This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
About this Course
Learner Career Outcomes
Learner Career Outcomes
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
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TOP REVIEWS FROM INTRODUCTION TO PROBABILITY AND DATA WITH R
I always wanted to learn statistics from scratch, but I never had a good university teacher. Here I found a good teacher and also the opportunity to learn whenever I want ( and skipping parts I knew!)
After trying several courses to get me started with R programming, this one came to the rescue and had all the info I wanted. It also provides a great way to practice through labs and a final project!
This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.
A good introductory course to data analysis, statistics and the R programming language. Recommended to people who are new to data analysis and also those who are experienced and could use a refresh.
About the Statistics with R Specialization
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
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