Data Analysis and Statistical Inference
School: Duke University
Registration Link:Data Analysis and Statistical Inference
Start Date: September 1, 2014 (10 Weeks)
Workload: 8-10 Hours Per Week
Instructor: Dr. Mine Çetinkaya-Rundel
Credentials: Dr. Çetinkaya-Rundel teaches data analysis and statistical consulting courses to undergrads at Duke University. The co-author of OpenIntro Statistics, Çetinkaya-Rundel is also the co-editor of the Citizen Statistician blog and produces a monthly column in Chance magazine. She earned her Ph.D. at UCLA, where she also taught from 2009-2011.
Graded: Students can earn a Certificate of Achievement for meeting basic requirements or a Verified Certificate for completing additional coursework. Students who earn a Verified Certificate are also eligible to receive a Specialization Certificate in Reasoning, Data Analysis and Writing after taking a series of MOOC courses in this field.
Description: In this course, students will learn how to collect, analyze, and apply data “to make inferences and conclusions about real world phenomena.” An introductory course, it focuses on traditional methods for conducting research, formulating and testing hypotheses, designing studies, identifying relationships between variables, comparing data sets, and making decisions based on probabilities. It will also cover more recent concepts like visualizations and simulations, with an emphasis on making conclusions reproducible. Students will learn from 5-10 minute video lectures, coupled with quizzes and exercises. To earn a Verified Certificate, students must complete a capstone project (an analysis of real data sets) and successfully pass a midterm and final exam.
Review: “This is an excellent course on introductory statistics! Dr. Çetinkaya-Rundel presents the subject clearly and provides excellent online learning resources. The format of the labs (datacamp) is extremely helpful for learning R for someone with very little experience in the subject (such as myself).
This course is very fast-paced and rigorous and the student will need to invest a significant amount of time and energy in it if they wish to earn a certificate. But I would highly recommend it, it is by far the best online resource I’ve found for learning statistics.” For additional reviews, click here.
Additional Notes: Students will need to access R “(an online environment for statistical computing and graphics) and RStudio (an integrated development environment that serves as a user interface for R).” Çetinkaya-Rundel also advises students to use DataCamp, an interactive platform that houses lab exercises from this course in the R environment. She also encourages students to access her textbook, which is available at no cost online at OpenIntro.