The training consists of real-life analytical challenges for the learners to solve. You get new valuable concepts that can be applied right away through live examples. R has a steep learning curve and hence, offers step-by-step learning thus making it easier to learn. R Programming A - Z: R for Data Science: Udemy

Calculating frequencies and descriptives.

Creating bar character for categorical variables.

It also trains to get charts and tables out of R and share your results with presentation and web pages.īy the end of the course you would learn: The course further explains the examples of creating charts and plots using other data analysis tools. The author teaches statistical processing language in R, including installing R, reading data from SPSS and spreadsheets, and use packages for advanced R functions. Prerequisite: An up-to-date browser is recommended to enable programming directly in a browser-based interface.

Reproducible document preparation with R studio.

You will learn to apply general programming features like probability, inference, regression, and machine learning are also covered.įinally, the course develops skill sets that include: The course covers R’s functions and data types, then tackle to operate on vectors and when to use advanced functions like sorting. The course is designed to introduce its learners to the basics of R programming. Data Science R Basics Certificate by Harvard University: edX Language(s): Subtitles: Arabic, French, Chinese, Portuguese, Vietnamese, English, Spanish, Japanese. The course also covers practical issues in statistical computing which includes:

You will learn to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course introduces you to code in R and how to use R for effective data analysis. R Programming by John Hopkins University: Coursera

R studio udemy