In contrast, finance, health care and government industries often use SAS to perform predictive and prescriptive analytics, access and analyze raw data, manage data entry, format, recover and analyze data. Companies can use this programming language to import and clean data, provide statistics for data science and access programming elements, such as conditionals and loops, that are useful for data analysis. The finance, marketing and business industries are major users of R. Here are some of the fundamental differences between SAS and R: Uses They perform similar functions but have many differences in features, use and functionality. The IT industry considers SAS and R as counterparts that rely on each other. Related: The 14 Best Programming Languages To Learn in 2022 SAS vs. This compatibility also enhances creativity because you can implement coding techniques from different languages to improve your output. You can use R to manipulate objects directly in other programming languages, which can save time and costs. It also provides alternative codes you can use to achieve similar functions, which can save you time. The growing popularity of R has led to a widening of its graphic libraries, which makes the coding process more efficient. It also allows people to use their creativity by allowing them to change the code to suit their needs. Because R is an open-source programming tool, it can improve problem-solving speeds and allow users to make code changes quickly. The various benefits R programming language offers can include: Related: What Is Data Security? Definition, Importance and Strategies Advantages of using R Its enhanced data security features make it popular among users and because the code isn't available to the public, it's challenging for unauthorized users to access to your systems. SAS offers close source capabilities, providing adequate data security. This software's interface includes graphs and plots, which can make statistical computing easier. This makes it a good language for people with little programming experience to learn and master. SAS uses a simple syntax that uses short and intuitive commands. Using SAS can offer several benefits to individuals or organizations, including: Related: 50 Types of Programming Languages and What They Do Advantages of using SAS You can use it to determine how variables fluctuate with time. Time regression: This statistical method uses a model's response history to predict future responses. You can broadly classify statistical inference into estimations and hypotheses. Statistical inference: This is the process of using data to infer properties of an underlying probability distribution. Some popular machine learning algorithms include random forest, naive Bayes, dimensionality reduction and gradient boosting. Machine learning algorithms: This refers to methods through which artificial intelligence (AI) systems use to perform their tasks and predict output values. The graphical slope defines how a change in one variable affects the other. Linear regression: This is a regression model that determines the relationship between an independent and dependent variable using a straight line. You can use it in research, academics and business, particularly among startup companies. R organizes data, uses formulas to analyze it and produces visual reports on the information it finds. It's a free, open-source platform, meaning its code is publicly available to anyone. R is a programming language data scientists use as an alternative to SAS for data analysis. Related: How To Analyze Data in 7 Simple Steps: A Definitive Guide What is R? Make strategic decisions, also known as business intelligence Gather information from large amounts of raw data The program reads and stores data, analyzes it and produces reports based on its findings. You can create reports in graphs, tables, portable document formats (PDFs), HyperText Markup Language (HTML) or rich text format. Large companies typically use SAS to: Statistical Analysis System (SAS) is a system of software products that people who work in IT use for advanced statistical and data analysis. In this article, we define SAS and R, discuss the pros of using each and explain their key differences to help you determine which one suits you for your purposes. If you work in IT, knowing at least one of these programs and understanding the differences between them can help you operate analysis software more efficiently and improve your industry skills. Two of the most common tools for data science are SAS and R. Many information technology (IT) companies and departments depend on data analysis for their daily operations and functions.
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