Python VS. SAS Vs. R – Which Is Better? – Starweaver
Python VS. SAS Vs. R - Which Is Better?

Python VS. SAS Vs. R - Which Is Better?

For many years, data scientists have debated the merits of using Python, SAS, and R for data analysis. But this discussion has never reached its conclusion as there’s heavy competition between these three analytics tools and deciding which is the best tool to use for the job.

As a data analyst, it’s always important to know all the strengths and weaknesses of each tool you intend to use. So, not wanting to assume any use cases, we thought it best to present you with a detailed comparison between all three.
With that, we hope you will benefit from this discussion.

Description

SAS:

SAS is an integrated system of software solutions and a leader in the data analytics field. This program also has a lot of features that make it the most desirable option for data analytics. SAS is one of the most effective technical support and will help you achieve the following tasks:

• Applications development

• Operations research and project management

• Business forecasting and decision support

• Mathematical and statistical analysis

• Report writing and graphical design

• Data entry, management, and retrieval

SAS is used by some of the most reputbutable companies like Barclays, HSBC, BNB Paribas, Volvo, and Nestle.

R:

R is another highly reputed programming language used by some top-rated companies like Bing, Bank of America, Foursquare, and Uber. It’s one of the leading analytics tools for statistical computing and graphics. Since it was developed in 1995 by Robert Gentleman and Ross Ihaka, R has evolved to become one of the most revered analytics tools available.

This programming language offers a wide range of graphical and statistical techniques for business. It’s also open source, making it highly extensible for users. Besides statistics and graphical techniques, R also does the following:

• Machine learning

• Visualize data

• Manipulates strings

• Easily manipulates packages

• Works with both regular and irregular time series

Python:

Python is an object-oriented programming language that was created by Guido Van Rossum in 1991. This analytics tool is simple and easy to learn too. It should help you complete your projects more quickly and effectively. Since its development, Python has become more popular due to its simplicity and ease of use. Some of the famous companies that use Python include Quora, Google, Reddit, and ABD-AMRO.

Reasons for Comparison

Technology and industries are growing dynamically. And with it, the data analytics field. Therefore, if you are new to this field, it might be best to learn which analytics tool is best for your projects and how to use them. You must also prepare yourself for any challenges and frustrations that come with using these programs, especially due to upgrades.

The most recent comparison of these languages is a worthy consideration due to regular updates and upgrades of these tools. Therefore, any comparisons done a few years back aren’t considered relevant and may not help you choose the best option among the three.

There are several factors used to compares these languages. Of course, you may not use a tool based on these factors, but they should at least shine some light on which one of the three suits your needs.

  1. Cost

    SAS is straight up the most expensive software on the list. You’ll need to invest millions of dollars to get a SAS license. As a result, it’s often used only by large corporations and upscale enterprises. That’s why only a few companies use SAS. As a data analyst, you can benefit a great deal when you join a company that uses SAS. For you get to use some of the best analytics features for the most convenience, making it well worth the investment.

In contrast, Python and R are both open source software that you can download and use at any time. This makes both programs more preferable and more widely used compared to SAS.

  1. Accessibility

    As a closed source software, you will be required to buy new products to use advanced SAS features. This tool doesn’t offer you the option to download any features or use them instantly. This tool also has strict licensing limitations that some corporations may not find feasible.

On the other hand, Python and R allow users to access and upgrade their software to advanced features like multicore packages, parallel processing, etc. These features should help you to perform repetitive or concurrent operations with ease.

  1. Ease of Learning

    One of the upsides of using SAS is that it’s simple and easy to learn. SAS also provides an easy option for data analysts who have basic knowledge in SQL. SAS has one of the best stable GUI interfaces in its repository. It also has several tutorials on its website with comprehensive documentation. SAS also has certifications from training institutes, but all these come at a cost.

Python is best known for its simplicity features in the programming world. It also has one of the simplest and easy to learn data analysis options as well. While it may not have a widespread GUI interface right now, it’s possible that this software will become more mainstream in the coming future. Python provides some amazing features for sharing and documentation.

Of the three languages, R has the steepest learning curve. You must know how to code to learn and understand R. As a low-level programming language, you may be required to use longer codes even for simple procedures.

  1. Graphical Capabilities

    R easily ranks on top of this list for having the best graphical capabilities among the three tools. It has advanced packages for graphical capabilities, making it the preferred option for most companies and data analytics that rely heavily on graphics.

    SAS, on the other hand, only uses basic graphical capabilities. Therefore, customization on various plots isn’t always simple and may require in-depth knowledge on how to use the SAS graph package.

With Python, you can use native libraries like matplotlib or derived languages that allow you to call for other R functions.

  1. Customer Support and Community

    SAS takes the lead when it comes to customer support and service. It has a dedicated customer support and service team and a community to always address your issues. Therefore, you can rest assured that the customer support will address any technical issues you may have promptly. You can also contact the customer support center directly for the best hands-on assistance.

In contrast, R and Python don’t have a customer support center. So, if you have any trouble navigating through the site or using any feature, you’ll be alone. However, both R and Python have a comprehensive online community where you can address your issues. The only downside is that your issues may not be addressed instantly. Their customer support isn’t anywhere near the level of SAS.

  1. Job Scenario

    Python and R both have more job openings, which are also expected to increase in the future. Python and R are used more by companies looking for cost efficiency without having to sacrifice much on their productivity. This makes them the best option for startup companies.

On the other hand, SAS is used widely by large organizations and corporate companies that spend top dollar for all the services available.

Globally, SAS is a market leader for most available corporate jobs and big organizations. Alternatively, R and Python are better, more affordable options for startups and corporations looking for cost-efficiency.

  1. Support for Visualization

    Visualization plays a fundamental part in data science and analysis. The main visualization platform that SAS uses is called SAS Visual Analysis. However, most startup businesses and small corporations consider this a little too costly to use. This makes SAS Visual Analysis exclusive to large corporations that don’t feel the service’s financial pinch.

Python and R have a lot of visualization tools that companies can use for free. These programs also don’t require you to sign a contract or pay for their visualization tools like in SAS.

  1. Agility

    Thousands of contributors across the globe support both Python and R. Therefore, if there are any developments or upgradations available to these languages, customers can access and use them with ease.

On the other hand, SAS products are only accessible to the SAS Institute Incorporated. Also, only SAS developers can produce new features on the platform. Usually, this takes a lot of time, and you can complete your project before there are any new updates for SAS features.

  1. Advancements in Tools

    Generally, all these languages have basic required functions. However, the latest technologies and other functions also matter a lot, especially if your project expects it.

As open source programs, Python and R receive numerous enhanced features, updates, and technologies. The development of new technologies is also very fast in R, making it a desired option for most companies.

In contrast, SAS takes a lot of time to update to the latest features and capabilities. This is mainly because it works in a controlled environment. However, all the features SAS rolls out are always completely tested, which makes the chances of errors very minimal.

The Bottom Line

From a more in-depth point of view, SAS will definitely satisfy all your data analytics needs. However, it’s not always the most suitable option in the long run. Most companies are now moving towards open source programming languages that are simpler and easier to use, which puts R and Python top of the list.

Due to its restrictiveness and closed tools, SAS isn’t always the most preferred option for most companies these days. However, the bottom line is that there’s no obvious winner among the three options since each programming tool has its own advantages and disadvantages. And their strengths and differences are what have helped them survive for this long. Whichever programming tool is right for you depends more on your needs for the project you are undertaking at that time.

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