Data Science -R Programming Language
R acquires a large catalog of statistical and graphical methods. It includes machine learning algorithm, linear regression, time series, statistical inference to name a few. R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. Most of the R libraries are written in R language, but for heavy computation task, C, C++ and Fortran codes are chosen.
R is not only delivered in academic, but many large companies also use R programming language, Big shots like- Uber, Google, Airbnb, Facebook and so on, use R programming language as the major tool.
Data Science with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results
- Program: R is a clear and comprehensible programming tool
- Transform: R is made up of a assortment of libraries constructed specifically for data science
- Discover: Examine the data, refining assumption and analyze them comes under discovery.
- Model: R provides a wide collection of tools to apprehend the right model for your data
- Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world. While pursuing any project for specific client during data science R language is preferred by most of the Data Scientists.
Why use R?
Data science is reforming and re-establishing the ways on which run their businesses. Without a doubt, Artificial Intelligence and Machine learning can bring wonderful achievements to the company. In the new era where Artificial intelligence is so much in trend and have reduced human labor immensely. They are a plethora of tools available to carry out data science. Learning a new language entails some period investment.
Should you choose R?
Data scientist can use two excellent tools: R and Python. Learning statistical modeling and algorithm is far more important than to learn a programming language, but languages make work simpler and easier. A programming language is a tool to calculate and publicize your discovery. The most important task in data science is the way you deal with the data: import, clean, prep, feature engineering, feature selection. This should be the principal focus at the time of pursuing the project. If you are trying to learn R and Python at the same time without a solid background in statistics, then not to worry, they are so learning friendly that anyone can learn them if they are really putting in efforts and applying the knowledge with learning. Data scientist are not programmers. Their job is to understand the data, manipulate it and expose the best approach. If you are thinking about which language to learn, let's see which language is the most appropriate for you.
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