Data science courses

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.

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

Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.

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

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

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

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

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

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

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

Master sampling to get more accurate statistics with less data.

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

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

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

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Transform almost any dataset into a tidy format to make analysis easier.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

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.