Leveraging C++ to more than double your code’s speed.
has always been a quite spectacular language for statisticians, as it has been optimized with statistics in mind. As data became more complex and larger, R started to become more relevant even as Python took over programming “market share” in the space, and to this day, R still holds its own for many statistical paradigms needed to be solved today. In this article, I will outline how to leverage C++ in R to allow you to supercharge your code by orders of magnitude using Rcpp.
As the name implies Rcpp acts as an interface between R and C++. Not only does it allow R users to take functions they have already built out in pure R and build it in C++ for even faster results, but it also enables your C++ functions to be easily exported and used in your R scripts.
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Find out here. Although data science job descriptions require a range of various skillsets, there are concrete prerequisites that can help you to become a successful data scientist. Some of those skills include, but are not limited to: communication, statistics, organization, and lastly, programming. Programming can be quite vague, for example, some companies in an interview could ask for a data scientist to code in Python a common pandas’ functions, while other companies can require a complete take on software engineering with classes.
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
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