8 Differences Between SAS and Python

Introduction

 

Data Science has been one of the most promising careers for talented individuals who have a knack for technology. Data scientists today have advanced way more than the traditional skills of analyzing data, data mining, and programming skills. Data professionals need to understand the flexibility and capability of the subject and master the full spectrum of data science. 

 

Most of the data we receive from the world is unstructured and requires mining in order to be interpreted. For a very long time, professionals have been debating on the efficiency of SAS vs R as data science tools. Recently Python has also been added to the list. Python is one of the fastest-growing languages since its establishment. Each of these languages has its own pros and cons that need to be taken into consideration. 

 

In this article, we will discuss the difference between SAS vs Python in relevance to attributes like availability, ease of learning, graphical capabilities, data management capabilities, job opportunities, customer service, etc.

 

What is SAS?

 

Statistical Analytics System (SAS) is a software tool used for statistical data analysis and has been an undisputed market leader for many years. The program features a large number of statistical capabilities, a user-friendly interface, and excellent technical support and is suitable for complex operations. 

 

SAS is not pocket-friendly, so it is impractical for individuals and small businesses to use it. It is only used by large corporations like Nestle, Volvo, Barclays, etc. The operations that cannot be done using SAS are data visualization, advanced analytics, and machine learning methods.

 

What is Python?

 

Python is an open-source programming language widely used for web development, software development, and data science. It is an interactive high-level Object-oriented programming language known for its simplicity and readability due to its clear syntax. 

 

There are many libraries provided by Python that allow its user to work in the fields of data transformation, data filtering, data wrangling, machine learning, predictive analysis, etc. It now includes libraries (NumPy, scipy, and matplotlib) and methods for practically any statistical operation or model construction. 

 

Since their inception, pandas have grown in strength in structured data processing. It is used by many huge companies like Google, Quora, Reddit, etc. 

 

SAS vs Python

 

1. Cost-effectiveness

 

SAS is commercial software that is used by companies due to its extremely high rates. It is beyond the reach of individuals or companies who are just starting out. SAS holds the highest market share in private organizations. SAS provides a free university version for students with some limitations.

 

Python is an open-source programming language that is absolutely free to download and use. Many companies prefer to use Python over SAS due to its transparent nature in all functionalities and cost-effectiveness. 

 

 

2. Learning Ease

 

SAS is very easy to learn and use, as it has a better and more stable Graphic User Interface(GUI) in comparison to Python. It is important for a user to have knowledge of SQL in order to work in SAS. There are also many resources available on many websites with tutorials for SAS.

 

Python is a very simple and versatile programming language that is very easy to learn and understand. It is beginner-friendly in the field of programming as well as data analysis. There are no popular GUIs as of now. The learning curve of Python is a little more difficult than SAS.

 

 

3. Data Handling

 

SAS can handle data very smoothly and stably. Data is increasing in size and stability every day and SAS is able to store and organize huge amounts of data efficiently.

 

Python is also very effective when it comes to data handling. It has libraries like Panda and NumPy which make data handling very easy. It is parallel to SAS and the user can choose any of both in this relevance.

 

 

4. Graphical Capabilities

 

The functional graphical capabilities of SAS are adequate. It has its limitations when it comes to any sort of customization. In order to customize the graphical capabilities, one must have a good grasp of the graph package. 

 

Python is a clear winner when it comes to graphical capabilities. With the help of graphical packages like VisPy and Matplotlib, it is easier to create custom graphs easily. 

 

(Suggested blog: Python interview questions for data science)

 

 

5. Job opportunities

 

SAS is the leading market language globally and is widely used in corporate setups and larger companies as it is more professional and easier to use. There are many job openings all over the market for SAS.

 

Python is an open-source free language, hence it is widely used by learning individuals and startups or small businesses. Recently, in the last few years, there has been a spike in the job opportunities for python which has given huge competition to the market of SAS. 

 

(Similar read: Julia vs Python)

 

 

6. Advancement of tools

 

Although both SAS and python provide all the basic essential functions that one might require to work in Data Science, SAS is a little more advanced in the services provided. As it is licensed software and releases its updates in a controlled environment, all the features are well tested. So it is less prone to errors.

 

Python, due to its open nature, provides updates faster than SAS. Thus the user will have access to the latest features and techniques earlier. But, since it is a free language, there are more chances of errors and bugs in the latest developments.

 

 

7. Customer Service

 

SAS is a licensed and paid software, so the user is paying for the services they want to use. SAS provides great customer service and technical support. The community is also very interactive and helpful. So, if someone gets stuck during installation or any other processes, they will get immediate technical help from the team on reaching out.

 

(Must read: NLTK python tutorial)

 

Python, on the other hand, does not provide any sort of technical assistance and there is no support team to reach out to if one gets any technical difficulties. But, it is worth mentioning that due to its popularity, Python has a huge community and there are a lot of resources available on the internet that provide information about it. So one can still get some external assistance on doing proper research.

 

 

8. Deep Learning

 

SAS is in the development phase of deep learning as it has just introduced deep learning and there is a lot to work on. 

 

Python has progressed a lot in the field of deep learning. It has many packages like Tensorflow and Keras that have made the process easier for the users. 

 

Conclusion

 

For beginners or advanced learners who are interested in the field of data science, it is very important to take the correct approach. SAS and Python are both effective ecosystems when it comes to basic features. But, beyond that, there are some advantages and disadvantages of both. 

 

There is a cut-throat competition going on between SAS and Python in the market and the field of data analytics and the search for the best tool is repetitive and never-ending. The best-suited tool for an individual would depend on his/her requirements. In this article, we have discussed a few factors that differentiate the two and make them suitable for different setups.