Statistics and R

A wonderfully presented course which is a part of a larger series of 8 related courses, this course covered the basics of using R and a general overview of statistics. Course material is released every week, but all the quizzes were due about 4 months after the course actually started, which allows flexibility for students. I also liked the way in which R MarkDown scripts were provided for each lecture, and working through the scripts (in text form) really reinforced the concepts covered in the video lectures. The exercises usually built on these as well, although I felt that the wording for some of the questions were quite dubious - often a lot of the time I spent on this course was figuring out what the question was asking rather than actually working on getting a solution!

(Note, I took this before the reorganization of the courses. I believe the material in the first two-three courses remains the same, so my comments should still be valid here.)

This is the first course in the PH525 sequence offered by HarvardX on the EdX platform. The sequence is taught by Rafael Irizarry, a noted computational biologist at Harvard and the Dana Farber Cancer Center. The course offers a relatively gentle introduction to biostatistics, and there's little emphasis on genomic analyses here. Topics that are covered include probability, the normal distribution, some inferential statistics (T-tests, confidence intervals, power calculations, association tests, and simultation), and exploratory data analysis.

The introduction to R is rather cursory, and I have to imagine that the homework assignments might be challenging for those unfamiliar with the language, although there is a fair bit of handholding for the most difficult parts. For those that have taken R Programming, as I had, this course will seem very easy. I took it during a self-paced period and finished the entire course in a little more than three days, working only a few hours a night and a bit here and there during free periods at work, and I don't think I spent much more than 10-20 minutes on any of the programming problems.

There is some value to be had here even for those with experience in R, though. The basic introduction of actual statistical tests in this course is likely to give students taking the statistical inference courses in the Data Science and Genomic Data Science specializations a bit of a head start. The section on dplyr, a powerful method of splitting datasets and performing operations on their contents in a more intuitive way than in base R, is also reasonably good. Additional follow-up courses are available for matrix operations and advanced statistics.

Overall, four stars. The actual instruction in programming in R is a bit slim here, but for those with experience with the language but with little experience on the statistics side of R (which would describe most everyone currently taking or having recently taken R Programming), there is a lot of value here for little effort. The EdX platform is not as nice as Coursera's, especially when it comes to the discussion boards, but this doesn't detract much from the course.

The instruction videos are very sloppy, and the text book and other resources are not very helpful as well. The exercises have questions on topics that have either not been discussed or very poorly described. Additionally, the language of the questions in the exercises is sometimes very ambiguous and left me confused about what was being asked or wondering if there was a question in there at all. I don't think that I'll be continuing with this course. Money down the drain I guess. Do not waste your time by taking this course!

Note: I hold in doctorate degree in biological sciences and have extensive experience in carrying out statistical analysis using both R and Python. I wanted to take this course to have some sort of certification.