Overview
- Python has joined the long-running R vs SAS debate
- Each of R, SAS and Python have their pros and cons and can be compared with criteria such as cost, the work setting and support for different machine learning algorithms.
- You can also choose any of the three tools depending on what stage of your data science career you are in.
Note: This article was originally published on 27 March 2014 and updated the 12 September 2017
Introduction
We love comparisons!
Samsung vs. Apple vs. HTC on smartphones; iOS vs. Android vs. Windows on mobile operating systems to compare candidates for upcoming elections or select captain for the world cup team, comparisons and discussions enrich us in our lives. If you love discussions, all you need to do is ask a relevant question in the middle of a passionate community and then watch it explode. The beauty of the process is that everyone in the room leaves as a more informed person..
I am provoking something similar here. SAS vs. R has probably been the biggest debate Data science the industry could have witnessed. Python is one of the fastest growing languages right now and has come a long way since its inception. The reason I start this discussion is not to watch it explode. (that would be fun too). I know we will all benefit from the discussion.
This has also been one of the most frequently asked questions on this blog. I thought I would discuss it with all my readers and visitors!!
Hasn't much already been said on this subject?
Probably yes! But I still feel the need to discuss it for the following reasons:
- the Data science the industry is very dynamic. Any comparison that has been made makes 2 years might no longer be relevant.
- Traditionally Piton has been left out of the comparison. I think now it's more than just a worthy consideration.
- While I will discuss global trends on languages, I will add specific information regarding the industry analyticsAnalytics refers to the process of collecting, Measure and analyze data to gain valuable insights that facilitate decision-making. In various fields, like business, Health and sport, Analytics Can Identify Patterns and Trends, Optimize processes and improve results. The use of advanced tools and statistical techniques is essential to transform data into applicable and strategic knowledge.... from India (who is at a different level of evolution)
Then, without forther delay, Let the combat begin!
Bottom
Here is a brief description about the 3 ecosystems:
- SAS: SAS has been the undisputed market leader in the business analytics space. The software offers a wide variety of statistical functions, has a good GUI (Enterprise Guide & Miner) for people to learn quickly and provides amazing technical support. But nevertheless, ends up being the most expensive option and is not always enriched with the latest statistical functions.
- R: R is the open source counterpart of SAS, traditionally used in academia and research. Due to its open source nature, the latest techniques are released quickly. There is a lot of documentation available on the Internet and it is a very profitable option.
- Piton: With origin as an open source programming language, Python usage has grown over time. Today, has sports libraries (numpy, scipy and matplotlib) and works for almost any statistical operation / build models you want to make. Since the introduction of pandas, has become very strong in structured data operations.
Comparison attributes
I will compare these languages in the following attributes:
- Availability / Cost
- Ease of learning
- Data handling capabilities
- Graphics capabilities
- Advances in the tool
- Work scenario
- Support deep learningDeep learning, A subdiscipline of artificial intelligence, relies on artificial neural networks to analyze and process large volumes of data. This technique allows machines to learn patterns and perform complex tasks, such as speech recognition and computer vision. Its ability to continuously improve as more data is provided to it makes it a key tool in various industries, from health...
- Customer service and community
I am comparing them from an analyst's point of view. Therefore, if you are looking to buy a tool for your company, you may not get a complete answer here. The following information will continue to be useful. For each attribute I give a score to each of these 3 Languages (1 – Low; 5 – Alto).
The weighting of these parametersThe "parameters" are variables or criteria that are used to define, measure or evaluate a phenomenon or system. In various fields such as statistics, Computer Science and Scientific Research, Parameters are critical to establishing norms and standards that guide data analysis and interpretation. Their proper selection and handling are crucial to obtain accurate and relevant results in any study or project.... It will vary depending on where you are in your career and your ambitions.
1. Availability / Cost
SAS is commercial software. It's expensive and still out of reach for most professionals (individually). But nevertheless, has the highest market share in Private Organizations. Therefore, until and unless you are in an organization that has invested in SAS, it may be difficult to access a. Even if, SAS has brought a university edition that is free to access, but it has some limitations. You can also use Jupyter Notebooks there!!
R & Python, Secondly, they are completely free. Here are my scores on this parameter:
SAS – 3
R – 5
Python – 5
2. Ease of learning
SAS is easy to learn and offers a simple option (PROC SQL) for people who already know SQL. Even otherwise, has a nice stable GUI in its repository. Regarding resources, tutorials are available on the websites of various universities and SAS has complete documentation. There are certifications from SAS training institutes, but again they have a cost.
R has the steepest learning curve among the 3 languages listed here. Requires you to learn and understand coding. R is a low-level programming language and, Thus, simple procedures may require longer codes.
Python is known for its simplicity in the world of programming. This also remains valid for data analysis. While there are no generalized GUI interfaces as of now, i hope python laptops become more and more common. They provide amazing features for documenting and sharing.
SAS – 4.5
R – 2,5
Python – 3.5
3. Data handling capabilities
This used to be an advantage for SAS until some time ago. R calculates everything in memory (RAM) Y, Thus, the calculations were limited by the amount of RAM in the machines 32 bits. This is not the case. All three languages have good data handling capabilities and options for parallel calculations.. I think this is no longer a big differentiation. All of them have also brought Hadoop and Spark integrations, and also support Cloudera and Apache PigThe Pig, a domesticated mammal of the Suidae family, It is known for its versatility in agriculture and food production. Native to Asia, Its breeding has spread all over the world. Pigs are omnivores and have a high capacity to adapt to various habitats. What's more, play an important role in the economy, Providing meat, leather and other derived products. Their intelligence and social behavior are also ....
SAS – 4
R – 4
Python – 4
4. Graphics capabilities
SAS has decent functional graphics capabilities. But nevertheless, it's just functional. Any customization in the charts is difficult and requires you to understand the complexities of the SAS Graph package.
R has very advanced graphical capabilities along with Python. There are numerous packages that give you advanced graphics capabilities.
With the introduction of Plotly in both languages now and with Python that Seaborn has, making custom graphics has never been easier.
SAS – 3
R – 4.5
Python – 4.5
5. Advances in the tool
The 3 ecosystems have all the basic and most necessary functions available. This feature only matters if you are working on the latest technologies and algorithms.
Due to its open nature, R & Python gets the latest functions quickly. SAS, Secondly, updates its capabilities on new version releases. Since R has been widely used in academia in the past, the development of new techniques is fast.
Having said this, SAS Releases Updates in a Controlled Environment, so they are well proven. R & Python, Secondly, has an open contribution and there are chances of errors in the latest developments.
SAS – 4
R – 4.5
Python – 4.5
6. Work scenario
Worldwide, SAS remains the market leader in available corporate jobs. Most large organizations still work at SAS. R / Python, Secondly, they are better options for startups and companies looking for profitability. What's more, it has been reported that the number of jobs in R / Python has increased in recent years. Here's a trend widely posted on the internet, showing the trend of R and SAS works. Python jobs for data analysis will trend similar to or higher than R jobs:
The graph below shows R in blue and SAS in orange.
It is, Secondly, now shows R in blue and Python in orange.
In general, the language-based marketplace can be represented as such:
SAS – 4
R – 4.5
Python – 4.5
7. Customer service and community
R and Python have the largest online communities, but they don't have customer service support. Then, if you have problems, he is alone. But nevertheless, you will receive a lot of help.
SAS, Secondly, has a dedicated customer service together with the community. Therefore, if you have installation problems or any other technical challenge, you can communicate with them.
SAS – 4
R – 3,5
Python – 3.5
8. Deep learning support
Deep learning in SAS is still in its infancy and there is a lot to work on.
Secondly, Python has made great strides in the field and has numerous packages like Tensorflow and Keras.
R has recently added support for those packages, along with some basics too. The kerasR and keras packages in R act as an interface to the original Python package, Hard.
SAS – 2
Python – 4.5
R – 3
Other factors:
Below are some more noteworthy points:
- Python is widely used in web development. Then, if you are in an online business, using Python for web development and analytics can provide synergies
- SAS used to have a huge advantage in end-to-end infrastructure deployment (visual analysis, data warehouse, data quality, reports and analysis), that has been mitigated with integration / R support on platforms like SAP HANA and Tableau. Still far from seamless integration like SAS, but the journey has begun.
Conclution
We see the market leaning slightly towards Python in the current scenario. It will be premature to bet on what will prevail, given the dynamic nature of the industry. Depending on your circumstances (Professional stage, finance, etc.), you can add your own weights and think about what might be right for you. Then, some specific scenarios are shown:
- If you are entering the analytics industry (specifically in India), I would recommend learning SAS as your first language. It is easy to learn and has the highest participation in the labor market.
- If you are someone who has already spent time in the industry, you should try to diversify your experience to learn a new tool.
- For industry experts and professionals, people should know at least 2 of these. That would add a lot of flexibility for the future and open up new opportunities..
- If you are in a start-up company / autonomous, R / Python is more useful.
Strategically, corporate configurations requiring more hands-on assistance and training choose SAS as an option.
Researchers and statisticians choose R as an alternative because it helps in heavy calculations. As they say, R was meant to get the job done and not make your computer easier.
Python has been the obvious choice for today's startups due to its lightweight nature and growing community.. It is also the best choice for deep learning.
Here is the final scorecard:
These are my views on this comparison. Now, it's your turn to share your views through the comments below.
