Technological advancements in high throughput experimentation and next generation sequencing have made it such that biological research is moving at a pace much faster than it has seen in history. Researchers must work with increasinly large volumes of data in order to advance their respective fields. In addition, journals associated with prestige and career success increasingly require large volumes of data, or use of advanced technologies to to satisfy reviewers and editors of those journals. As a result, computational methods are often required in order to make career-progressing discoveries. Many individuals that enter biological research are not required to have background in computation or statistics, both required in some level of competency in order to process the volume of data being created. The Molecular Biology Institute at UCLA sought to create a required course for graduate students in the biosciences to grant experience with both computer programming and intermediate statistical methods used in biological research. This repository contains the data and code used for instruction for this course.
The lessons for the course are written in R markdown and saved in the instruction materials folder.
This course covers: R basics with data types and structures Data visualization and plotting Useful statistical tools (linear modeling, randomization, clustering) Working with multidimensional data