Python vs. SAS for Clinical Research
Basic introduction to both languages and start of article series to provide information of alternative in data science for clinical research outside SAS.
Basic introduction to both languages and start of article series to provide information of alternative in data science for clinical research outside SAS.
When it comes to data analytic tools and data science profession major question would be “Which platform/language should I choose?”. Amongst most popular (but not the only) are SAS and Python. Despite the title this article is not going to be setting of competition between Python and SAS. There a far too many comparisons out there and our aim would be to introduce Python and compare it to SAS as alternative of platform which can be used for advanced data processing for clinical research.
First and most distinguishing difference between both is that SAS is proprietary closed system and Python is Open Source and contains detailed transparency of all its functionalities. SAS is one of the most expensive software to achieve and Python is free to use. If you want to achieve new functionalities from SAS you should buy separate modules while in Python all goes free. Besides that, SAS University edition can be used for free if you want to get impression and learn the language. In Python you have Python package indexer where you can find thousands of packages which are free for use and even can be modified for your own purposes because are open source.
SAS is the integrated system of software solutions. Company which stays behind provide awesome technical support. SAS helps you to do various data tasks like:
Data management
Advanced analytics
Multivariate analytics
Business intelligence
Statistical and mathematical analysis
Applications development
Python is an object-oriented programming language that has clear and easily readable syntax. Guido Van Rossum published the first version of Python code (version 0.9.0) at alt.sources in February 1991. It has become more popular in a short period of time because of its simplicity. Python can be used through various different IDEs or platforms. Nowadays using Python via Jupiter notebooks has become very popular amongst different kind of scientists. Basically, all listed above capabilities of SAS can be achieved with Python as well.
Both platforms are having great capabilities. Because of our favors we prefer Python. Further in our blog posts we will dig into detail additional platforms which in combination can facilitate Python and provide it with advanced environment in which we can perform complex data transformations.
Meanwhile recently started an initiative from SAS which allows usage of both technologies SAS and Python. It is a library called saspy. In further articles we will explore this as well.
Intention of all further related articles is to provide detailed information of alternative in data science for clinical research outside SAS. Also, to address the curiosity of SAS programmers that are interested in what different language we can performing similar tasks with same performance. We will try to give practical examples of how these results can be achieved.
Here’s what can you expect soon.
