Coursera

Introduction to R Programming and Tidyverse (Coursera)

Offered by University of Colorado Boulder,

This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if: you want to do data analysis but don’t know programming; you know programming but aren’t familiar with R; you know some R programming but want to learn the tidyverse verbs. You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood. You will use RMarkdown to create nice documents and reports that execute your code freshly every time it’s run and that capture your thoughts about the data along the way.

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

This course has been designed for learners from non-STEM backgrounds to help prepare them for more advanced data science courses by providing an introduction to programming and to the R language. I am excited for you to join me on the journey!

What You Will Learn

By successfully completing this course, you will be able to write functions in R.
You will be able to analyze and visualize a data set.
You will be able to use RMarkdown to share documents in reports with the global R community.

Syllabus

WEEK 1
Introduction to R, RStudio and RMarkdown
In the first module of this course, you will install and configure R and RStudio. You will review the fundamentals of R and reproducibility, install R packages required for the course, and input basic commands using the RStudio console. Finally, you will create an RMarkdown document - the deliverable for this module.

WEEK 2
Functions
In this module, we will explore functions in R. You will review the syntax of functions and best practices of function creation. You will also practice writing functions with default arguments and argument validation.

WEEK 3
Data Visualization using ggplot2
In this module, you will be introduced to ggplot2 - an R package for data visualization. You will explore the different grammatical elements and aesthetic mappings (layers) that are essential to visualize data in ggplot2.

WEEK 4
Data Analysis with dplyr
In the final module of this course, you will be introduced to data analysis using dplyr. You will learn and practice with the many dplyr verbs including select, filter, arrange, mutate, group_by, and summarize.

Go to Class

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

Related Courses

Coursera

Johns Hopkins University

Exploratory Data Analysis (Coursera)

Statistics & Data Analysis Data Science

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.

Jun 22nd 2026

4 Weeks

Statistics Data Analysis Data Science

Coursera

University of California, Davis

Fundamentals of GIS (Coursera)

CS: Information & Technology Sci: Physical & Earth Sciences

Explore the world of spatial analysis and cartography with geographic information systems (GIS). What you will learn: define core geospatial concepts; practice with subset data using selections and feature attributes; create map books using advanced mapping techniques; create layer and map packages.

Jun 22nd 2026

4 Weeks

Data Analysis Geographic Information Systems GIS

Coursera

University of Illinois at Urbana-Champaign

Text Retrieval and Search Engines (Coursera)

Statistics & Data Analysis Data Science

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.

Jun 22nd 2026

5-12 Weeks

Machine Learning Search Data Mining

Coursera

Johns Hopkins University

Mathematical Biostatistics Boot Camp 1 (Coursera)

Sci: Mathematics

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

Jun 22nd 2026

4 Weeks

Math Statistics Probability

Coursera

Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 22nd 2026

4 Weeks

Statistics Probability Data Analysis

Coursera

Johns Hopkins University

The Data Scientist's Toolbox (Coursera)

Statistics & Data Analysis Data Science

In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Jun 22nd 2026

4 Weeks

Data Github Data Analysis

Coursera

University of Colorado Boulder

Introduction to Data Analytics for Business (Coursera)

Statistics & Data Analysis Data Science

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Jun 22nd 2026

4 Weeks

Business SQL Data Modeling

Coursera

Johns Hopkins University

Principles of fMRI 1 (Coursera)

Statistics & Data Analysis

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data, including psychological inference, MR Physics, K Space, experimental design, pre-processing of fMRI data, as well as Generalized Linear Models (GLM’s).

Jun 22nd 2026

4 Weeks

Medicine Statistics Data Analysis

Coursera

Rice University

Introduction to Data Analysis Using Excel (Coursera)

Statistics & Data Analysis

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This course is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills.

Jun 22nd 2026

4 Weeks

Spreadsheets Excel Data Analysis

Coursera

University of Minnesota

Interprofessional Healthcare Informatics (Coursera)

Statistics & Data Analysis Data Science

Interprofessional Healthcare Informatics is a graduate-level, hands-on interactive exploration of real informatics tools and techniques offered by the University of Minnesota and the University of Minnesota's National Center for Interprofessional Practice and Education. We will be incorporating technology-enabled educational innovations to bring the subject matter to life. Over the 10 modules, we will create a vital online learning community and a working healthcare informatics network.

Jun 22nd 2026

5-12 Weeks

Healthcare Informatics Telehealth

Coursera

University of California, Santa Cruz

Bayesian Statistics: From Concept to Data Analysis (Coursera)

Statistics & Data Analysis Data Science

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach.

Jun 22nd 2026

4 Weeks

Statistics Probability Data Analysis

Coursera

Johns Hopkins University

Practical Machine Learning (Coursera)

Statistics & Data Analysis Data Science

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

Jun 22nd 2026

4 Weeks

Algorithms Machine Learning Regression