Introduction to R (DataCamp)

Offered by DataCamp,
Introduction to R (DataCamp)

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames. In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis.

Class Deals by MOOC List - Click here and see DataCamp's Active Discounts, Deals, and Promo Codes.

With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this free R online course today!

Chapter 1: Intro to basics
In this chapter, you will take your first steps with R. You will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started!

Chapter 2: Vectors
In this free R course, we'll take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R! After completing this chapter, you will be able to create vectors in R, name them, select elements from them and compare different vectors.

Chapter 3: Matrices
In this chapter you will learn how to work with matrices in R. By the end of the chapter, you will be able to create matrices and to understand how you can do basic computations with them. You will analyze the box office numbers of Star Wars to illustrate the use of matrices in R. May the force be with you!

Chapter 4: Factors
Very often, data falls into a limited number of categories. For example, humans are either male or female. In R, categorical data is stored in factors. Given the importance of these factors in data analysis, you should start learning how to create, subset and compare them now!

Chapter 5: Data frames
Most data sets you will be working with will be stored as data frames. By the end of this chapter focused on R basics, you will be able to create a data frame, select interesting parts of a data frame and order a data frame according to certain variables.

Chapter 6: Lists
Lists, as opposed to vectors, can hold components of different types, just like your to-do list at home or at work. This intro to R chapter will teach you how to create, name and subset these lists.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Programação para todos (Conceitos básicos de Python) (Coursera) Coursera
University of Michigan

Programação para todos (Conceitos básicos de Python) (Coursera)

Este curso tem como objetivo ensinar a todos os conceitos básicos de programação de computadores usando Python. Abordamos o básico de como criar um programa a partir de uma série de instruções simples em Python. O curso não tem pré-requisitos e evita tudo, exceto a matemática mais simples. Qualquer pessoa com experiência moderada em computadores deve ser capaz de dominar os materiais deste curso. Este curso abordará os capítulos 1 ao 5 do livro “Python para Todos”.

Sep 21st 2026
5-12 Weeks
Understanding Data Engineering (DataCamp) DataCamp
DataCamp

Understanding Data Engineering (DataCamp)

Discover how data engineers lay the groundwork that makes data science possible. No coding involved! In 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine. Data engineers lay the foundations that make data science possible.

Self Paced
Self-Paced
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Sep 21st 2026
5-12 Weeks
Programming with Cloud IoT Platforms (Coursera) Coursera
Pohang University of Science and Technology - POSTECH

Programming with Cloud IoT Platforms (Coursera)

Internet of Things (IoT) is an emerging area of information and communications technology (ICT) involving many disciplines of computer science and engineering including sensors/actuators, communications networking, server platforms, data analytics and smart applications. IoT is considered to be an essential part of the 4th Industrial Revolution along with AI and Big Data.

Sep 21st 2026
5-12 Weeks
A Scientific Approach to Innovation Management (Coursera) Coursera
Università Bocconi

A Scientific Approach to Innovation Management (Coursera)

How can innovators understand if their idea is worth developing and pursuing? In this course, we lay out a systematic process to make strategic decisions about innovative product or services that will help entrepreneurs, managers and innovators to avoid common pitfalls. We teach students to assess the feasibility of an innovative idea through problem-framing techniques and rigorous data analysis labelled ‘a scientific approach’.

Sep 21st 2026
5-12 Weeks
Data Science for Business Innovation (Coursera) Coursera
Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Sep 21st 2026
4 Weeks
Data Science Ethics (Coursera) Coursera
University of Michigan

Data Science Ethics (Coursera)

What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values.

Sep 21st 2026
4 Weeks
Data Science Math Skills (Coursera) Coursera
Duke University

Data Science Math Skills (Coursera)

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.

Sep 24th 2026
4 Weeks
Bayesian Statistics: Mixture Models (Coursera) Coursera
University of California, Santa Cruz

Bayesian Statistics: Mixture Models (Coursera)

Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.

Sep 21st 2026
5-12 Weeks
VLSI CAD Part II: Layout (Coursera) Coursera
University of Illinois at Urbana-Champaign

VLSI CAD Part II: Layout (Coursera)

A modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of the chip, and refines it step-wise to a final design.

Sep 21st 2026
5-12 Weeks