Data Analysis for Life Sciences 1: Statistics and R

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Data Analysis for Life Sciences 1: Statistics and R

Course Details

Cost

FREE

Upcoming Schedule

  • TBA

Course Provider

edX online courses
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Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

Provider Subject Specialization
Sciences & Technology
Business & Management
24425 reviews

Course Description

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 con...

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.

The courses in this series will be released sequentially each month 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.

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Reviews 8/10 stars
13 Reviews for Data Analysis for Life Sciences 1: Statistics and R

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Steven Frank profile image
Steven Frank profile image
6/10 starsDropped
  • 59 reviews
  • 57 completed
5 years, 1 month ago
I dropped this course. While the topics in statistics are well chosen and the problems interesting, particularly for those in the life sciences, the course itself had many problems. First, it is completely unstaffed, and I mean completely. This is problematic for any course, but particularly here, where the material was beset by technical problems. For example, a heavily used R function is unavailable in current R implementations, so you have to ignore a good chunk of the code patterns you're given and substitute new code -- not too hard for me, since I've programmed in R, but no fun for the less experienced. The discussion board included complaints about auto-graders rejecting correct answers. Etc. There were no staff responses. In terms of content, I found the lectures to ramble somewhat, and since the content is covered in chapters from the course textbook provided online, I started following that instead. But at least as ... I dropped this course. While the topics in statistics are well chosen and the problems interesting, particularly for those in the life sciences, the course itself had many problems. First, it is completely unstaffed, and I mean completely. This is problematic for any course, but particularly here, where the material was beset by technical problems. For example, a heavily used R function is unavailable in current R implementations, so you have to ignore a good chunk of the code patterns you're given and substitute new code -- not too hard for me, since I've programmed in R, but no fun for the less experienced. The discussion board included complaints about auto-graders rejecting correct answers. Etc. There were no staff responses. In terms of content, I found the lectures to ramble somewhat, and since the content is covered in chapters from the course textbook provided online, I started following that instead. But at least as presented, these chapters are often disjointed -- for example, discussing new topics as if they'd been previously introduced. When I found myself turning to Wikipedia for enlightenment, I decided it was time to bail.
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Michael Hunt profile image
Michael Hunt profile image

Michael Hunt

10/10 starsCompleted
5 years, 7 months ago
I thoroughly recommend this course and the others in the series (I have done the first three) for their rigour, focus on matrix algebra and pace. Not for complete beginners in R, unless you want to learn on the side (but, hey, if you want to, go for it!), but also genuinely useful and interesting for non-biologists, like me. Very, very good.
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Student

6/10 starsCompleted
5 years, 10 months ago
I know basic statistics principles, but difficult to figure out or get meaning of questions. This course doesn't teach fundamentals, funny mix of using R (not programming) and application that happen to involve statistics. I completed, but spent more than the indicated effort ( 2 - 4 hours/week), especially first few weeks.
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Andrew Chan profile image
Andrew Chan profile image

Andrew Chan

9/10 starsCompleted
5 years, 11 months ago
I'd rate this course content and the instructor's presentation among the top 3 of all Statistics courses I have taken whether online or in a traditional classroom setting.
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Flar Lazo profile image
Flar Lazo profile image

Flar Lazo

4/10 starsDropped
6 years ago
The topics dont get covered really well compared to the questions that are made in the assignments. Everything ok till week 3, then the questions and class become more complicated and had to dropped cause I didnt like the teaching. FYI, I've made a r programming in edx and another one in coursera, both way helpful than this one. You have to know really well statistics in order to dont get lost.
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student

6/10 starsCompleted
6 years, 1 month ago
Statistics are glossed over as if review. Not great as an introductory course. Great course for those already familiar with the statistics but limited exposure to R.
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Jaiyon Han profile image
Jaiyon Han profile image

Jaiyon Han

10/10 starsCompleted
6 years, 3 months ago
I love this course. Prof. Irizarry tries to help the student understand the concepts and R coding as easy as possible. I like the way Prof. Irizrry explain. All of quizzes are practical and useful. I strongly recommend someone who want to learn how to code R and statics concepts.
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timothy235 profile image
timothy235 profile image
10/10 starsCompleted
  • 12 reviews
  • 10 completed
6 years, 1 month ago
This is a good introduction to statistics at the college level.  While the course is intended for people entering the life sciences it can be taken by anyone interested in statistics.  Lab exercises use the R programming language.  The lectures are very good.  Recommended.
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Jeff Winchell profile image
Jeff Winchell profile image
7/10 starsCompleted
  • 91 reviews
  • 66 completed
6 years, 6 months ago
Fairly short practical oriented course on statistics which teaches you some R. I think the UT-Austin course on edX, Foundations of Data Analysis is probably a better way to learn statistics and R than this course. However, that course has numerous errors due to carelessness, and you don't get that with a course from Harvard. So you'll have to decide. Also, if you have less available time, this one takes half the time as the Texas one, (with the expected shortcuts in the amount you will learn and/or retain). This and the following 2 courses were part of a longer course PH525 previously offered. While it is nice that you can finish this course anytime within a 4 month window, I kind of would like to get my certificate as soon as I tell edX I'm finished (or I have answered all the possible assignments). As I finished a little late after the first deadline, now I need to wait 3 months.
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Student

8/10 starsTaking Now
6 years, 6 months ago
I have now been trying to learn R for about 3 weeks using this course, another in Coursera, and lastly from Code School (try R). I have a good programming background (C and others), and I use SAS extensively. So I don't really need the statistics (though review never huts), but I need to see how it is all done in R. I would have liked a chapter on programming where one discusses data types, and declaring variables. This would have avoided most of the error messages. That said, maybe I got more errors because I am using Rstudio on a PC, and other platforms take care of this issue in a different way. Overall this is a good course (I am in the middle of Week 3)
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Student

5/10 starsCompleted
6 years, 6 months ago
This course should be useful for those with a strong understanding of basic statistics, but without any knowledge of R. This course may be somewhat useful as a refresher for those who already have a background in statistics. For those who are interested in learning the material from the beginning this course is not helpful. Most of the topics are glossed over and the focus of the course is on answering questions using R rather than understanding the material. I supplemented this course with Sal's statistics lessons at Khan Academy, which focus on theory and understanding, but with little application, and got a lot more out of this experience than I would have otherwise. The professor makes a lot of mistakes and does not speak clearly. In a live classroom where a student can simply ask a clarifying question I'm sure his teaching style works very well, but on the web a student cannot slow the teacher down and the method of education su... This course should be useful for those with a strong understanding of basic statistics, but without any knowledge of R. This course may be somewhat useful as a refresher for those who already have a background in statistics. For those who are interested in learning the material from the beginning this course is not helpful. Most of the topics are glossed over and the focus of the course is on answering questions using R rather than understanding the material. I supplemented this course with Sal's statistics lessons at Khan Academy, which focus on theory and understanding, but with little application, and got a lot more out of this experience than I would have otherwise. The professor makes a lot of mistakes and does not speak clearly. In a live classroom where a student can simply ask a clarifying question I'm sure his teaching style works very well, but on the web a student cannot slow the teacher down and the method of education suffers for it. I would be interested in taking another course on statistics at edX, but not from this instructor or from the team responsible for developing this course.
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Student

10/10 starsCompleted
6 years, 6 months ago
Imagine yourself a high school student with little statistics or biology, but interested in genomics? You will be tutored
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Student

9/10 starsCompleted
6 years, 7 months ago
This was definitely a very useful course. I am a student of genetics and this will hopefully be very helpful for me when I soon began analysis of my own data. The course is quite well taught and the discussion board was very active making the experience that much better. I had very little background in the statistics beforehand but had completed some tutorials on DataCamp beforehand in R which I would recommend completing as a precursor. I found the learning curve very steep. The first week was a breeze but the difficulty seemed to almost increase exponentially. Like it has been suggested, trying some tutorials in R at DataCamp and some stats videos on Kahn Academy beforehand will set you up very well for success.
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