Computer science requires mastery in various fields such as machine learning, R programming, Python, deep learning and many more. Among all these, one of the basic key programming languages required for any data scientist is R programming. These programming languages help a data scientist collect data, create visualization, perform predictive and statistical analysis, and communicate the outcome with stakeholders.
Programming Language Basics R:
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 series and more. Detailed analysis such as data reduction, correlation and clustering of data is easily performed with R programming. The data management process for R is very efficient compared to the rest. Critical programming language concepts are easily analyzed by R. It also provides large, integrated, cohesive collection of transition tools, especially used for data analysis. Graphic facilities, flexible tools along with well-structured programs are its specialties. For exhaustive programs, C, C ++ and FORTRAN are often accompanied in the run-time process.
R can help analyze data that takes place in the series with the following steps:
Programming: R is an accessible and transparent programming tool
Transforming: It is a collection of different library tools that can help us solve problems especially related to data science.
Discover: refinement, analysis and data analysis needs to be done.
Modeling: The real model of the data is captured by R using a variety of tools.
Communication: Integrate graphs, outputs and codes to share output of data.
R programming is used by several industries, namely academic, agriculture, healthcare, government, insurance, finance, retail, manufacturing and much more. Large companies like Google, Facebook, Microsoft, Uber, Twitter, HP, Ford and Novartis use R multifunctions. In addition to these, IT sectors, Analytics, financial institutions and consulting firms also use R to function better. Financial forecasting, behavioral analysis, advertising effectiveness, data visualization, statistical analysis and credit risk modeling are some of the key uses of R programming.
R provides various methods to simplify statistical analysis of big data using the following functions:
Non-linear and linear modeling
Time series analysis
Interfaces and extensibility to other programming languages
The mapping capabilities and graphics in this language are unmatched. Initially, R gained popularity in the academic sector where data had to be constantly reproduced and it became a key to credibility. Reproducible work has been advantageous in business, where pieces of data were analyzed time and time again. Each tool required by a data scientist to evaluate and manipulate data is included in the programming language R.
Computer science defines the way companies conduct their business. Learning a language like R can help you in various ways such as providing the best insight into data, which eventually makes you a successful data analyst.