An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
- 4 weeks long
- 2-4 hours per week
- Learn for FREE, Ugpradable
- Taught by: Rafael Irizarry, Michael Love
- View Course Syllabus
Online Course Details:
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.
What you’ll learn
- Random variables
- Inference: p-values and confidence intervals
- Exploratory Data Analysis
- Non-parametric statistics