Harvard University offers course on Statistics and R for life Sciences

Harvard University is conducting an online course on "Data Analysis for Life Sciences 1: Statistics and R". The courses focuses on the introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.


About The Course:

In the course you will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analysing data with R. The course provides 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. During the course 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.

Statistics and R for life Sciences: Online course

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.

  • 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


Candidate should have a knowledge of basic programming and basic math

Course Syllabus:

  • Random variables
  • Distributions
  • Inference: p-values and confidence intervals
  • Exploratory Data Analysis
  • Non-parametric statistics

Course Details:

  • Course length: 4 weeks
  • Course starts on: October 15

For more information on the course visit the website

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