Python vs R for Data Science: And the winner is..

Python’s suite of specialized deep learning and other machine learning libraries includes popular tools like scikit-learn, Keras, and TensorFlow, which enable data scientists to develop sophisticated data models that plug directly into a production system.RR was developed in 1992 and was the preferred programming language of most data scientists for years..It is a procedural language which works by breaking down a programming task into a series of steps, procedures, and subroutines..This is a plus when it comes to building data models because it makes it relatively easy to understand how complex operations are carried out; however, it is often at the expense of performance and code readability.R’s analysis-oriented community has developed open-source packages for specific complex models that a data scientist would otherwise have to build from scratch..R also emphasizes quality reporting with support for clean visualizations and frameworks for creating interactive web applications..On the other hand, slower performance and a lack of key features like unit testing and web frameworks are common reasons that some data scientists prefer to look elsewhere.Process of Data ScienceNow, it is time to look at these two languages a little bit deeper regarding their usage in a data pipeline, including:Data CollectionData ExplorationData ModelingData VisualizationData CollectionPythonPython supports all kinds of different data formats..You can play with comma-separated value documents (known as CSVs) or you can play with JSON sourced from the web..You can import SQL tables directly into your code.You can also create datasets..The Python requests library is a beautiful piece of work that allows you to take data from different websites with a line of code..It simplifies HTTP requests into a line of code..You’ll be able to take data from Wikipedia tables, and once you’ve organized the data you get with beautifulsoup, you’ll be able to analyze them in-depth.You can get any kind of data with Python..If you’re ever stuck, google Python and the dataset you’re looking for to get a solution.RYou can import data from Excel, CSV, and from text files into R..Files built in Minitab or in SPSS format can be turned into R data frames as well.. More details

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