R Language Programming Course Certification

R is rapidly becoming the leading language in data science and statistics. Today, R language programming is the tool of choice for data science professionals in every industry and field. Whether you are full-time number Cruncher, or just the occasional data analyst, R will suit your needs. This R Language Programming Course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. 

In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required. What makes this course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the Data Camp platform. R Language Programming is a dominant tool for statistical computing and data analysis. Developed by statisticians Ross Ihaka and Robert Gentleman, R Language Programming  is an open-source language that provides a wide cluster of statistical and graphical techniques. Its compressive library of packages allows users to perform complex data manipulations, statistical modeling, and data visualization with ease. R Language Programming is highly extensible, supporting integration with other languages like C++ and Python. The language’s comprehensive documentation and active community contribute to its robustness and flexibility. R Language Programming  is widely used in academia and industry for tasks ranging from data cleaning and preparation to advanced analytics and machine learning. Its convenient syntax and strong support for data visualization make it an fundamental tool for data scientists, statisticians, and researchers. R Language Programming continues to evolve, assuring it remains important in the rapidly advancing field of data science.

Key Features of R

1. Statistical Computing:  R provides a wide variety of statistical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, and clustering.

2. Data Visualization:  R has excellent tools for creating detailed and customizable plots and graphics. Packages like ggplot2 and lattice allow for sophisticated visualizations.

3. Extensibility:  Users can extend R’s capabilities with packages. The CRAN (Comprehensive R Archive Network) repository has thousands of packages for various statistical and graphical applications.

4. Open Source:  R is free and open-source, which encourages collaboration and the sharing of packages and tools among users.

5. Community and Support:  R has a strong community and extensive documentation. There are numerous online forums, tutorials, and resources available for learning and troubleshooting.