Popular Duke Statistics MOOC Expands Into Multi-Course Specialization
In today’s increasingly data-centric world, statistics has become an in-demand skill sought by employers in multiple industries. Mine Çetinkaya-Rundel, assistant professor of the practice in the Department of Statistical Science at Duke University, experienced this first-hand when she launched an online statistics course on Coursera in 2014. Over 200,000 individuals from around the world, from […]
In today’s increasingly data-centric world, statistics has become an in-demand skill sought by employers in multiple industries. Mine Çetinkaya-Rundel, assistant professor of the practice in the Department of Statistical Science at Duke University, experienced this first-hand when she launched an online statistics course on Coursera in 2014. Over 200,000 individuals from around the world, from high school students to Ph.Ds, enrolled in three sessions of the course. And they wanted more.
“One of the topics people wanted to hear more about was Bayesian statistics,” Çetinkaya-Rundel said, referring to the particular field of statistics in which she, and Duke’s statistics department generally, specializes. She was happy to oblige, and worked with Online Duke to create a multi-course Coursera Specialization that expands her original course into four shorter courses and a capstone project.
“One big motivation was to tell a more complete story of a good foundation of statistics,” she said. The Specialization, titled Statistics with R, adds more information on Bayesian statistics and also teaches students to use R, a widely-used data analysis programming language. Three additional faculty in the statistics department, David Banks , Merlise Clyde and Colin Rundel, were brought in to help teach the new content.
Coursera offers several other courses on statistics and R, but the Duke Specialization is unique in its accessibility for statistics novices.
“The mathematical background required for the course is not very high, and this was intentional in our course design, since it is difficult to find introductory Bayesian material at that level,” Çetinkaya-Rundel said. The Specialization also “truly starts at step one with R,” she said. This makes it well-suited for a number of audiences, including individuals with advanced degrees in technical subjects like software engineering that want to expand their skill set and high school teachers who want to see how someone else teaches introductory statistics.
Since the material covered in the courses is introductory, Çetinkaya-Rundel sees it as a sort of teaser for the statistics-curious. “I hope first of all that they’ll leave the course wanting to learn more,” she said. “We also emphasize doing data analysis reproducibly, so beyond the statistical methodology, I hope they walk away with an appreciation of how important that is.”
The first course in the Specialization, Introduction to Probability and Data, launched in April 2015, and the second course, Inferential Statistics, launched May 9. The remaining courses will launch at intervals over the next few months. Individual courses can be audited for free or cost $79 to receive a grade and shareable certificate; learners can enroll in the entire Specialization for $355. Financial aid is available through Coursera for learners who cannot afford the course fees. More information and registration is available at Coursera.