Uplatz offers extensive course on R Programming for Data Science & Machine Learning. It is a self-paced video course. You will be awarded Course Completion Certificate at the end of the course.
R is a high-level programming language for statistical analysis, graphics representation and reporting. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science.
R provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical data representation. It also offers various data manipulation and cleaning tools, as well as a wide range of add-on packages for specialized tasks.
R programming language is a popular language among statisticians, data analysts, and researchers who deal with large amounts of data. Here are some key characteristics of R programming language:
Open source: R is free and open-source software, which means that it can be used, modified, and distributed by anyone.
Graphics: R provides a wide range of graphical capabilities, including static and interactive visualizations, which can be used to analyze and communicate data effectively.
Large community: R has a large community of users and developers, which means that there are many resources available for learning and troubleshooting.
Packages: R has over 18,000 packages available on the Comprehensive R Archive Network (CRAN) that extend its functionality and can be used for a wide variety of applications.
To learn R programming language, here are some steps you can follow:
Install R: You can download and install R from the Comprehensive R Archive Network (CRAN) website. It is available for Windows, Mac, and Linux.
Choose an Integrated Development Environment (IDE): An IDE makes it easier to write and run R code. Some popular IDEs for R are RStudio, Visual Studio Code, and Jupyter Notebook.
Learn the basics: Start by learning the basics of R syntax, data types, and data structures. You can find many online resources, such as tutorials, books, and videos.
Practice coding: Start coding small programs and practice coding with real-world data sets. This will help you to get familiar with R and its capabilities.
Explore packages: R has a vast library of packages that can help you to perform specific tasks. You can explore these packages and find the ones that are relevant to your work.
Join the community: Join R user groups, forums, and mailing lists to connect with other R users and get help with your coding challenges.
Overall, learning R programming language can be challenging but rewarding, and it can help you to unlock the full potential of your data analysis projects.
In this R Programming for Beginnerscourse, you'll be learning about the basics of R, and you'll end with the confidence to start writing your own R scripts. But this isn't your typical textbook introduction to R. You're not just learning about R fundamentals, you'll be using R to solve problems related to movies data. Using a concrete example makes the learning painless. You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R's most important data structures -- vectors! From vectors, you'll then learn about lists, matrix, arrays and data frames. Then you'll jump into conditional statements, functions, classes and debugging. Once you've covered the basics - you'll learn about reading and writing data in R, whether it's a table format (CSV, Excel) or a text file (.txt). Finally, you'll end with some important functions for character strings and dates in R.
