Old Version Data Scientist with R A Data Scientist combines statistical and machine learning techniques with R programming to analyze and interpret complex data. Learn More

Introduction to R Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

Intermediate Python Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

Python Data Science Toolbox (Part 1) Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Intermediate R Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Python Data Science Toolbox (Part 2) Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Statistical Thinking in Python (Part 1) Build the foundation you need to think statistically and to speak the language of your data.

Data Manipulation in R with dplyr Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.

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

Statistical Thinking in Python (Part 2) Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Writing Functions in R Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.

Correlation and Regression in R Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Data Visualization with ggplot2 (Part 1) Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Reporting with R Markdown Learn to create interactive analyses and automated reports with R Markdown.

Introduction to Data in R Learn the language of data, study types, sampling strategies, and experimental design.

Exploratory Data Analysis in R Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Data Types for Data Science in Python Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them t...

Introduction to Machine Learning Learn to train and assess models performing common machine learning tasks such as classification and clustering.

Joining Data in R with dplyr This course will show you how to combine data sets with dplyr's two table verbs.

Intermediate Importing Data in R Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Intermediate R: Practice Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.

Data Visualization in R This course provides a comprehensive introduction to working with base graphics in R.

Case Study: Exploratory Data Analysis in R Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Machine Learning with caret in R This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

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

Multiple and Logistic Regression in R In this course you'll learn to add multiple variables to linear models and to use logistic regression for classificat...

Data Visualization with ggplot2 (Part 2) Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2.

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

Data Visualization with ggplot2 (Part 3) This course covers some advanced topics including strategies for handling large data sets and specialty plots.

Importing & Cleaning Data in R: Case Studies In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.