Visualization tools help Data Scientists present complex data in an endless array of charts and graphs-a task that can be as much art as it is science. Open-source Jupyter Notebook is another popular application, comprising statistical modeling, data viz, machine learning functions, and more. One of a Data Scientist’s most important tools is RStudio Server, which supports a development environment for working with R on a server. For some seriously heavy number-crunching, there are Hadoop-based tools like Hive. R is better suited to smaller datasets, while Python comes in handy for Natural Language Processing applications. Both can run complex statistical functions, including regression analysis, linear and nonlinear modeling, statistical tests, and time-series analysis, among others. R is purpose-built for data analysis and data mining the more widely used Python is a general-purpose programming language that also happens to be well-suited to data analysis operations. While there are a handful of statistical programming languages, R and Python are by far the most popular data science programming languages.
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