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Statistics and R

Harvard University via edX

students interested
  • Provider edX
  • Subject Data Science
  • $ Cost Free Online Course
  • Session Self Paced
  • Language English
  • Certificate $99 Certificate Available
  • Effort 2-4 hours a week
  • Start Date
  • Duration 4 weeks long

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Overview

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.

Taught by

Michael Love and Rafael Irizarry

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Reviews for edX's Statistics and R
3.6 Based on 18 reviews

  • 5 stars 22%
  • 4 stars 39%
  • 3 stars 22%
  • 2 star 6%
  • 1 stars 11%

Did you take this course? Share your experience with other students.

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  • 1
Brandt P
4.0 2 years ago
by Brandt 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 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 in…
4 people found
this review helpful
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Adelyne C
4.0 3 years ago
by Adelyne 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 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!
7 people found
this review helpful
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Chris F
4.0 3 years ago
by Chris 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 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.
6 people found
this review helpful
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Ayse N
1.0 5 months ago
Ayse partially completed this course.
The way this course is taught feels pretty sloppy; it is easy to feel lost. They teach one way, and the answers they provide for some exercises is written in a completely different way they have never taught.

To be able to understand some things, you need to already know a bit about the topic.

Also the way they name variables is quite cringe-worthy, in some place they name a variable "X", another variable is "x"; since R is case sensitive, no need to worry, right?
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Max P
3.0 2 years ago
by Max 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.
1 person found
this review helpful
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Anonymous
4.0 a year ago
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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Robert G
5.0 4 years ago
Robert 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....
4 people found
this review helpful
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Anonymous
1.0 a month ago
Anonymous partially completed this course.
Course is not organised well, neither instructor doesn't explain material in depth. Videos are almost useless, many terms used in the them without explaining; for example: hypergeometric distribution
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Anonymous
2.0 2 months ago
Anonymous audited this course.
Not suitable for the beginners.

Tutor gave the example which will not use in the exercise, so you will get lost easily.
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Jinwook J
3.0 3 years ago
by Jinwook completed this course.
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Colin K
4.0 3 years ago
by Colin completed this course.
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Davide M
4.0 2 years ago
by Davide completed this course.
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Piotr D
5.0 2 years ago
by Piotr completed this course.
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Raphael R
5.0 2 years ago
by Raphael audited this course.
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Rafael P
3.0 3 years ago
Rafael completed this course.
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James W
5.0 3 years ago
James completed this course.
1 person found
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Alun R
4.0 3 years ago
Alun audited this course.
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Matteo F
3.0 3 years ago
by Matteo completed this course.
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  • 1

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