Data science requires proficiency 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 help a data scientist to collect data, create visualizations, perform predictive and statistical analysis, and communicate the course of action. the results to stakeholders.
Basics of the 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. Includes statistical interference, linear regression and machine learning algorithm, time series and much more. Detailed analysis such as data reduction, correlation, and data clustering is easily done with R programming. R’s data handling process is very effective compared to the rest. Critical programming language concepts are easily analyzed with R. It also provides a large, integrated, and consistent collection of transition tools, particularly 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 joined in the runtime process.
R can be useful for analyzing data that occurs in a series of the following steps:
- Programming: R is an accessible and transparent programming tool
- Transforming – is a collection of various library tools that can help us solve problems particularly related to data science.
- Discover: You must refine, analyze and investigate the data.
- Modeling: R captures the correct model of the data using a wide range of tools.
- Communicate: Integrate graphs, outputs, and code to share data outputs.
R programming is used by numerous industries including academia, agriculture, healthcare, government, insurance, finance, retail, manufacturing, and much more. Major companies like Google, Facebook, Microsoft, Uber, Twitter, HP, Ford, and Novartis use multipurpose R. Apart from these, IT sectors, analytics, financial institutions and consulting companies also use R for better operation. Economic forecasting, behavioral analysis, advertising effectiveness, data visualization, statistical analysis, and credit risk modeling are some of the main uses of R programming.
R provides several methods to simplify the statistical analysis of big data by using the following features:
- Linear and nonlinear modeling
- Time series analysis
- Group
- Interfaces and extensibility to other programming languages
- Shareable code packages
The charting and graphing capabilities of this language are unmatched. R initially gained popularity in academia where data had to be constantly reproduced and became a key to credibility. Reproducible work has been advantageous in the business sector where chunks of data were analyzed from time to time. Every tool required by a data scientist to evaluate and manipulate data is included in the R programming language.
Data science is redefining the way companies run their business. Learning a language like R can help you in a number of ways, such as providing the best understanding of data, eventually making you a successful data analyst.
