{"id":76768,"date":"2020-02-24T16:48:05","date_gmt":"2020-02-24T11:18:05","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=76768"},"modified":"2020-02-24T16:48:05","modified_gmt":"2020-02-24T11:18:05","slug":"sas-vs-r-vs-python","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/sas-vs-r-vs-python\/","title":{"rendered":"SAS vs R vs Python – The Battle for Data Science!"},"content":{"rendered":"

Get your own judgment on SAS vs R vs Python by going through this article and select which is the perfect match for your Data Science journey.<\/strong><\/p>\n

For more than two decades, data scientists have been debating the merits of using R and SAS for data analysis. The discussion has never reached a conclusion, but Python has now joined the race as a new popular tool for data science. Today, we will compare SAS vs R vs Python and try to decide which tool is the best for data science.<\/p>\n

Why Use R for Data Science?<\/h3>\n

R<\/strong><\/a> is an open-source programming language for statistical computation<\/strong>. It is highly useful for performing complex mathematical and statistical calculations on data as well as for data visualization<\/strong>. Due to its open-source nature, R has a massive community. The R community has, over the years, created a large number of packages that make R capable of performing all data science tasks.<\/p>\n

Why Use Python for Data Science?<\/h3>\n

Python<\/strong><\/a> is an open-source scripting language. It is famous for web development, software development, and data science. It has a wide variety of libraries that allows you to work with several fields like data-transformation<\/strong>, data-filtering<\/strong>, data-wrangling<\/strong>, machine learning<\/strong>, predictive analysis<\/strong>, etc.<\/p>\n

Why Use SAS for Data Science?<\/h3>\n

SAS stands for Statistical Analytics System<\/strong>. It is a proprietary software tool for statistical analytics. SAS is very expensive<\/strong>. Large corporations use it, but it is out-of-reach for individuals and small organizations. SAS is suitable for complex statistical operations but lacks in data visualization, advanced analytics, and machine learning techniques.<\/p>\n

SAS vs R vs Python?<\/h2>\n

There has never been a conclusive answer to the question of which is better SAS vs R vs Python? All three technologies have situations where they excel while lacking in some. What tool one chooses should be based on the person\u2019s requirements<\/strong>. We have made comparisons among them based on the following points:<\/p>\n

    \n
  1. Ease of learning<\/li>\n
  2. Data handling ability<\/li>\n
  3. Data visualization<\/li>\n
  4. Cost-effectiveness<\/li>\n
  5. Customer service and community support<\/li>\n
  6. Updates<\/li>\n
  7. Market demand<\/li>\n<\/ol>\n

    1. Ease of Learning<\/h3>\n

    SAS is probably the easiest to learn<\/strong> of all three. It has a good GUI that makes it even easier to learn and use. To effectively use SAS, one needs to know SQL beforehand.<\/p>\n

    R has the steepest learning curve among these. It is a low-level language<\/strong>, so it requires more code for simpler tasks. Without prior knowledge of effective coding practices, the code can be even messier and longer to accomplish the simplest of tasks.<\/p>\n

    Python is known for its flexibility and simplicity. It doesn\u2019t have a widespread GUI, but Python notebooks are becoming popular. Python is a high level, object-oriented language<\/strong>, and is easier to learn than R.<\/p>\n

    When it comes to learning, SAS is the easiest to learn, followed by Python and R.<\/p>\n

    2. Data Handling Ability<\/h3>\n

    Data is increasing in size and complexity every day. A data science tool must be able to store and organize large amounts of data effectively.<\/p>\n

    SAS is smooth and stable when it comes to handling data on stand-alone machines.<\/p>\n

    R works only on RAM<\/strong>, this makes working with large datasets very slow. It does have packages<\/strong> like plyr<\/strong> and Dplyr<\/a> that make data handling much easier in R. We can also integrate R with Hadoop, which makes distributed data storage and processing possible.<\/p>\n

    Python has libraries like Panda and NumPy that make data handling extremely easy.<\/p>\n

    As base packages or with added extensions, all three can handle large data effectively.<\/p>\n

    3. Data Visualization<\/h3>\n

    SAS can plot graphs and render basic graphs, but its data visualization capabilities are merely functional.<\/p>\n

    R excels at data visualization with packages<\/a><\/strong> like ggplot2, plotly, Rvis, Rgis, etc.. It is the best tool for data visualization.<\/p>\n

    Python has extensions like matplotlib and seaborn that make creating custom graphs easy.<\/p>\n

    R is the clear winner for data visualization.<\/p>\n

    4. Cost-Effectiveness<\/h3>\n

    SAS is proprietary software. It is highly expensive<\/strong> and out-of-reach for individuals and small and medium-sized organizations. While there is a free university edition available, it has limited features.<\/p>\n

    R is open-source<\/strong>. Anyone can use it and contribute to it. Many startups and large-scale organizations as well, use R.<\/p>\n

    Python is also an open-source language. Students, as well as professionals, can use it for free<\/strong>.<\/p>\n

    R and Python being open-source have an advantage over SAS in terms of cost-effectiveness.<\/p>\n

    5. Customer service and Community Support<\/h3>\n

    SAS has dedicated customer support that helps with all issues about installation and usage. However, due to its cost, the community is not that large.<\/p>\n

    R does not have a dedicated customer service team, but it does have a massive community. The R community has people from almost all industries and from all over the world. A solution for any issue can be provided by the large community.<\/p>\n

    Python is also open-source, and therefore, it also has a large community. However, Python\u2019s popularity has increased in recent years and, thus, the community is not as large as R\u2019s.<\/p>\n

    While SAS may have dedicated customer support, it\u2019s community pales in comparison with that of R\u2019s pr Python\u2019s.<\/p>\n

    6. Updates<\/h3>\n

    SAS only gets updated at every new version rollout. However, the SAS team includes new features after thorough testing, and they have almost no errors.<\/p>\n

    R and Python are open-source. Anybody can add new features<\/strong><\/a> with new packages and extensions. They receive the latest updates faster.<\/p>\n

    R and Python get the latest updates much faster, and anybody can contribute towards it.<\/p>\n

    7. Market Demand<\/h3>\n

    While SAS was the global leader in available corporate jobs in data analytics earlier. The scenario has changed in recent years. More and more companies are opting for open-source technologies creating more job openings for people with skills like R and Python. Large corporations like Ford use R<\/a><\/strong> along with Hadoop for data analysis. They require professionals with skills in such technologies.<\/p>\n

    Apart from data science, web and software development industries also use Python and hire professionals with skills in it.<\/p>\n

    SAS was a global leader in corporate jobs for data analysis. But now, the open-source technologies have taken over. We expect the job opportunities for R and Python to keep increasing.<\/p>\n

    Want to know how Ford combined R with Hadoop? Find out here<\/a>.<\/strong><\/p>\n

    Summary<\/h2>\n

    In the cutthroat and competitive field of data analytics, this battle for the best tool for data science may be never-ending. All three tools have their strengths and weaknesses. There is no one solution for all data science problems.<\/p>\n

    To choose the best tool suited for your purpose, you should focus on technologies whose strengths lie with your requirements. While we cannot make a choice for you, we can recommend the technologies based on your situation.<\/p>\n

    SAS is best for corporate setups that can afford it and make full use of it. While we would recommend R for heavy calculations and strong data representation and visualization. Python would be a great choice for startups and small scale organizations.<\/p>\n

    These are our views on SAS vs R vs Python.<\/p>\n

    Got your answer? Or Still confused?<\/p>\n

    You can ask your query through the comment section.<\/p>\n

    Happy Learning!!<\/p>\n","protected":false},"excerpt":{"rendered":"

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