Data scientists use Python and R for analysis from "summary" of Data Science For Dummies by Lillian Pierson
Data scientists rely heavily on programming languages like Python and R to carry out their data analysis tasks. These languages offer a wide range of tools and libraries that make it easier for data scientists to manipulate, visualize, and analyze data in a meaningful way. Python, for example, is known for its versatility and readability, making it a popular choice among data scientists for tasks such as data cleaning, visualization, and machine learning. R, on the other hand, is a specialized language designed specifically for statistical analysis and data visualization. It offers a wide range of packages and functions that are tailored to the needs of data scientists working with large datasets. Data scientists often use R for tasks such as statistical modeling, hypothesis testing, and data visualization. Both Python and R have strong communities of users who contribute to the development of new tools and libraries that can be used for data analysis. This means that data scientists have access to a wide range of resources that can help them tackle complex data analysis tasks more efficiently. By leveraging the power of Python and R, data scientists can uncover valuable insights from their data and make informed decisions that drive business success.
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