Large corporations and multinationals have often ridden on data analysis to know their customers, understand their needs, and tailor products and services that delight them (customers) enough to lock them in loyalty for a long time. For this, they need data scientists who are well versed, at the least, with programming languages and who know well how to use data analytics tools like Hadoop, SQL, Scala, SAS, R, and Python programming to draw insight from data.
For would-be data scientists, it has been said that training, say, taking a Python data science course, and earning a credential is the way to go. First things first, a background in statistics and mathematics is a requirement. On the other hand, however, beginners may be stuck at which language they should learn in their quest to become data scientists.
In this article, we analyze two data science tools; SAS and Python to see which one beginner data scientists should learn.
SAS for data science
SAS stands for Statistical Analytics System. It is commercial software developed to handle complex analytics, statistical modeling, and other statistical functions and is mostly used by large corporations especially in the banking, healthcare, and insurance sectors. SAS is not open-source, not free, and not cheap either which is the greatest deterrent for small businesses and start-ups who would wish to adopt it.
Although it is slowly upgrading to include AI and machine learning tools, these are yet to be established compared to others like R and Python. Other products that SAS has started offering are customer intelligence, security intelligence, risk management, and big data capabilities.
SAS is made up of more than 200 components including:
- Base SAS programming language for data management and analytics
- SAS/INSIGHT used in data mining
- SAS.STAT used in statistical analysis
- SAS EBI used in business intelligence applications
SAS features
- Not open-source hence not free
- Offers high stability and data security
- SAS offers excellent customer support as well as technical support and software maintenance services throughout.
- Cloud compatible through SAS Viya which allows programmers to process commands in the cloud.
- Has integrated AI and ML functionalities although these are not established
Is SAS good for beginners?
Being that SAS was developed primarily for industrial and commercial purposes, it may not be the best tool for beginners or solo data scientists to learn unless their ultimate goal is to end up working in an industrial setting or have additional skills to be more competitive in the job market.
For those who wish to learn SAS programming for free, there is a free version of SAS known as SAS University purely for academic purposes and not for commercial applications.
Python for data science
Python is an open-source object-oriented programming language that has grown remarkably popular among data scientists and software developers. Python is preferred because it supports structured, object-oriented, and functional programming among others, and also integrates well with existing infrastructure.
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Python flaunts a large number of libraries to support a wide range of data manipulation functions including data wrangling, data filtering, predictive analytics, visualization, and machine learning.
Popular python libraries include:
- NumPy used for numerical computing
- Pandas for data manipulation
- Matplotlib used for data visualization
- Scikit Learn for machine learning
- Tensorflow for machine learning operations and numerical computing
Python features
- Easy-to-use and easy-to-learn syntax for beginners with basic coding knowledge
- A large number of libraries including those of AI and ML
- It is an interpreted language
- Is supported by many operating systems including Windows, Linux, and Mac platforms
- Fast and highly scalable
- Comes with a range of visualization, data analysis, and data manipulation functions
Is Python good for beginners?
Save for the fact that Python is the most popular language among data scientists and developers, it is quite easy to learn, read, and use. Python features simple readable syntax which makes it favorable for beginners as they do not have to go into much coding. This gives them time to focus more on learning other data science functions.
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Besides, Python has a vibrant and very supportive community and plenty of learning resources and tutorials online.
SAS vs Python for data science beginners
Whether, as a beginner, you want to learn SAS or Python first is a personal choice that depends on your needs. However, here are a few points to consider when deciding which one to go for.
1. Learning curve
For people who are already familiar with SQL, learning the base SAS language is easy thanks to its interactive GUI. Before writing code, an individual must first familiarize him/herself with the SAS GUI interface. It is not necessary to have a programming background to learn SAS.
Python, on the other hand, is also easy to learn thanks to its simple syntax. However, in place of an interactive GUI like in SAS, Python has an IPython notebook that allows learners to share code.
Between the two, SAS is the easiest to learn.
2. Libraries and support tools
Python features several libraries for web development, application development, data science and visualization, desktop GUI programming, as well as machine learning and AI frameworks. For this reason, Python is a good choice for manipulating and visualizing large data sets. Again, SAS offers a range of inbuilt business intelligence, data warehousing, analytical, and statistical tools which makes it a great tool for data manipulation, particularly in independent servers or machines. However, while SAS can be used to plot graphs quite well, it is not as good as Python in terms of data visualization as it cannot create custom graphs. Again, it lacks advanced AI and machine learning capabilities.
Both Python and SAS can handle large sets of data effectively.
3. Data science capabilities
In the data science field, Python language excels particularly in analyzing unstructured data. Libraries such as Scikit Learn, Pandas, and NumPy, and Matplotlib for visualization make it the go-to option for beginners who intend to pursue a career in data science.
SAS also features functional data science capabilities, for instance, for sequential data analysis as well as database access and management through the integrated SQL database system.
4. Cost-effectiveness
SAS is closed-source software and one of the most expensive, hence it is often not an option for small businesses and start-ups.
Python is open-source and can be downloaded for free by anyone including beginners who need it for learning purposes.
5. Customer service and community support
SAS has a dedicated customer support to assist its customers with all matters related to SAS software from installation, operation, to maintenance. However, it lacks a wide community network.
Python is open-source. As such, it has a vibrant supportive community that keeps growing. Users float their questions and issues in the community forums for others to answer.
6. Updates
SAS only gets updated when a new version is rolled out.
Python constantly gets updated with new features from the community and so gets the latest updates faster than SAS.
7. Market demand
SAS dominated the market for a long time before and especially the corporate market. However, the market is gradually bending towards open-source technologies which is the reason why Python has grown remarkably in popularity.
Secondly, Python is a versatile tool that is not limited only to data analytics and software development functionalities which creates a wider market for people with Python programming skills.
8. Preference by industry
SAS is mostly adopted by big corporations whose concern is high stability, good security, and dedicated customer support and not the cost of the application.
Python is preferred by start-ups, small and medium-sized tech companies because it offers powerful features for manipulating large unstructured data sets at no cost. It also has AI and machine learning capabilities.
Conclusion
The industry is shifting towards open-source technology. Secondly, tools like Python are versatile and most preferred for data science. SAS is more suited for statistical analysis and business intelligence. For this reason, a beginner interested in pursuing data science would be more advantaged learning Python. However, adding SAS to their skill set would give beginners more opportunities.