Python vs R vs sAs
• The field of Data Science is rapidly expanding, and with the constant flow of various types of data, it is critical to have tools that can cope with the different needs and solve a variety of problems. • For a long time, the tools for dealing with data were proprietary software such as SPSS or SAS. • Nevertheless, with the introduction of ML, AI, DL, and Big Data, things have begun to change, necessitating the development of new tools that can function in the new Data Science landscape.
• As a result, it is critical to compare these tools and determine which one is superior and why. Tools • When it comes to Data Analysis as well as Data Science, the three most popular tools are currently SAS, R, and Python. • These three tools are currently engaged in a battle for the best Data Science tool.
• While SQL and SPSS continue to have their own area in which they work, modern-day solutions can still be achieved using these three tools (SAS, R, and Python). • There are major benefits and areas where certain of these tools excel and lead the way over others, while others lag. SAS • Source- https://en.wikipedia.org/wiki/SAS_ (software)#/media/File:Sas-studio-v-2.png • The first point to note is that, whereas SAS is specialized statistical software, R and Python are free and open-source software/languages.
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• This distinction becomes apparent during their adoption as a tool for conducting Data Analytics and Data Science, particularly with small and medium-sized businesses that are highly specialized in specific aspects of data-related operations. • While SAS is a closed source software with limited flexibility and a high cost, it can provide a more secure and stable platform, which is something that many large corporations and other institutions that deal with highly sensitive data require. • R and Python, on the other hand, have much more flexibility than SAS and almost no cost, and have all of the capabilities and even more when compared to SAS.
• While more and more companies begin to use open source languages to deal with data, especially since the introduction of Cloud-Based computing, where data can be securely saved, and the next concern that is being brought up is, which is preferable, R or Python for Data Science? • Because both languages are open source, they share common pluses that SAS obviously lacks, including being cost-effective, having a growing community, and because the source code is available. • The community contributes to making the software better with experience and much more quickly.
R vs Python
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• However, there are some areas where they significantly differ, and this is where a person or organization should focus before adopting any of them. • R is a general-purpose programming language, but it was designed for statisticians, so interacting with statistical calculations and models is its specialty and core strength. • Python, on the other hand, is a simple high-level language with a wide range of applications, beginning with automation and progressing to data science. • Another issue of debate is the IDE provided by these languages, with Python for Data Science appearing to take the lead as it provides the user with a variety of options to choose from.
• If models are developed using a statistical approach, one may choose R, but if concepts such as AI and Machine Learning are concerned, Python appears to be a better candidate. • In some areas, such as visualization, R takes the lead because it has better packages and a much more sturdy framework for producing high-quality complex graphs. • If we solely go by the numbers, Python consumers, courses, and jobs related to Python Data Science are all increasing. • Tutort Academy is an ed-tech startup led by a team of NIT Trichy, Google, and Microsoft Alumni.
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