R Development Online Courses & Certifications
R is a programming language and environment specifically designed for statistical computing, data analysis, and graphical visualization. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand in the 1990s. R is widely used in academia, research, and industry for statistical modeling, data exploration, and data-driven decision making.
R is open-source and free to use, which contributes to its popularity and widespread adoption in the data science and statistical communities. Its rich set of features, strong statistical capabilities, and active community support make it a preferred choice for data analysis, research, and statistical computing tasks.
This course is a great introduction to both fundamental statistics concepts and the R programming language. R is used by professionals in the Data Analysis and Data Science fields as part of their daily work.
R is a widely used programming language that works well with data. It’s a great option for statistical analysis, and has an active development community that’s constantly releasing new packages, making R code even easier to use. It’s built around a central data science concept: The DataFrame, so if you’re interested in data science, analysis, and visualization, you’ll want to learn how to use R.
Learn about the difference between simple linear regression and multiple linear regression in R
Discover how to include multiple explanatory variables in a model, how interactions affect predictions, and how linear and logistic regression work in R.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Learn what Bayesian data analysis is and how to use it for business modeling focusing on cash flows, investments, annuities, and saving for retirement in R
Learn to read .xls, .csv, and text files in R using readxl and gdata, before learning how to use readr and data.table packages to import flat file data.
In this course you will learn the basics of machine learning for classification.
Learn the essential data structures, including lists and data frames and have the chance to apply that knowledge directly to financial examples using R.
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for performance, and how to preprocess data
Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.
Learn how you can predict housing prices and ad click-through rate by implementing, analyzing, and interpreting linear and logistic regressions using R.
Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.
Learn how to use graphical and numerical techniques for exploratory data analysis while generating insightful and beautiful graphics in R.