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.
Projet d'apprentissage appliqué
Throughout this Specialization, you’ll apply your skills to solve authentic data challenges using real-world datasets and industry-standard tools such as R, RStudio, Tidyverse, Quarto, and Git/GitHub. You’ll gain hands-on experience transforming raw data into meaningful insights, crafting compelling visualizations, and producing reproducible analyses that can be shared confidently with others. Each course emphasizes practical applications, from cleaning and preparing messy datasets to exploring ethical dilemmas in data usage, such as algorithmic bias and privacy concerns. By working through programming companions and guided exercises, you’ll develop the technical and ethical expertise to tackle real-world data problems and communicate results that inform decisions in any professional or research context.
