Source: Udemy
The editors at Solutions Review have compiled this list of the best R courses on Udemy to consider if you’re looking to grow your skills.
R is a language and environment for statistical computing and graphics. R can be considered as a different implementation of S, and while there are some important differences, much of the code written for S runs unaltered on R. The language provides a variety of statistical and graphical techniques including linear and nonlinear modeling, classical statistical tests, time-series analysis, and classification and clustering. R capabilities are enhanced via user-created packages that allow for special statistical techniques, graphical devices and reporting.
With this in mind, we’ve compiled this list of the best R courses on Udemy if you’re looking to grow your skills for work or play. Udemy is one of the top online education platforms in the world with more than 130,000 courses, expert instruction, and lifetime access that allows you to learn on your own schedule. As you can see below, we broke the best R courses on Udemy down into categories based on the recommended proficiency level. Each section also features our inclusion criteria. Click GO TO TRAINING to learn more and register.
Note: We included courses with more than 1,000 reviews and a rating of 4.3 stars or better.
Description: Learning R will help you conduct your projects. In the long run, it is an invaluable skill that will enhance your career. Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the applied family of functions can be used for looping. In the graphics section, you will learn how to create and tailor your graphs.
Description: The course is meant for absolute beginners, so you don’t have to know anything about R before starting. You don’t even have to have the R program on your computer. But after graduating from this course you will have the most important R programming skills – and you will be able to further develop these skills by practicing, starting from what you will have learned in the course. This module contains about 100 video lectures in nine sections.
Description: If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place. Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart, or how to compute a simple statistical test like the one-sample t-test. Everything is here, in this course, explained visually, step by step.
Note: We included courses with more than 900 reviews and a rating of 4.5 stars or better.
Description: This course includes professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for analytics in the real world. This module shows you how to prepare data for analysis in R, how to perform the median imputation method, how to work with date-times, what Lists are and how to use them, and more. After every section, you will already have a strong set of skills to take with you into your data science career.
Description: In this course, the instructors will show you step-by-step how to master R Shiny. The module starts out with the general shiny script. You will then learn how to make your app interactive by using input widgets. You will also uncover how to style your app for an appealing layout. The module also shows you how to use HTML tags to integrate or embed standard web content like YouTube videos, PDFs, text, pictures, and more. All the software downloads, add-on packages as well as entry-level hosting for shiny are totally free.
Note: We included courses with more than 1,000 reviews and a rating of 4.3 stars or better.
Description: This course was designed by two professional data scientists to share knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. The instructors will walk you step-by-step through the field of machine learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of data science. The module is also packed with practical exercises that are based on real-life examples. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Description: This course is truly step-by-step. In every new tutorial, instructors build on what students had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.
Description: This comprehensive course is comparable to other data science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture, this is one of the most full-featured modules for data science and machine learning on Udemy. Instructors will teach you how to program with R, how to create amazing data visualizations, and how to use machine learning with R!
Description: R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’. You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations and analysis in R.
Description: After completing this course you will be able to confidently build predictive machine learning and deep learning models to solve business problems and create a business strategy, answer machine learning related interview questions, and participate and perform in online data analytics competitions such as Kaggle competitions. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in real-world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.
Description: In this course, you will learn how to handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and times especially tricky for the programmer. You will also learn about POSIXt classes in R Base, the chron package, and especially the lubridate package. Then you will see how different models work, how they are set up in R, and how you can use them for forecasting and predictive analytics.
Description: The course will teach you the basic concepts related to Statistics and data analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application. Additional areas of statistics that are covered include descriptive analytics, data visualization, probability, population and sampling, probability distributions, and hypothesis testing. This module was designed for anyone who wants to learn R and R Studio for career advancement.
Description: The course will mostly focus on helping you implement different statistical analysis techniques on your data to interpret the results. After each video, you will learn a new concept or technique which you may apply to your own projects immediately. This module was designed for people working in any numerate field which requires data analysis, students of the natural sciences, and individuals with some prior knowledge of the R interface.
Description: After completing this course you will be able to confidently build predictive Machine Learning models to solve business problems and create a business strategy, answer Machine Learning related interview questions, and participate and perform in online data analytics competitions such as Kaggle competitions. With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
