STATISTICAL ANALYSIS SOFTWARE (SAS) VS PYTHON: BETTER TO USE
In the era of digital transformation, there are many companies or businesses that have large amounts of unstructured data, so to maintain such data, businesses need a data scientist or analyst who converts data from unstructured to structured. ssssAnd it also helps to trade after knowing the demand in the market. Hence, there is a need for a software that helps in learning the techniques of data analysis as per the trends in the market. Banking, insurance and others are some of the industries that require data analysts who analyze data accordingly.
To maintain or learn data analysis there are some programs that help in creating a perfect report. Now today we talk about SAS vs Python, both are programs that help to acquire the techniques of analyzing data and know the activities of data science. SAS stands for Statistical Analysis Software, and it can be difficult for newbies but is used by many companies and helps to use operational series on database to create a perfect report for data analysis which is used by programmers of SAS unlike Python, it is one of the most popular programming languages for existing or new developers and provides a structured pattern that is helpful for building or modifying plans and learning other data science activities.
So, now let us dive into the depth of both SAS and Python and also learn about SAS vs Python in detail.
WHAT DO YOU UNDERSTAND BY SAS?
SAS stands for Statistical Analysis Software and was designed in 1960 by Anthony James Burr at North Carolina State University, which is a self-supporting platform, which means that SAS runs on both Linux and Windows operating systems is developed to assist data technologists, i.e., it is a fully structured programming language that performs data analysis in large businesses.
WHAT IS PYTHON?
Python is a computer programming language designed by Guido van Rossum in 1991 and used to create websites, software programs, and easily execute code in Python. And it is an intuitive language to learn and use, which is why it is a friendly programming language for new program developers as python performs multiple tasks and handles large amounts of data.
ELEMENTS OF SAS VS PYTHON:
Elements of SAS:
- 4 Generation Programming Language (4GL): SAS is a 4GL language which means that SAS has easy to learn patterns and the code of SAS programming language is like statements which are simple and short instructions to systems.
- Administration: Administration is an important element of the SAS language consisting of environmental supervision that alerts, monitors and maintains the analysis infrastructure. Also, the extended Java graphical user interface (GUI) manages SAS tasks in the administration of SAS.
- SAS Studio: This is the standout element among SAS elements that one can access from any device or any web browser i.e., all the libraries and data files required in SAS program can be accessed through any browser and no need for client installation.
- Secure data design: SAS provides a secure facility to abstract access and also secure SAS data on disk through a number of designs.
- Powerful Element of Data Analysis: SAS is a versatile software which provides complete knowledge of data analysis field which means it analyzes from simple data to high level data.
- Output Format of Reports: SAS has the ability to test analytical and statistical results with various reporting options and has advanced level quality graphics. In SAS, the user can save and create new reports in default formats such as RTF, PowerPoint, etc.
ELEMENTS OF PYTHON:
- Support Libraries: In Python, there is a huge library of standards which includes network protocols, operating system interfaces, online services and many more. And there are already coded programming activities in the library which reduce the time to write the code.
- Dynamic Language: Python is a high-level dynamic language which means that the value fluctuations are not fixed in the future but in the present and due to dynamic nature there is no need to specify the type of the values.
- Open-source language: Python is an open-source language, which means that the code of Python language is available to the people on the official website at zero cost.
- Integration with third parties: The Python package is integrated with third parties, which means it helps in communication with multiple platforms in multiple languages.
- Graphical Support: Python supports Graphical User Interface (GUI), which means Python program helps to develop graphical software with its modules and some of the modules supporting GUI are PEQ15, PEQ14 and PEQ15 is one of the most popular options for graphical software programs in python.
- Intuitive Coding: Python is a very easy language to learn, and it also helps to write code effortlessly unlike other languages and new developers easily learn the basics of Python, that's why it is a friendly language for developers.
STATISTICAL ANALYSIS SOFTWARE VS PYTHON: WHY WE CHOOSE?
Statistical Analysis Software:
- Large for Commercial Purposes: SAS provides a wealth of data analysis functions for large businesses and is a great learning option for beginner data analysts.
- Designed for educational purposes: SAS is a free software for educational purposes and is available to anyone who wants to learn SAS computing for free.
- Suitable for complex data analysis: SAS is a versatile software suitable for performing all data science tasks.
• Easy for new developers: Python is a simple and structured language and highly readable unlike other languages, that's why new developers choose Python. Python also helps in writing less code.
• Large standard library: In Python, there is a large set of libraries that contain already written code for developers give to ease software development for developers. TensorFlow, SciPy, and Keras are just a few of Python's machine learning libraries.
• Good for data science: Python is good for data science and the community of data scientists, new programmers and machine learning experts give ease of building databases with Python for testing capabilities. Some libraries that can be used for data science are TensorFlow, Seaborn, etc.
• All-Rounder Language: Python is an all-rounder language, which means that Python is used in Machine Learning, Artificial Intelligence and many other projects. Python is a favourable language among developers due to all-rounder feature, and it has some automation tools which help in upgrading the performance like Arduino and Raspberry.
From the above talk, we learn about SAS vs Python where we know that SAS is a fully efficient language suitable for large organization, and it is difficult for newbies unlike Python is a friendly language for new developers, and it is a free programming language and who are interested in Data Science so, the above discussion about SAS vs Python is useful for them.