Programming Languages of Data Science

Data Science is a study of analyzing data in different aspects. In several cases of consideration of data analysis, there is a general abstract framework that describes a basic structure on how data has to be designed. For example, in the generation of music notes, there’s a certain criterion like using only particular music notes for the respective tunes. Describing data analysis is a difficult conundrum. Developing a framework involves considering the elements of the data and implementing it using programming language. Why should we use programming languages for data analysis? As we know, data is used in many streams

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How to Structure Your Programming Code

I remember my first fumble with basic on my ZX Spectrum computer back in the 1980s, ploughing through pages of basic commands and example code without any real idea of how I could write programs myself. It was like reading a dictionary where I could learn certain words and their meanings with limited information on how I could construct them into entire sentences to write a document. Every programmer who has dabbled in basic has probably come across the famous “Hello Word” routine which consists of a two-line program that prints this phrase unlimited times on the screen. Your program

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R Programming: Data Science

Data science requires mastering in various fields like machine learning, R programming, Python, deep learning and many more. Among all these, one of the basic key programming languages required for every data scientist is R programming. These programming languages helps a data scientist to collect data, create visualization, perform predictive and statistical analysis and to communicate the course of results with stakeholders. Basics of R programming language: It is a programming language developed by Robert Gentleman and Ross Ihaka. It deals with various concepts of graphical and statistical methods. It includes statistical interference, linear regression, and machine learning algorithm, time

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