- Provider edX
- Cost Free Online Course (Audit)
- Session Self Paced
- Language English
- Certificate $99 Certificate Available
- Effort 2-4 hours a week
- 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.
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Reviews for edX's Statistics and R Based on 20 reviews
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- 4 stars 35%
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- 2 star 5%
- 1 stars 15%
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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?
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…
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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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Tutor gave the example which will not use in the exercise, so you will get lost easily.
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.
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