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This is NOT an introductory R course for beginners. The main problem of the course is that the lectures are too distant from the quizes and assignments. The smallest examples in the lectures were definitely not enough to complete the home tasks. The lecturer's attention is too much on some not so elementary R features which most beginners simply do not need (like optimization and to a less extent coercion) while really basic topics that appear the most challenging to beginner (and which are the most required to do ANYTHING AT ALL, like subsetting ) are not receiving enough time and EXAMPLES. The on- screen presentations are terrible in that they do not contain enough info to help you, e.g., when you are struggling with an assignment. Most of the time they look like just fragments of R code with NO COMMENTS WHATSOEVER. This course could be much improved if the presentation slides contain more info from what the lecturer actually talks ( in form of comments or as a superscripted handwriting, like I saw it in other courses). This course definitely needs more examples. The good examples should be based not on some rnorm() output, but on a single array of meaningful data, which should be analyzed throughout the course by means of different functions. It will help understanding and remembering different ways of data treatment. Previously I took Coursera's Interactive Programming in Python from Rice Uni, and that was really what a beginner's course should look like. I tried Coursera's Statistics One from Princeton, and while it has a demanding learning curve and underestimates time consumption on its page, Statistics One is free from most of the problems of CfDA and I will definitely retake it when available. I took this course in preparation for Coursera's Data Analysis from the same JHU, now I think of dropping them both.