Python vs R vs SAS- The showdown of your favorites!

We are sure you’ve heard about the three heavyweights Python, R and SAS dominating the data science industry.

Have you ever wondered why Python, R and SAS are creating all this buzz? 

They are the leading tools when it comes to making your mark as a data scientist. If you’re just starting out in this space, we recommend you read a little about these giants. Let us do this by way of a little competition, shall we?

Meet the Champions!

Python has been growing leaps and bounds over the years. It has ample libraries, is easy to use and can be read as simple English. It is free and inviting, you can easily download, change and distribute even its source code.

R has been the predominant language in this sphere for a while now. It is also available under an open-source license. Developers can write their own software and distribute them as add-on packages.

SAS has been around for the longest time. It is a 4th generation programming language created primarily to help with statistical analysis in big companies.

While SAS is deeply favored by private organizations, R has been dominant in popularity. However, Python has overtaken R and is emerging to be a force to be reckoned with.

Round 1: Open source vs. Propriety

This round judges how accessible contenders are to its users. A highly-rated programming language should be easily available to its users to help broaden its customer base.

Python                                                                            10/10

Python is an open-source programming language. It’s easily available and free to use and is rapidly gaining approval from users worldwide.

R                                                                                     10/10

R is also an open-source programming language. R competes strongly with Python when it comes to ease of use and affordability.

SAS                                                                                  6/10

Ever since it’s launch in the 1970s, SAS has been a commercial favorite.
The pricing of SAS makes it difficult for students and professionals to afford it individually.

*Brownie Points: SAS earns extra points in Round 1 for launching a University Edition which is free to use!

Round 2: Ease of learning

In this round, the languages will be judged on how easily can they be picked up by newbies.

Python                                                                            10/10

Python is easy to grasp. That’s one of the first reasons why it is getting popular by the minute. Python has been continuously applauded for its simplicity and flexibility.

R                                                                                       6/10

R is a low-level programming language which means longer codes are required for the simple procedures. Working knowledge of coding is a must if you’re opting for R.

SAS                                                                                  8/10

SAS is the easiest language to learn. You don’t even need a background in programming to rack it up. SAS can be mastered by anyone having previous experience in SQL and macros although it does need longer lines of coding than Python.

Round 3: The power of community

We will reveal in round 3, just how close-knit are the user communities. We’ll rank the languages higher if they have more support groups, discussion channels and platforms for new user interactions.

Python                                                                            10/10

Python revels in its huge sea of online communities. Users discuss, engage and even contribute towards its growth. In fact, just recently, Stack Overflow reported that Python has become the most questioned language on its forums.

R                                                                                     10/10

Much like Python, R users participate in building and contributing towards online communities. Its an added advantage for users to be able to resolve queries without having to wait for customer support.

SAS                                                                                  8/10

It’s hard to find SAS communities. As such, a lot of user queries which could be resolved in simple threads among community members can only be submitted to its customer support. We’re not denying that SAS has robust customer service in place for its users, but we do think that open forums and community engagements would make for better user experience.

Round 4: Keeping up with new trends

This round challenges the abilities of the contenders to change and adopt current and groundbreaking practices. All three languages are incredibly responsive to updates and advancements. However, due to their nature, this is how they fare in this round.

Python                                                                           8.5/10

Python being open-source receives contributions to improve its effectiveness from all users. As such, the turnaround time to include new techniques is almost zero.

R                                                                                    8.5/10

R also receives an abundant contribution from its users to keep it at a strong pace. However, these contributions, being individual have chances of error much like its counterpart, Python.

SAS                                                                                  8/10

SAS updates are possible only through launching newer versions. That said, SAS is effortless in incorporating all the latest features in competitive time.
All changes and updates offered by SAS are heavily tested and extremely reliable.

Round 5: Doing what they do best!

All 3 contenders have made their own niche. In this round, we will understand the contestant’s best capabilities and how they outshine the competition.

Python                                                                           9.5/10

Python competes successfully with the best capabilities of both SAS and R. Besides, it is the go-to option available to users for tapping into the world of machine learning. NumPy and SciPy are two great libraries of the language that users rely on for data mining and machine learning.

Another library Panda is a hot choice for its users. Panda makes any basic computation, even reading data, easier.

R                                                                                    8.5/10

In addition to the capabilities of SAS, R finds its uses in machine learning. It’s important that R can be used for visualization. Data should be explored before it is fed to machine learning algorithms.

R is favored most by academia. Academics continue to work on new algorithms and release them as R packages.

SAS                                                                                  8/10

SAS comes in handy in exploratory data analysis. SAS shines in visual analytics, statistical modeling and predictive analysis. Its strongest suits are charts, graphs and visualization.

Round 6: Which industries use them?

Python, SAS and R are finding their uses in diverse industries. While some industries have stuck with their choice of language since the very beginning, others are testing the waters with newer options.

Python                                                                              9/10

Python is used majorly in acing customer research and market analysis. It is great for web development. Right from aerospace to business and consulting services, Python is a thriving option for almost every industry.

R                                                                                       8/10

R is a preferred choice in the world of academics. It’s also used for market analysis on open source tools. Also, R is quite in demand in the pharmaceutical and clinical space.

SAS                                                                                  8/10

Like we said SAS is organization friendly. Banking, financial services and insurance services heavily depend on deploying SAS to their sensitive data.

Round 7: Are they deep learning-friendly?

Finally, this round gauges the compatibility and ability of the languages for their deep learning techniques. Deep learning is the undeniable future. Building and en-cashing these capabilities have given a powerful head start to Python over the other two.

Python                                                                            10/10

It is easy-to-use and to implement with advancements happening into deep learning in real-time.
You have an array of packages to choose from with Python. Tensorflow from Python is a crucial stepping stone when it comes to building neural networks.

R                                                                                      5/10

While R finds its way into machine learning, it is not the most preferred deep learning tool and has failed to make a mark in this space.

SAS                                                                                  0/10

SAS does not fare well when it comes to machine learning capabilities. The software continues to lose market share to its competitors handicapped by its lack of feasibility in the world of machine learning.

With this, we conclude the face-off between these three coding sweethearts. Let’s have a look at the total tally of our contestants-

Drum roll please…….

And with a grand total of 67, the winner is Python!

TIOBE Index voted Python as the programming language of 2018. Stack Overflow’s annual developer survey, 2019 ranks it as the most wanted language of the year.

Although it is hard to pit three trending data science tools against each other, Python won our hearts in this nail biter and we’re not surprised that it is the fastest-growing programming language today!

Do you agree with our ranking? Can you think of any more rounds we could’ve included in this face-off? Do let us know your thoughts in the comment section!