Overview

Master the essentials of data science with the Data Science with R Specialization. Designed for beginners and professionals, this series provides the foundational skills to transform, visualize, and ethically analyze data with the R programming language. Whether you're exploring a career in data analysis, expanding your professional toolkit, or seeking to understand how your data analysis choices touch on ethics, this Specialization equips you with the technical and practical skills to thrive in today’s data-driven world. With no prior programming experience needed, this series is accessible to learners with diverse educational and work backgrounds, including analysts, researchers, and aspiring data scientists. Through three courses, you’ll explore foundational topics in data science, including data visualization, statistical analysis, and advanced data transformation. You'll gain hands-on experience with real-world datasets, mastering doing data science with R and essential tools like Tidyverse, RStudio, Quarto, Git, and GitHub. The learning experience emphasizes practical application, offering engaging live-coding experiences and guided exercises to build confidence and competence in data science workflows. 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
- Offered by Duke University. This course is an introduction to data science and statistical thinking. Learners will gain experience with ... Enroll for free.

Course 2: Data Tidying and Importing with R
- Offered by Duke University. This course aims to better develop your statistical toolkit in the world of statistics and data science. You ... Enroll for free.

Course 3: Data Science Ethics with R
- Offered by Duke University. This course highlights the ethical responsibilities we have as statisticians and data scientists when working ... Enroll for free.

Courses

0 reviews
12 hours 40 minutes
View details

This course is an introduction to data science and statistical thinking. Learners will gain experience with exploring, visualizing, and analyzing data to understand natural phenomena and investigate patterns, model outcomes, and do so in a reproducible and shareable manner. Topics covered include data visualization and transformation for exploratory data analysis. Learners will be introduced to problems and case studies inspired by and based on real-world questions and data via lecture and live coding videos as well as interactive programming exercises. The course will focus on the R statistical computing language with a focus on packages from the Tidyverse, the RStudio integrated development environment, Quarto for reproducible reporting, and Git and GitHub for version control. The skills learners will gain in this course will prepare them for careers in a variety of fields, including data scientist, data analyst, quantitative analyst, statistician, and much more.
0 reviews
12 hours 49 minutes
View details

This course aims to better develop your statistical toolkit in the world of statistics and data science. You will learn how to collect, manipulate, and transform data in R into a more readily usable format using tidyverse data pipelines, primarily using verbs from the dplyr and tidyr packages. The topics covered provide you with the tools necessary to convert data to be better suited for data visualization (Course 1) and modeling; which is to come in this certificate program in a future course. Additionally, we discuss the topics of web scraping and the considerations one must take prior to scraping data from the web.
0 reviews
6 hours 21 minutes
View details

This course highlights the ethical responsibilities we have as statisticians and data scientists when working with data. This course demonstrates situations where ethical concerns with data arise and helps train our brains to be more aware of how data are used and the intent behind data collection. By the end of this course, you will be able to identify misrepresentation in visualizations, describe the basics of data privacy, and recognize potential situations where algorithmic bias is at play.