Overview

Master the essentials of data science with the Data Science with R Specialization. Designed for beginners and professionals alike, this four-course series builds the foundational and applied skills needed to transform, visualize, model, and ethically analyze data using the R programming language. Whether you are exploring a career in data analysis, expanding your professional toolkit, or seeking to understand how analytical choices influence real-world outcomes, this Specialization equips you with the confidence and technical fluency to thrive in today’s data-driven world. Through four hands-on courses, you’ll progress from exploring and visualizing data to cleaning and transforming messy datasets, applying ethical reasoning, and modeling relationships to make predictions. You’ll master core tools of modern data science (including R, Tidyverse, RStudio, Quarto, Git, and GitHub) while developing the practical and ethical mindset to use them responsibly. ws. By the end of the series, you’ll be able to confidently tidy and transform data, create compelling visualizations, communicate insights that drive decisions, and apply ethical principles to address algorithmic bias, data privacy, and misrepresentation in your analyses.

Syllabus

Course 1: Data Visualization and Transformation with R
Course 2: Data Tidying and Importing with R
Course 3: Data Science Ethics with R
Course 4: Data Modeling and Prediction with R

Courses

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12 hours 55 minutes
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Learn the foundations of data science by exploring, transforming, and visualizing data with R. In this course, you’ll develop core skills in exploratory data analysis and statistical thinking including: using visualizations to uncover patterns, identifying trends, and generating insights. You’ll gain hands-on experience with Tidyverse packages in R, work in RStudio, and create reproducible reports with Quarto. Along the way, you’ll also learn version control practices with Git and GitHub to document and share your work. By the end of this course, you’ll be able to transform and summarize data, craft clear and informative graphics, and communicate your findings through professional, reproducible workflows - laying the groundwork for all your future data science projects.
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13 hours 4 minutes
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Build confidence working with messy, real-world data. In this course, you’ll learn how to import, clean, and organize data in R so that it’s ready for analysis, visualization, or modeling. Using dplyr, tidyr, and other Tidyverse tools, you’ll practice joining datasets, reshaping data, and creating efficient data pipelines that support reproducible work. You’ll also explore how to responsibly collect and scrape data from online sources, including ethical and legal considerations. By the end of this course, you’ll know how to transform raw datasets into structured, tidy formats and you’ll understand how responsible data handling and documentation are essential to high-quality, ethical data science.
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6 hours 36 minutes
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Develop the ethical mindset every data scientist needs. In this course, you’ll examine the real-world implications of how data are collected, analyzed, and presented and the role of ethics in ensuring fairness, transparency, and trust. Through examples and case studies, you’ll learn to recognize misrepresentation in visualizations, algorithmic bias in models, and privacy risks in data collection. You’ll also explore strategies for mitigating these challenges and communicating results responsibly. By the end of this course, you’ll be able to identify ethical risks, apply frameworks for responsible data use, and make informed choices that uphold integrity in your analyses.
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11 hours
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Learn how to move from exploring data to modeling it with confidence. In this course, you’ll build and interpret linear and logistic regression models in R to uncover relationships, make predictions, and quantify uncertainty. You’ll begin by learning how to fit and interpret simple and multiple linear regression models, then advance to modeling categorical outcomes with logistic regression. Finally, you’ll explore bootstrapping and hypothesis testing to understand and communicate the uncertainty in your results. By the end of this course, you’ll be able to use statistical modeling to make and explain data-driven decisions – an essential skill for data scientists, analysts, and anyone working with real-world data.