Why Data Scientists Use Python: Pros, Cons & Alternatives

Python is a programming language used by beginners and experts alike. It allows people to create all sorts of projects without dealing with too many technical terms, though it can get quite complicated if you want it to be. That being said, there are a few popular alternatives to it, so let’s review why you should try Python and if it’s right for you.

Data scientists use Python because it’s easy to learn, and has a variety of packages, and it’s very shareable. The community makes Python a top choice, too. However, Python is a bit slower and not as good for mobile apps as few other programs. Alternatives include R, Java, PHP, and more.

Throughout this article, you’ll also learn the following info about why data scientists use Python:

  • Numerous benefits of Python and why it’s a top choice
  • Potential deal-breakers for programmers considering Python
  • Alternative programs to get similar results

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

11 Important Benefits of Using Python That You Must Know

Python rules the programming world for many users. It’s incredibly common, which means it’s taught in online and in-person courses. Data scientists and many other programmers worldwide rely on Python’s technology daily. From its versatility and usability to the thriving community and easy edits, we’ll cover all of Python’s benefits today.

Python Is Easy To Learn

There’s a big reason that coding academies and data science college courses tackle Python from day one; It’s one of the easiest programming languages around. Coding and programming are challenging, so why complicate them? Python takes out the unnecessary jargon that becomes useful later, but clarity makes it a simple program for most people.

According to ZeoLearn, coding language often steers away potential programmers. It’s a bit intimidating since it’s quite literally a brand-new way of working, reading, and operating. Fortunately, Python reduces clutter and brings comfort to the table. You can learn Python programming at almost any age.

Everything in Python is straightforward. Many other coding programs require special codes or phrases for every task, but Python lets you edit with your natural language (English, Spanish, German, etc.). It’s also simplified and explained by the community, as you’ll learn later in the article.

If you’re on the fence about coding, start with Python and see if you can grasp it. You might be surprised about how easy it is!

It’s Free for Everyone

Data science and other jobs that use programming and coding languages are lucrative. You can make quite a bit of money. Data Science Degree claims that data scientists can earn between $95,000 to $195,000. Training for such a high-paying job might sound expensive, but Python is as free as it gets.

While you might have to take online courses or college classes to earn your degree, you can practice whenever you want. A basic laptop can tolerate and run Python (though it’s a bit tasking on memory and data, as you’ll see in the ‘downside’ section). If you want to learn the ropes, you can do it wherever you have internet access.

There are plenty of open scripts and free programming languages. The feature that sets Python apart is that it’s perhaps the most widely used of all. In other words, you don’t have to spend a dime to get into the field. Teach yourself, watch YouTube videos, or get a short online course to use Python wherever and whenever you’d like.

There Are Several Add-Ons

You might think that a basic, free program wouldn’t be updated, but you’d be wrong! Python has add-ons and upgrades offered by the community all the time. SciPy, Panda, NumPy, and many other additions expand your coding horizons for free. It’s capable of working with all of these programs because users submit them regularly.

Python is such an extensive coding language that you could spend months learning every corner. When you’re ready to improve your skills, you can check out the add-ons. Don’t overwhelm yourself by downloading them right away, though. You shouldn’t get in over your head before you’ve remembered the basics.

It’s very inspiring and encouraging knowing that your path to data science is paved with freebies. Most careers require heavy investments to get the big bucks, but coding isn’t like other jobs. Python has endless benefits with very few downsides. It’s hard to pass up a free handout to the biggest competitor in the industry.

It’s Upgraded Occasionally

Community members upgrade Python to prevent it from going out of touch. Understandably, a free program might lose its grip on modern coding, especially since new coding languages are improved regularly. However, members and creators ensure that Python won’t fall behind the pack.

Much like the previously mentioned add-ons, these upgrades come with the package. You don’t have to spend money to reap the benefits of hard-working people who want to improve Python. However, it’s crucial that you pay attention to the updates so you don’t fall behind everyone else who’s reading everything that comes out.

Here’s a list of improvements that Python gets:

  • Speed updates prevent your computer from lagging behind.
  • Memory reduction changes stop massive data consumption, though it’s a work in progress.
  • Compatibility is at the forefront of users’ concerns. Python is known to switch to other languages easily.
  • More add-ons are designed to upgrade the user experience.

Viewing Data Is Smooth and Easy

Data Flare Training suggests that Python’s best feature is readability. Rather than having to learn various codes and what they mean, Python uses natural language (English, for the most part) to clarify each line. Most people who aren’t data scientists can learn how to read Python programs within a day.

Transitioning between coding lines is simple as well. There’s no need for brackets and other marks, though they become useful in add-ons and Python’s experienced pages. You can go at your own pace to view every page without feeling overwhelmed. As you might imagine, this benefit doubles when applying for jobs.

Half of the coding battle is learning how to read and interpret programming languages. Most companies who hire freelance data scientists don’t know how to do it, which is why they’re hiring you. Once you’ve produced the product with Python, you can explain every detail without losing their attention. Furthermore, you can translate the code to tables, graphs, and other useful formats.

Python Is Versatile

Python might be an old coding language, but it’s far from dying. It continues to swing with the punches of other languages, including Java, R, and leading programs worldwide. Python allows you to write your code in C and transfer it to C++ or vice versa. You won’t be limited, which broadens your career horizons.

To make it better, many programs accept Python code. Rather than Python being prepared for integration, these companies have software prepared for an immediate transfer. In layman’s terms, you can write a file on Python and open it with various programs without losing progress or ruining the format.

Python is versatile down to its core. You can download and edit the source file, which is relatively rare in the industry. Free, open-source coding is becoming the norm, but Python never strayed from the path. Whether you’re opening files and editing via Python or another language, you can rely on this program.

You Can Work on a Wide Range of Projects

One of Python’s favorite traits is that it works with many different projects. If you’re a freelance data scientist or want to adjust your visual settings for your job, Python can get it done. Let’s explore three examples below.

  • Analytics India Mag points out that Python lets you switch to a table format with one click. You don’t have to code for hours to create a unique format; They provide basic tools to start the conversion process immediately.
  • If you want less structure, you can switch to a web diagram. Show brainstorming ideas or connections with visual representations. It’s the most basic way to learn graphic changes without detailed programming or complicated coding.
  • Some clients and employers prefer specific Python requirements. You can learn these features quickly to expand your capabilities and work with more people. Python promotes networking, so you don’t have to stress about feeling limited or held back.

It Boasts More Job Opportunities

As you’ve probably gathered from previous examples, Python opens career doors, unlike most other programming languages. R and Java are heavily used in the industry, but Python remains a top competitor with unparalleled ease-of-learning. If you want to become a data scientist, almost anyone will point you toward Python.

Another reason that Python is suitable for job opportunities is that it teaches you the essentials of coding, opening doors for other programs. Once you understand Python, you can move onto other programs to increase your chances. In some cases, companies will provide a pay raise for those who know multiple coding languages.

Python can become a profitable language without costing anything. Freelancers can head to numerous online or in-person job interviewers knowing how to create tables, webs, and other methods to observe analytical data. In short, if you want to succeed as a data scientist, web developer, database administrator, computer systems analyst, or another data career, put Python on your to-do list.

Beginners Can Read and Use It

Even if a client, friend, family member, boss, or coworker doesn’t read coding languages, you can show them Python’s basics. You’ve already read how to make tables, graphs, and other tools, so why not use them to make a readable project? You can create statistical analysis about virtually anything known to humankind.

Towards Data Science shows us that Python has been around since the late 1980s, so it’s had quite a bit of time to simplify everything. They warn that too much flexibility leaves room for detrimental creativity, though. In other words, programmers get ahead of themselves and create unreadable content. Fortunately, it can be easily corrected.

There are hundreds of libraries to choose from, so you’ll have no problem trying to make a readable, simplified project. If you want to make it complicated for readers who understand coding languages, Python has the tools you need for that too. Freedom rarely comes without a price in the world of coding, but Python is an ideal example.

It’s Easier To Debug Python

Since Python is operated by countless community members, people work quickly to fix bugs and errors. If anything goes wrong, hundreds of worker bees tackle the problem as quickly as possible. Each line is coded and saved individually, which might not sound important, but it makes debugging much more manageable.

When a group of lines is blocked with one another, they’re edited in bulk. This process causes every bug to penetrate the lines, causing all sorts of frustrating issues. If you mess up while coding, you’ll have to delete a chunk or sift through your coding to figure out what went wrong and how you can fix it.

That’s not the case with Python. In this program, you can edit each line by itself. Locate the error, delete it, and get back to the project. It’s as easy as it gets. You don’t have to stress about day-long bugs unless you made hundreds of errors, which can be corrected in due time.

Python’s Community Is Second to None

Why is a community not only beneficial but necessary? Python’s community shows us why programs worldwide can reap the rewards of asking questions and helping others. If you’re a beginner, you can ask anyone for help. You won’t be left to figure everything out for yourself. This feature invited future data scientists to give it a chance.

Let’s review the three best benefits of Python’s community:

  1. Python’s community can share project files. If you want to know what an experienced programmer can do, head to the Python community and research their projects. You’ll be impressed and inspired to start projects of your own.
  1. They’re there to help with any questions you have. If you’re confused, lost, or want to know the potential, then you’re in luck. The community wants to answer anything and everything. Programming is a niche career and/or hobby, so data scientists prefer Python for its community.
  1. You can use anyone’s edited source files and projects. If you want a base starting platform or want to tinker with another file, the community offers plenty of free editable projects. It’s an excellent way for beginner and intermediate programmers to learn more about Python.

4 Important Downsides of Using Python That You Must Consider

Unfortunately, Python isn’t perfect. It’s such a large programming language that people find it hard to update. While it’s considered modern and upgraded, there are a few challenging situations. Below, we’ll cover the setbacks that Python has to deal with and comparisons to similar programs that might do it a bit better.

Python Is Slow

Net Informations explains that Python is much slower than many coding languages. It’s easy to understand why a multi-decade old, free, open-source programming language is a bit slower, though. Many new-age languages were designed with better technology that’s not as taxing on the computer’s memory.

When you’re performing simple tasks, you won’t notice a difference. Major issues arise when you’re converting tables and graphs, transferring to a different coding language, and so on. There are undoubtedly slower programming languages on the market, but Python isn’t one of the quickest. It lags a bit behind the pack but makes up for it in other areas.

Many sources claim speed isn’t much of an issue since it’s rare to work on such a time-consuming project. Most clients prefer detailed submissions rather than instant results, so Python remains an excellent choice for data scientists.

It’s Not Great for Mobile Development

Mobile technology didn’t come out until the last few decades, but applications weren’t developed for many more years. Since Python was made long before anyone envisioned smartphones, it can’t create apps as easily as other programming languages. Most mobile developers switch to other programs once they have the basics out of the way.

Python is an excellent starting platform, though. Many data scientists never involve themselves in mobile app development, so this downside might not be a con for you. If you’re interested in creating analytical data on a computer rather than smartphones, tablets, and smartwatches, then you can skip to the next subheading.

Unfortunately, mobile development is the future of many jobs. Video game developers must learn to incorporate apps into their database, and marketers have to know how to use apps to sell products, promote companies, and observe customer data.

It’s Not As In-Depth As Others

Python covers the basics of data science and programming, as you’ve learned throughout this article. It’s a top-notch tool for beginners, but experts might find that it lacks deep processing capabilities and coding potential. For this reason, it should be viewed as a necessary starting tool for everyone, but not the end of the line.

Another issue is that, while Python has hundreds of libraries, it typically can’t access various R libraries. R is a high-end programming language that we’ll discuss in the ‘alternatives’ section. Some data scientists get frustrated by this limitation, but it’s minor compared to the vast collection already available.

Some sources have denoted Python as primitive or behind the times. Truthfully, Python is updated to be a beginner learning language and a tool for free creation in the community. It’s not behind or lagging because it does exactly what it’s designed for.

Python Is a Memory Crusher

If you have an old-school computer or there’s not a lot of processing power, you might be in trouble. Almost anything can run Python, but you might experience loud fans, overheating, and slower performances and response times. It’s not as efficient as most programs that were created within the last couple of decades.

It’s far from the smoothest language, but there’s no denying its usefulness. Data scientists choose Python because it leads the industry in many areas. A little bit of lag or slow processing isn’t going to take it off the top of the programming mountain. Have patience when transferring or converting, and you’ll be good to go.

It’s up to you to decide if the pros outweigh the cons. If you’re interested in data science, it’s safe to assume that Python will be a significant part of your journey. Focus on the previously mentioned pros rather than the minor cons, and you’ll be off to the races. For those who want to expand their programming languages, read on.

5 Alternatives to Python That You Can’t Neglect

If you’re interested in expanding your programming languages toolkit, then you’re in the right place. It’s recommended that all data scientists learn Python eventually because it’s an integral language. However, some of the languages in this section are equally as useful and productive. Without further ado, here are five Python alternatives you must consider adding to your programming skillset:


Alongside Python, R is one of the world’s most common programming languages. Much like Python, R is an open-source, free programming language. There are many similarities between them, including massive libraries, occasional add-ons, and learning simplicity. Beginners will find R slightly complicated compared to Python, but well worth the time.

According to KDNuggets, R is designed for data analysis and visualization. In other words, it’s not as easy as Python and focuses more on data collection than development and beginner tooltips. You can perform similar functions, though.

Developed in the mid-1990s, R maintains a thriving community that’s just as helpful as Python’s. Another reason they’re alike is that R allows creating graphic tables, charts, and other methods to display data.


Java is much more complicated than R and Python. It’s a high-functioning business programming language used to design some of the top video games, applications, and websites worldwide. Once you’ve learned Python and R, you might feel more comfortable jumping up to Java.

Simple Programmer puts it best; Java has its own phrases, so you’ll have to get up to speed with its terminology before moving forward. You can’t jump into Java without programming experience. It’s far too tricky, though some users claim that it has a similar learning curve as all other programming languages.

The best way to view Java is to understand that it’s as deep and complex as it gets, but that shouldn’t be intimidating. It doesn’t use traditional spoken languages as Python and R do, so you’ll have to go slower.


PHP is designed for web development, so it’s only valuable to a small portion of data scientists. If you’re creating webpages for clients to analyze data, then it’s an incredibly useful tool. As shown by EDUCBA, PHP has been used to design various pages, including the widely known WordPress. There’s no denying its potential as a long-lasting, flexible coding language.

Another benefit of PHP is that it’s relatively easy to use. Once you understand Python and R, you can try PHP. It’s not as challenging as Java, so beginners and intermediate programmers or data scientists can find relief.

Note: Many data scientists won’t find a use for PHP. It doesn’t allow you to create complex tables without a webpage, so your clients might not need it. Again, if you’re making webpages or full websites to analyze data, it can be an integral part of your career or hobby.


Much like PHP, Ruby is only used for web development. This means that you’ll have to create data analysis for web pages rather than submitting it via other formats. However, it’s very similar to Python, so beginners will find it much easier than advanced programming languages. Knowing multiple languages increases your appeal to future employers, too.

One of Ruby’s most significant benefits is that it is cross-interpreted, which means you can view it through multiple programs and compile it elsewhere. You’ll work smarter, not harder. Ruby might not be as popular as many other coding languages, but it’s worth checking out.


Last but not least, Golang is an excellent Python alternative. Perhaps its most significant advantage is that it was developed by Google. Since Google is the internet’s top search engine and almost everything they do succeeds, you can rest assured that it’s here to stay.

Many users claim that Goland is similar to C, other than a few minor features. It’s designed for backend functionality and web page development. Still, it’s only statistically typed like Java, C++, and Scala. Speed is Golang’s great asset, which is useful for programmers of all experience levels.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


Python continues to be an industry-leading tool for programmers. It’s not going away any time soon, but why not expand your programming skills to other languages? The more you know, the more you can improve your job portfolio and work on more projects.

Here’s a quick recap of the post:

  • Python is free, easy, creative, ever-growing, and fun to use.
  • It’s not as in-depth as many other programming languages.
  • R and Java are two of Python’s biggest competitors.
  • Python is one of the most taught languages for data scientists.
  • You can take online or in-person classes to learn Python.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. Costa, C. D. (2020, August 21). Python vs(and) R for data science. Medium. https://towardsdatascience.com/python-vs-and-r-for-data-science-4a32580846a4
  2. Data science. (n.d.). Loyola University Maryland – A Jesuit, Liberal Arts University in Baltimore, MD. https://www.loyola.edu/academics/data-science/blog/2018/why-python-and-why-you-should-care
  3. DataFlair Team. (2019, December 10). Advantages and disadvantages of Python – How it is dominating the programming world – DataFlair. DataFlair. https://data-flair.training/blogs/advantages-and-disadvantages-of-python/
  4. How much is a data scientist’s salary? (2018, December 13). University of Wisconsin Data Science Degree. https://datasciencedegree.wisconsin.edu/data-science/data-scientist-salary/
  5. Importance of software skills in data science. (2020, January 17). Online Data Science Graduate Program | UVA School of Data Science. https://onlinedatasciencemasters.virginia.edu/blog/importance-of-software-skills-in-data-science/
  6. Python alternatives. (2020, October 26). EDUCBA. https://www.educba.com/python-alternatives/
  7. Python for data science. (2019, March 28). I School Online – UC Berkeley School of Information. https://ischoolonline.berkeley.edu/blog/python-data-science/
  8. R vs Python for data science: The winner is …. (n.d.). KDnuggets. https://www.kdnuggets.com/2015/05/r-vs-python-data-science.html
  9. Why should you learn Python for data science? (2020, December 2). Analytics India Magazine. https://analyticsindiamag.com/why-should-you-learn-python-for-data-science/

Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.


Daisy is the founder of DataScienceNerd.com. Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

Recent Posts