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.
What you'll learn:
- Random variables
- Inference: p-values and confidence intervals
- Exploratory Data Analysis
- Non-parametric statistics
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 Data Analysis for Life Sciences courses are self-paced and include:
- Statistics and R for the Life Sciences
- Introduction to Linear Models and Matrix Algebra
- Statistical Inference and Modeling for High-throughput Experiments
- High-Dimensional Data Analysis
- Introduction to Bioconductor: annotation and analysis of genomes and genomic assays
- High-performance computing for reproducible genomics
- Case studies in functional genomics