Harvard University - Statistics and R

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Statistics and R
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Overview

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

Duration	13 hours
Start from	Start Now
Total fee	Free
Mode of learning	Online
Schedule type	Self paced
Difficulty level	Beginner
Official Website	Explore Free Course
Credential	Certificate

Statistics and R
at
edX
Highlights

SKILLS YOU WILL GAIN: Management Theory, Strategic Management, Leadership, Management
Learn from expert professor of Harvard University
Get a certificate for Rs. 19097 upon successful completion
Self paced Course

Statistics and R
at
edX
Course details

Skills you will learn

R Statistics Data visualization Data analysis Data Science

More about this course

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.