It is a well-known fact that Python, R and SAS are the most important three languages to be learnt for data analysis.
If you are a fresh blood in the data science community and are not experienced in any of the above-mentioned languages, then it makes a lot of sense to be acquainted with R, SAS or Python.
Does that sound too difficult? Do not fret, by the time you are done reading this post you will know without a doubt which language is the right one for you to take up first.
Introduction to the languages:
- R programming: R is the lingua franca for statistics. It is an open source programming language, free to access and pen to all to perform data analysis tasks.
- Python: this is a multi-purpose, open source programming language, which has become a very popular one these days in data science due to its vibrant community and immense data mining libraries.
- SAS: SAS is currently the undisputed market leader when it comes to enterprise analytics space. It provides a huge array of statistical functions; it has a good GUI for people to pick it up faster and also offers a well-backed tech support team.
You must be aspiring to start a career in data science for gaining some knowledge and to be able to transition to this field in the near future. And if so, then some research on your part is necessary to understand what you must take up as your first lesson to excel in this complex field. It will help up your chances of landing the right job. But the question here remains – whether you should take up R? Or should it be better to make SAS a priority for learning? Or should one learn Python instead?
Here are the factors that one should consider, before deciding what to learn:
Industries where the tool is used the most:
An international HR firm Burtch Works, asked about 1000 quantitative professionals about which language they prefer better – is it SAS, R or Python. The survey results, came out to be something like this:
big companies mostly prefer SAS, because they offer better customer services, this is also the reason why SAS has an advantage within the financial services sector and the marketing companies, where the budget is not a concern for selecting the tool.
On the other hand, start-ups and mid-sized firms use R and Python. Tech as well as telecom companies also require a large amounts of unstructured data to get analyzed, and hence, many data scientists associated with these sectors use machine learning techniques for which Python and R are more suitable.
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The ease of learning, and pocket pinch:
The language SAS is an expensive software used for commercial purposes and is mostly used by large corporations with massive budgets.
However, R and Python are free software, which can be used and downloaded by anyone keen on learning.
No prior knowledge in programming is required by people for learning SAS, as it has simple GUI, which is easy to use. There is the provision of parsing SQL codes, combining it with macros along with other native packages it makes learning SAS a cakewalk for those with basic knowledge of SQL.
For analyzing data in Python one will need data mining libraries like Pandas, Scipy, and Numpy. In other words, one will not code in a native Python language for data analysis. The codes one writes in these abovementioned libraries are somewhat similar to those in R. So, it is easier for people to learn Python who are already aware of R in data science. For those who already know R it is recommended that they learn the basics of Python Programming language before one starts to learn the Python data mining ecosystem.
Capabilities in data science:
SAS is very efficient at sequential data access and for database access through using SQL which is well integrated. With the drag-and-drop interface, it makes it easier for people to create better statistical models faster.
R is best known for in-memory analytics and is mainly used when the data analysis tasks need standalone servers. It is a great tool for exploring data.
Python libraries like Numpy, Pandas, Scipy and Scikit-learn allows it to be the second most popular programming language in the field of data science right behind R. One can also create a lot of beautiful graphs and charts with libraries like Seaborn and Matlplotlib.
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R and Python have a huge community support online from things like mailing list, Stackover flow and other user-contributed documentations and codes.
SAS also has an online active community which is regulated by the community managers.
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