Introduction to R and R Studio for Data Science

Best Free and Open Source Alternatives to Base SAS

SAS Institute Inc. (“SAS”) is an American multinational developer of analytics software based in Cary, North Carolina. The company has around 14,000 employees.

SAS started as a project at North Carolina State University to create a statistical analysis system used mainly by agricultural departments at universities in the late 1960s.

SAS is the name of their software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. It has more than 200 components covering areas including statistical analysis, econometrics and time series analysis, an interactive matrix language, data mining and much more.

This series looks at free and open source alternatives to the SAS software suite. We look at the best free and open source alternatives to its main components.

SASBase SAS is a fourth-generation programming language (4GL) for data access, data transformation, analysis and reporting. It is included with the SAS Platform.

Base SAS is designed for foundational data manipulation, information storage and retrieval, descriptive statistics and report writing. It also includes a powerful macro facility that reduces programming time and maintenance headaches.

4GLs are designed to reduce the overall time, effort and cost of software development.


1. R

R is a statistical programming language that can be used for data manipulation, visualisation of data and statistical analysis. The R language consists of a set of tokens and keywords and a grammar that you can use to explore and understand data from many different sources.

R offers a huge range of functions for every data manipulation, statistical model, or chart which is needed by the data analyst. R offers inbuilt mechanisms for organizing data, running calculations on the given information and creating graphical representations of that data sets.

Learn R with our recommended free books and free tutorials.


2. Python

Python is our second recommended open source language for data scientists mainly because of its incredibly powerful ecosystem with its huge array of machine learning/deep learning libraries, and powerful visualization software. It offers a huge array of libraries and functions for almost statistical operation / model building.

Python has an even larger following than R.

Learn Python with our recommended free books and free tutorials.


All articles in this series:

Alternatives to SAS's Products
SASBase SAS is a fourth-generation programming language (4GL) for data access, data transformation, analysis and reporting. It is included with the SAS Platform.
SAS JMPJMP (pronounced “jump”) is a suite of computer programs for statistical analysis. JMP software combines interactive visualization with powerful statistics.
SAS Enterprise BI ServerSAS Enterprise BI Server provides a solid basis for vendor consolidation and BI standardization, enabling IT to focus on more effectively aligning with the business.
SAS/ETSSAS Enterprise Miner aims to streamline the data mining process. It helps you analyze complex data, discover patterns and build models so you can more easily detect fraud, anticipate resource demands and minimize customer attrition.
SAS/ETSSAS/ETS provides SAS procedures for econometric analysis, time series analysis, time series forecasting, systems modeling and simulation, time series data management, and more.
SAS/GRAPHSAS/GRAPH is a data visualization tool that lets you create effective, attention-grabbing graphs. It consists of a collection of procedures that let you provide a variety of charts, plots, 3-D scatter/surface plots, and more.
SAS/IMLSAS/IML is a powerful, flexible matrix programming language for interactive and exploratory data analysis.
SAS/INSIGHTSAS/INSIGHT is a tool for data exploration and analysis. Explore data through graphs and analyses linked across multiple windows.
SAS/STATSAS/STAT provides tools and procedures for statistical modeling of data. It includes analysis of variance, linear regression, predictive modeling, statistical visualization techniques and a lot more.
Share this article

Share your Thoughts

This site uses Akismet to reduce spam. Learn how your comment data is processed.