Statistics and R

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This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.

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 code. 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 course materials 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. We start with simple calculations and descriptive statistics. 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 two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

• 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

Genomics Data Analysis:

• PH525.5x: Introduction to Bioconductor
• PH525.6x: Case Studies in Functional Genomics
• PH525.7x: Advanced Bioconductor

This class was supported in part by NIH grant R25GM114818.

What you will learn

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

Skills you learn

• Matrix Algebra
• Statistics
• Statistical Inference
• Life Sciences
• R (Programming Language)
• Biology
• Software Engineering
• Functional Genomics
• Data Warehousing
• Statistical Analysis
• Bioconductor (Bioinformatics Software)
• Linear Model
• Data Analysis
• Descriptive Statistics
• Confidence Intervals

Auto Summary

Dive into the world of statistical data analysis with the "Statistics and R" course, designed for IT and Computer Science enthusiasts. Led by edX, this 12-week course explores statistical inference, p-values, and confidence intervals using R programming. Ideal for life science professionals, the course offers seven self-paced modules, part of two Professional Certificates. Tailored for both beginners and seasoned statisticians, it covers everything from basic calculations to advanced concepts like hierarchical models and parallel computing. Enhance your data analysis skills and conduct reproducible research with this comprehensive program.