In this course on Statistics and R online from Harvard, you’ll get an introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
About this course
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.
You 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. 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. You will ne able to use visualization techniques to explore new data sets and determine the most appropriate approach, also they 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. Course commence with the 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 makeup two Professional Certificates 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
- 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 from Statistics and R online from Harvard?
- Random variables
- Inference: p-values and confidence intervals
- Exploratory Data Analysis
- Non-parametric statistics
- Basic programming
- Basic math
Syllabus for Statistics and R online from Harvard
Introduction and Resources
- Welcome and Frequently Asked Questions
- Course Materials and R Resources
- Pre-Course Survey
- Getting Started
- Introduction to Exploratory Data Analysis
- Random Variables and Probability Distributions
- Central Limit Theorem
- The inference I: P-values, Confidence Intervals and Power Calculations
- Inference II: Monte Carlo Simulation, Permutation Tests and Association tests
- Exploratory Data Analysis
- Robust Summaries
- Data Analysis for Life Sciences Series
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- Harvard University
- Online Course
- 1-4 Weeks
- Free Course (Affordable Certificate)
- Bioinformatics Data Analysis Practical Statistics