RPM to 7200 RPM will surely enhance performance and provide better speed. However, you should also be ready for its higher power consumption, heat and noise production, higher cost, and lower life expectancy.

Well, if you’re already planning to upgrade your storage system, we recommend using an SSD any day. Nonetheless, if you still prefer a mechanical hard drive, the below comparison chart should help you decide which one is better.

Factors5400 RPM7200 RPMData Throughput100 MB/s (average)120 MB/s (average)Rotational Latency5.56 ms4.17 msPower Consumption0.18 Watts (idle), 1.4 Watts (load)4 Watts (idle), 8 Watts (load)Heat ProductionComparatively lessComparatively moreNoise ProductionComparatively lessComparatively moreLifespanLasts longerLasts shorterCostLess expensive (depends on brand and cache)More expensive (depends on brand and cache)Best Suitable ForLaptopsDesktopsComparison Chart: 5400 RPM Vs 7200 RPM

What Data Scientists Really Do?


Have you ever wondered what data scientist job description look like? Data scientists, a unique blend of mathematicians and computer scientists, play a crucial role in the big data industry. Businesses need help to extract meaningful insights from vast amounts of unstructured data, but data scientists possess the expertise to unearth valuable information. Their high demand and generous compensation are driven by their ability to navigate this data maze and separate the wheat from the chaff. The allure of solving challenging puzzles and lucrative paychecks has attracted IT professionals to this field. This article serves as a comprehensive guide to the captivating world of data science, catering to those interested in pursuing this rewarding career path.

Who is a Data Scientist?

Data holds immense significance in today’s business landscape. Data scientists decode its meaning, uncover hidden insights, identify trends, and develop strategies for organizational growth

A data scientist excels in collecting and analyzing vast amounts of data using programming, statistics, and analytical skills. They play a vital role in organizations, leveraging data to create customized solutions.

By gathering and analyzing information from diverse sources, data scientists gain a clear understanding of organizational dynamics. They employ statistical, analytic, and AI tools to automate processes and devise smart responses to business challenges, aiding in trend analysis. Effective data scientists possess technical, analytical, and communication skills.

Let’s look at data scientist job description now:

Data Scientist Job Description

Data Scientists have a wide range of responsibilities. They work in various sectors, from business to technology to academia. Here is the data scientist job description:

The extraction of useful data from valuable sources is known as data mining.

Choosing features, building classifiers, and optimizing them using machine learning tools

Performing structured and unstructured data preprocessing

Improving data collection processes to include all pertinent data for creating analytical systems

Preparing, cleaning, and ensuring the accuracy of data for analysis

Finding patterns and solutions by analyzing a lot of data

Creating machine learning algorithms and prediction systems

Clearly presenting the results

Offer tactics and solutions to deal with business challenges.

Team up with the business and IT departments

Data Scientist Skills

If you want to pursue a career as a data scientist, you must become proficient in the skills needed for jobs in various organizations and industries. Let’s examine the essential skills of data scientists. Key competencies for a data scientist include:

Statistics: A strong understanding of statistical tests, distributions, regression, maximum likelihood estimators, and other applied statistical concepts is required in statistics. For businesses that rely on data, statistics expertise is crucial.

Machine Learning: Knowledgeable of machine learning techniques such as k-Nearest Neighbors, Naive Bayes, SVM, and Decision Forests.

Strong Mathematical Skills: Data scientists must have a solid grasp of mathematics, including algebra, statistics, and calculus. The ability to work with mathematical models is a must.

Data Wrangling: Proficiency in handling imperfections in data is essential to a data scientist’s job description.

Data Visualization: Experience with Data Visualization Tools like matplotlib, ggplot, d3.js., Tableau helps to encode data visually.

Excellent Communication Skills: Describing findings to a technical and non-technical audience is essential.

Source: Berkeley Boot Camps

How to Become a Data Scientist?

Our AI & ML BlackBelt Plus Program can help you reach your dream career faster. With one-on-one mentorship with guided projects and a comprehensive & personalized learning path, you are prepared for your AI and ML jobs.

The BB+ program offers weekly mentorship calls, allowing students to engage with experienced mentors who can guide them on their data science journey and offers the chance to work on industry projects under the guidance of experts. Analytics Vidhya’s BB+ program provides personalized recommendations tailored to each student’s needs and goals.

This personalized approach ensures that students can optimize their learning path and address their unique learning requirements, accelerating their journey toward becoming successful data scientists.

How Much Do Data Scientists Make?

Data scientists typically have higher demand and pay than most other ITES jobs. One of the most important factors in determining a data science professional’s salary range is experience. The following table shows the average salary for data scientists in different countries:

CountryAnnual Average SalaryIndia₹ 10.0 LPAUSA$124,016UAEAED 179,976UK£49,758New Zealand$219,489Australia$121,781

Career Growth and Opportunities

Currently, data science is regarded as one of the most lucrative professions. Data scientists are needed by businesses in all major sectors and industries to assist them in gaining insightful knowledge from large data. Demand for highly qualified data science specialists who can work in the business and IT worlds is rising quickly.

Data scientist careers offer various job paths. Data science is an interdisciplinary field. Thus there are multiple exposure and exit choices. The following are the most common designations for data science professionals:

Data Analyst

Associate data scientist

Data Scientist (senior-level)

Product Manager

Lead data scientist


Your progress will be based on how quickly you can pick up the necessary abilities to analyze massive data sets and how dedicated you are to your company. Your employer may promote you to more senior data science positions that could require line management of less experienced data scientists.

It may be rather simple to transition into new firms because the abilities you get are transferable across various industries.

A different route is to work on initiatives that larger organizations outsource by joining a start-up business. You might also find opportunities to move into larger organizations’ research and development (R&D) departments.

Source: Jerome Chamber of Commerce

Challenges and Future Trends in Data Science

Data science is the application of data and analytics to solve problems that are frequently complex (or numerous) and unstructured. The term “fishing expedition” is used in the analytics sector to describe a project that was never planned correctly in the first place and involves looking through the data for unexpected connections. Although this particular form of “data fishing” does not follow the rules of effective data science, it is nonetheless quite widespread. Therefore, defining the problem is the first thing that must be done.

Some of the challenges in data science include:

Data Preparation for Smart Enterprise AI

A data scientist’s top priority is locating and purging the appropriate data.

According to a CrowdFlower survey, cleaning, organizing, mining, and acquiring data take up about 80% of a data scientist’s day. The data is verified twice at this level before going through more processing and analysis.

The ideal way to handle this situation is to adopt AI-based solutions that assist data scientists in keeping their competitive edge and improving their efficacy. Augmented learning is a customizable AI tool for the workplace that helps with data preparation and illuminates the subject at hand.

Data Generation from Multiple Sources

Now, businesses may create complex virtual data warehouses with centralized platforms to consolidate their data sources in one place. To meet a company’s needs and boost productivity, altering or manipulating the data kept in the central repository is feasible.

Communication of Results to Non-Technical Stakeholders

A data scientist’s primary goal is to improve the organization’s decision-making capabilities, consistent with the business strategy that this role supports. Effectively conveying their findings and interpretations to corporate leaders and managers is the biggest challenge for data scientists. It is crucial to give managers and other stakeholders the correct foundational concept to apply the model using business AI because most of them are not familiar with the tools and technologies used by data scientists.

To do so effectively, data scientists must include ideas like “data storytelling” in their analyses and visualizations.

These are a few challenges faced by the data scientists. However, new technologies are now making firms more productive and improving their return on investment. Data analytics, artificial intelligence, big data, and data science. Business companies implement data-driven models to streamline operations and make decisions based on data analytics findings.

Data Science Trends in 2023

Top data science trends in 2023 and beyond include:

The Surge in Cloud Migration Predictive Analytics is Expanding Cloud-Native Technologies will Become Essential Enhanced User Interfaces Better Control of Data

As data becomes increasingly valuable and organizations face stricter data privacy and security regulations, there will be a growing emphasis on better data control. Organizations will invest in technologies and practices that provide better data control throughout its lifecycle. This includes implementing data encryption, access controls, and data loss prevention measures.

AI as a Service

AI as a service refers to providing artificial intelligence capabilities and services through cloud-based platforms. It will continue to grow as more organizations recognize the benefits of leveraging AI without heavy upfront investments. Cloud providers and specialized AI companies will offer a wide range of AIaaS solutions, making AI accessible to businesses of all sizes.


We hope you now understand the data scientist job description. No matter where you live, positions are always available for qualified data scientists. Starting a career in data science can be rewarding, primarily if you work in the financial, retail, or e-commerce industries. Jobs are also accessible to government agencies, colleges, research centers, communication companies, transportation companies, etc. Here are some articles that will help you succeed in this career:

Frequently Asked Questions

Q1. What is the job description of a data scientist?

A. With the aid of analytical, statistical, and programming talents, data professionals gather vast amounts of data. They are in charge of using data to create solutions specifically tailored to the organization’s demands.

Q2. What are the duties and responsibilities of a Data Scientist?

A. The roles and duties of a data scientist include gathering data from various sources, using machine learning tools to organize the data, processing, cleaning, and validating the data, looking for information and patterns in the data, developing prediction systems, clearly presenting the data, and making recommendations for strategies and solutions.

Q3. What are the skills required for a data scientist?

A. Programming, organizational, communication, mathematics, data analysis, problem-solving, and analytical abilities are just a few skills needed to become a data scientist.

Q4. What qualifications does a data scientist require?

A. A bachelor’s degree is required, but most data scientists hold master’s or doctoral degrees in statistics, mathematics, or data science. Additionally, they enroll in online classes to obtain specialized abilities and equipment that facilitate their work.


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