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To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.

How do these MOOCs or free online courses work?

MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.

1 out of 1 people found the following review useful

2 years ago

(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 co
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(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.

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.

5 out of 5 people found the following review useful

2 years ago

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 stud
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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!

4 out of 4 people found the following review useful

2 years ago

Pro: If you watch the videos, read the material, and do the exercises, you will emerge with a working understanding of statistics foundations (normal distribution, Student's t-distribution, Monte Carlo simulations, etc.) and R.
Con: The instructors were sometimes very sloppy in their explanations; they tended to use h
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Pro: If you watch the videos, read the material, and do the exercises, you will emerge with a working understanding of statistics foundations (normal distribution, Student's t-distribution, Monte Carlo simulations, etc.) and R.

Con: The instructors were sometimes very sloppy in their explanations; they tended to use hard-to-grasp lingo in the videos and even in the exercises. Between the forums and the exercise explanations, however, I was able to *eventually* understand the exercises that were poorly worded initially.

Con: The instructors were sometimes very sloppy in their explanations; they tended to use hard-to-grasp lingo in the videos and even in the exercises. Between the forums and the exercise explanations, however, I was able to *eventually* understand the exercises that were poorly worded initially.

2 out of 4 people found the following review useful

3 years ago
**completed** this course, spending **3 hours** a week on it and found the course difficulty to be **medium**.

The instructor was very good. The material was presented in a logical manner. Nice sample R code with explanations. Etc....

2 years ago
**partially completed** this course, spending **10 hours** a week on it and found the course difficulty to be **medium**.

I have a background in computer science but none in statistics. I began to get lost in the part about t-tests. This was basic statistical information, so someone with that background would be good to go. To get through the first quarter of the course I had to do a lot of googling for how to work with R, which was fine and helped me learn R.

4 months ago
is taking this course right now.

I took intro to stats two years ago, now I'm facing econometrics so needed to learn R and brush up on stats. So far (into Week 3) it is a good course for that. There is a fair amount of puzzling things out for yourself, but that's probably a good thing, too. It is a tremendous value for the price ;-)

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

0 out of 5 people found the following review useful

1 out of 4 people found the following review useful

0 out of 4 people found the following review useful

0 out of 4 people found the following review useful