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

Duke University

Inferential Statistics (Coursera)

Statistics & Data Analysis Data Science

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.

Oct 26th 2026

5-12 Weeks

Statistics Statistical Inference Inferential Statistics

Coursera

University of California, Davis

Data Visualization with Tableau Project (Coursera)

Statistics & Data Analysis Data Science

In this project-based course, you will follow your own interests to create a portfolio worthy single-frame viz or multi-frame data story that will be shared on Tableau Public. You will use all the skills taught in this Specialization to complete this project step-by-step, with guidance from your instructors along the way.

Nov 2nd 2026

5-12 Weeks

Data Analysis Data Visualization Tableau

Coursera

Northeastern University

Healthcare Information Design and Visualizations (Coursera)

Statistics & Data Analysis Data Science

Introduces processes and design principles for creating meaningful displays of information that support effective business decision-making. Studies how to collect and process data; create visualizations (both static and interactive); and use them to provide insight into a problem, situation, or opportunity. Introduces methods to critique visualizations along with ways to answer the elusive question: “What makes a visualization effective?”

Nov 2nd 2026

4 Weeks

Healthcare Storytelling Data Analysis

Coursera

Genentech

Making Data Science Work for Clinical Reporting (Coursera)

Statistics & Data Analysis Data Science

This course is aimed to demonstrate how principles and methods from data science can be applied in clinical reporting. By the end of the course, learners will understand what requirements there are in reporting clinical trials, and how they impact on how data science is used. The learner will see how they can work efficiently and effectively while still ensuring that they meet the needed standards.

Nov 2nd 2026

4 Weeks

Github Clinical Trials Data Science

Coursera

Knowledge Accelerators

From Excel to Power BI (Coursera)

CS: Information & Technology

Are you using Excel to manage, analyze, and visualize your data? Would you like to do more? Perhaps you've considered Power BI as an alternative, but have been intimidated by the idea of working in an advanced environment. The fact is, many of the same tools and mechanisms exist across both these Microsoft products. This means Excel users are actually uniquely positioned to transition to data modeling and visualization in Power BI! Using methods that will feel familiar, you can learn to use Power BI to make data-driven business decisions using large volumes of data.

Nov 2nd 2026

1 Week

Excel Data Analysis Microsoft Excel

Coursera

The Hong Kong University of Science and Technology - HKUST

Python and Statistics for Financial Analysis (Coursera)

Economics & Finance Business

Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

Nov 2nd 2026

4 Weeks

Python Statistics Inference

Coursera

Leiden University

Population Health: Predictive Analytics (Coursera)

Statistics & Data Analysis Data Science

Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. Then, we look at key concepts such as study design, sample size and overfitting.

Oct 26th 2026

4 Weeks

Health Data Analysis Predictive Analytics

Coursera

École Polytechnique Fédérale de Lausanne

Functional Programming in Scala Capstone (Coursera)

Statistics & Data Analysis Data Science

In the final capstone project you will apply the skills you learned by building a large data-intensive application using real-world data. You will implement a complete application processing several gigabytes of data. This application will show interactive visualizations of the evolution of temperatures over time all over the world.

Nov 2nd 2026

5-12 Weeks

Interface Scala Interactive

Coursera

Institut Mines-Telecom

Data intelligence for businesses and managers (Coursera)

Statistics & Data Analysis Data Science

With the proliferation of connected objects (computers, tablets, watches, etc.), huge masses of data are generated every second. This Big Data has led to the emergence of a data economy, where data is the main source of competitive advantage for companies. In this sense, data and its processing tools have become a strategic priority for companies, and the main gas pedal of their digital transformation.

Nov 2nd 2026

5-12 Weeks

Artificial Intelligence Data Management Business Strategy

Coursera

University of Michigan

Applied Plotting, Charting & Data Representation in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework.

Nov 2nd 2026

4 Weeks

Python Data Analysis Data Science

Coursera

Erasmus University Rotterdam

Econometrics: Methods and Applications (Coursera)

Statistics & Data Analysis Data Science

Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making.

Nov 2nd 2026

5-12 Weeks

Econometrics Data Analysis Linear Regression

Coursera

Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

Statistics & Data Analysis Data Science

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Oct 26th 2026

4 Weeks

Algebra Linear Algebra Data Science