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.

Build Tidyverse skills by learning how to transform and manipulate data with dplyr.

Analyze the relative popularity of programming languages over time based on Stack Overflow data.

Learn to combine data across multiple tables to answer more complex questions with dplyr.

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.

Apply your importing and cleaning data and data manipulation skills to explore New York City Airbnb data.

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Take your R skills up a notch by learning to write efficient, reusable functions.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!

Learn to perform linear and logistic regression with multiple explanatory variables.

Master sampling to get more accurate statistics with less data.

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

In this course you will learn the basics of machine learning for classification.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.