Introduction to R
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.
Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn to perform linear and logistic regression with multiple explanatory variables.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
In this course you will learn the basics of machine learning for classification.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Take your R skills up a notch by learning to write efficient, reusable functions.
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Transform almost any dataset into a tidy format to make analysis easier.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Master sampling to get more accurate statistics with less data.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.