subject
Intro

edX: Statistics and R

 with  Michael Love and Rafael Irizarry

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics


This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

15 Student
reviews
Cost Free Online Course
Pace Self Paced
Subject Data Science
Institution Harvard University
Provider edX
Language English
Certificates $99 Certificate Available
Hours 6 hours a week
Calendar 35 weeks long

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15 reviews for edX's Statistics and R

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1 out of 1 people found the following review useful
2 years ago
Brandt Pence completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
(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 Read More
(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.
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5 out of 5 people found the following review useful
2 years ago
Adelyne Chan completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
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 Read More
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!
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4 out of 4 people found the following review useful
2 years ago
Chris Falter completed this course.
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 Read More
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.
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2 out of 4 people found the following review useful
3 years ago
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Robert Grutza 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....
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2 years ago
Max Pietsch 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.
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4 months ago
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Anonymous 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 ;-)
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0 out of 1 people found the following review useful
2 years ago
Jinwook completed this course.
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2 years ago
Colin Khein completed this course.
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a year ago
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9 months ago
Raphael Rivero audited this course.
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0 out of 5 people found the following review useful
2 years ago
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Rafael Prados completed this course.
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1 out of 4 people found the following review useful
3 years ago
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James Warren completed this course.
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Matteo Ferrara completed this course.
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