To find out a winner, I have assigned points (on a scale of 0 to 5) to each programming language in the following categories: the speed of execution, learning curve involved, it's data analytics capabilities, visualization support, development tools (IDEs, dev/build/deployment, etc), ease of integration with other applications/languages and the job opportunities in the Industry. Analytical solutions such as Excel, Stata and SAS are not compared as they are not programming-oriented. Programming languages - R, Python, Octave, MATLAB, Octave, Julia, etc provide the capabilities to perform data analytics operations in a much better way than traditional programming languages - Java, C++, C, etc as they offer rapid prototyping, machine learning classifiers and regressors straightaway. ![]() This becomes even difficult if you are starting off and wondering which programming language to learn. It's always a challenge when it comes to choosing a particular programming language that comes out as a winner, especially in the field of Data Science.
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