In this blog, we are going to compare all the three languages on various aspects to give you a clear perspective about market value and capabilities of these languages, so that you can choose the language with that you can move forward.

It is a well-known fact that to learn data analysis, you can use three important languages that are Python, R, and SAS.

If you are a fresher in the data science community and do not have experience in any of the languages mentioned above, then it is vital to be acquainted with at least one language.

First, let’s take a quick introduction to all three languages.

SAS

Speaking of Enterprise Analytics Space, SAS is currently an Undisputed Market Leader. It provides a vast array of statistical functions; it provides a well-supported technical support team. It also has a good GUI for People to pick it up faster than others.

R

R is an open-source programming language. We can access it free and perform all data analysis tasks. It is the lingua franca for statistics.

Currently, R is the most widely used programming language, and it is also the first choice of data scientists. It is supported by a talented and vibrant community of contributors. R is also a part of university syllabus, that’s why taught in universities. It is deployed on critical business applications.

Python

Python is an open source, multi-purpose language. These days, it has become very popular in data science. The reason behind this, its immense data mining and vibrant community.

Now, we are going to compare on various aspects:

Features

Features of SAS

Strong Data Analysis Abilities
Flexible 4 Generation Programming Language (4GL)
SAS Studio
Support for Various Data Format
Multiple host system
Management
Report Output Format
Data Encryption and handling Algorithms

Feature of R

Connectivity with many databases and data types.
Effective storage and data handling facility.
Statistical flexibility
Remarkable data analysis
The capability of scripting and interface with other languages
Tools available to make predictions
Statistical flexibility

Feature of Python

Expressive Language
Cross-platform Language
Free and Open Source
Extensible.
Large Standard Library
GUI Programming Support
Integrated

Professional’s perspective

Let’s take a look at the use, on a professional’s perspective.

An international HR firm, asked about 1000 qualitative professionals about which language they prefer – whether it is SAS, R, or Python. Some of the results of the survey emerged like:

See the pie chart below:

Preference by various Industries

Let’s take a look at the preference by various industries.

Large companies mostly prefer SAS to provide better customer services and this is the reason behind SAS has an advantage within the marketing companies and financial services sector, where there is no concern on the budget for the selection of the tool.

On the other hand, Python and R are used in start-ups and mid-sized companies. Tech and telecom companies both require a large amount of unstructured data to get analyzed, and therefore, many data scientists of these sectors use machine learning techniques for which R and Python are more suitable.

In the graph, you can see the tool preference by various industries such as financial services, marketing, healthcare, retail etc.

Differences between SAS and R

Although both SAS and R is a widely used language in the field of data science, there are some notable differences which make them different from each other.

Sr.no.	Parameters	SAS	R
1	Objective	SAS is a specific programming language designed primarily for statistical analysis of data from spreadsheets or databases.	R programming language is widely used among statisticians and data miners to develop statistical software and data analysis.
2	Ease of Learning	Learning is easier than R because no programming knowledge is required. We can learn it with limited knowledge of SQL.	Learning is less easy than SAS because it contains tedious and lengthy code.
3	Statistical Ability	SAS provides a powerful package that offers all types of statistical analysis and techniques.	R is an open source tool that allows users to submit their own package/library. The latest technologies are often released in R First.
4	Cost	SAS is expensive commercial software.	R is an open source free software.
5	Graphical Capability	SAS has limited graphical support, Although there are some graphical capabilities in Base SAS, these abilities are not widely known, and therefore R gets a clear edge in this aspect.	R is the advanced in graphical visualization, due to the availability of various packages such as ggplot, latis, and RGIS.
6	Data Publishing	SAS supports data publishing in HTML, PDF, Excel, and other formats via the Output Delivery System.	R supports data publishing either in soft or hard copy.
7	File Sharing Capability	We cannot share SAS generated files with other users who do not have SAS Software.	Since R is an open source free software so that anyone can install it and use shared files.
8	Software Updates	SAS does not update frequently.	R is an open source software, so it continues the update.
9	Customer Support	SAS provides a huge and dedicated customer support	R has no customer service support. However, the most significant online communities support it.
10	Core Learning Support	Core learning in SAS software is still in its early stages, and there is a lot of work to do before it gets mature.	R provides great integrations for core learning.
11	Sharing of Market	Market share of SAS is facing crucial competition with R and other data analytical tools.	R is growing at high speed since the last five years because of its regular updates and integrated features.
12	Job Market	SAS software is the market leader in providing employment. Many large-scale companies prefer SAS for data analytics.	R is also providing a large number of jobs, and there has been a significant increase in the number of jobs in the last few years.

So, this brings us to the end of blog. This Tecklearn ‘SAS versus R versus Python’ blog helps you with commonly asked questions if you are looking out for a job in SAS. If you wish to learn SAS and build a career in Data Analytics domain, then check out our interactive, SAS Training for SAS BASE Certification Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/sas-training-for-sas-base-certification/

SAS Training for SAS BASE Certification Training

About the Course

SAS Certification Training is intended to make you an expert in SAS programming and Analytics. You will be able to analyse and write SAS code for real problems, learn to use SAS to work with datasets, perform advanced statistical techniques to obtain optimized results with Advanced SAS programming. In this SAS online training course, you will also learn SAS macros, Machine Learning, PROC SQL, procedure, statistical analysis and decision trees. You will also work on real-life projects and prepare for the SAS Certified Base Programmer certification exam. Upon the completion of this SAS online training, you will have enough proficiency in reading spreadsheets, databases, using SAS functions for manipulating this data and debugging it.

Why Should you take SAS Training?

The average salary for a Business Intelligence Developer skilled in SAS is $100k (PayScale salary data)
SAS, Google, Facebook, Twitter, Netflix, Accenture & other MNCs worldwide are using SAS for their Data analysis activities and advance their existing systems.
SAS is a Leader in 2017 Gartner Magic Quadrant for Data Science Platform.

What you will Learn in this Course?

Introduction to SAS

Introduction to SAS
Installation of SAS
SAS windows
Working with data sets
Walk through of SAS windows like output, search, editor etc

SAS Enterprise Guide

How to read and subset the data sets
SET Statement
Infile and Infile Options
SAS Format -Format Vs Informat

SAS Operators and Functions

Using Variables
Defining and using KEEP and DROP statements
Output Statement
Retain Statement
SUM Statement

Advanced SAS Procedures

PROC Import
PROC Print
Data Step Vs Proc
Deep Dive into Proc

Customizing Datasets

SAS Arrays
Useful SAS Functions
PUT/INPUT Functions
Date/Time Functions
Numeric Functions
Character Functions

SAS Format and SAS Graphs

SAS Format statements
Understanding PROC GCHART, various graphs, bar charts: pie, bar

Sorting Techniques

NODUP
NODUKEY
NODUP Vs NODUKEY

Data Transformation Function

Character functions, numeric functions and converting variable type
Use functions in data transformation

Deep Dive into SAS Procedures, Functions and Statements

Find Function
Scan Function
MERGE Statement
BY Statement
Joins
Procedures Vs Function
Where Vs If
What is Missover
NMISS
CMISS

PROC SQL

SELECT statement
Sorting of Data
CASE expression
Other SELECT statement clauses
JOINS and UNIONS

Using SAS Macros

Benefits of SAS Macros
Macro Variables
Macro Code Constituents and Macro Step
Positional Parameters to Macros

Got a question for us? Please mention it in the comments section and we will get back to you.

700