# Statistics and R

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Add a Verified Certificate for $129

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Image

Duration

4 weeks long

Time Commitment

2 - 4 hours per week

Pace

Self-paced

Subject

Course Language

English

English

Intermediate

Credit

Audit for Free

Add a Verified Certificate for $129

Add a Verified Certificate for $129

Platform

edX

Random variables

Distributions

Inference: p-values and confidence intervals

Exploratory Data Analysis

Non-parametric statistics

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.

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