Top 8 Online Courses for R Programming and Statistics
R is a statistical and visualization language which is deep and huge and mathematical. It is one of the most preferred programming languages of most data scientists. R makes it possible to find a library for whatever the analysis you want to perform. The rich variety of libraries makes R the first choice for statistical analysis, especially for specialized analytical work. Additionally, one of the standout features of using R is you can create beautiful data visualization reports and communicate the findings. In this post, we will look at the best online courses for R programming and Statistics.
Best Online Courses for R Programming & Statistics
4.6 (23,264 ratings) || 104,105 students enrolled
After Enrolling in this course you can Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2.
You’ll learn how to install packages in R, Learn how to customize R studio to suit your preferences, use R Studio, core principles of programming, create a while() loop and a for() loop in R, create vectors in R, create variables, build and use matrices in R, the matrix() function, learn rbind() and cbind().
You can also Practice working with statistical data in R, working with financial data in R, working with sports data in R.
4.5 (11,265 ratings) || 118,181 students enrolled
In this course you will learn how to: Navigate in the RStudio interface, make basic graphs, about the basic structure of R including packages, perform basic commands in the R programming language
You will also learn how to handle add on packages, how to use the R help tools and generally how to find your way in the R world.
4.6 (8,471 ratings) || 45,216 students enrolled
In this course you’ll learn how to use the R programming language for data science and machine learning and data visualization!
You’ll Program in R and Use R for Data Analysis, Create Data Visualizations, R to handle csv,excel,SQL files or web scraping, R to manipulate data easily, R for Machine Learning Algorithms and for Data Science
4.6 (4,347 ratings) || 34,402 students enrolled
I this Course you’ll Perform Data Preparation in R, Identify missing records in dataframes, Locate missing data in your dataframes, Apply the Median Imputation method to replace missing records, Apply the Factual Analysis method to replace missing records, Understand how to use them which() function and Know how to reset the dataframe index.
Also you’ll able to Work with the gsub() and sub() functions for replacing strings, Explain why NA is a third type of logical constant, Deal with date-times in R, Convert date-times into POSIXct time format, Create, use, append, modify, rename, access and subset Lists in R, Understand when to use  and when to use [] or the $ sign when working with Lists.
Creating a time series plot in R, Understand how the Apply family of functions works, Recreate an apply statement with a for() loop, Use apply() when working with matrices, Use lapply() and sapply() when working with lists and vectors, Adding your own functions into apply statements, Nest apply(), lapply() and sapply() functions within each other and Using the which.max() and which.min() functions
4.4 (2,512 ratings) || 28,731 students enrolled!
In this course you’ll learn how to install R and RStudio, Create vectors and data frames in R, Plot points and lines with ggplot, Access vectors from data frames, Group with ggplot, Plot residual lines with ggplot, Fit a least squares line to a data set and to Use a least squares line for prediction.
4.6 (15, 219 ratings) || 4,25,625 students enrolled
In this course, you will learn how to program in R and how to use R for effective data analysis. You will learn how 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 covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
4.7 (14,208 ratings) || 56,241 students enrolled
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
2,76,623 students already enrolled!
This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.
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