Statistics and R
Intermediate 16 hours English Verified CertificateFREE
What you'll learn
Use R to analyse life-sciences datasets
Apply the central limit theorem and t-distribution
Run inference: confidence intervals, p-values, hypothesis tests
Perform exploratory data analysis with R plots
Address multiple testing in genomics data
Use Monte Carlo simulation to understand sampling distributions
Reason about statistical models in research papers
This course includes
16h
On-demand video
20+
R programming labs
8
Graded assessments
Optional
Verified certificate
Comparison · LBS
Compare alternatives for Python
Same topic, different providers. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
eXedX4.5(6,400)
Statistics and R
- Price
- FREEAudit free · $199 cert
- Duration
- 16 hrs
- Level
- Intermediate
- Certificate
- Verified
CCoursera4.6(102K)
IBM Data Science Professional Certificate
- Price
- FREEAudit free · Cert $49/mo
- Duration
- 110 hrs
- Level
- Beginner
- Certificate
- Professional
CCoursera4.9(78,000)
Machine Learning Specialization
- Price
- FREEAudit free · Cert $49/mo
- Duration
- 94 hrs
- Level
- Intermediate
- Certificate
- Professional
Prices and ratings refreshed daily. We're not affiliated with any single provider.
Syllabus· 4 courses · 15+ lessons
- Motivation: why statistics matters in life sciences
- Random variables
- Null distributions
- Probability distributions
- Central Limit Theorem
- t-distribution in practice
- Confidence intervals
- Power calculations
- QQ plots and histograms
- Boxplots and scatterplots
- Robust summaries
- EDA in R with ggplot2
- Wilcoxon and permutation tests
- Multiple testing and FDR
- Genomics case study
Instructor
RI
Rafael Irizarry
Professor of Biostatistics, Harvard T.H. Chan School of Public Health
1.2M learners8 courses 4.7 instructor rating
Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute. He is the author of "Introduction to Data Science" and known for foundational work in genomics. He has taught statistics on edX since 2014.
Requirements
- College-level algebra
- Basic computer literacy
- Willingness to install R and RStudio (free)
- About 4 hours per week for 4 weeks
Who this course is for
- Life-science researchers needing rigorous stats
- Bioinformatics and genomics analysts
- Data scientists wanting a Harvard-led stats grounding
- Graduate students before a methods course
About this provider
eX
edX
University-backed online learning platform · 142M learners · 7,000+ courses
4.6 trust score·Refund within 14 days
Frequently asked questions
Harvard's version (Irizarry) is more rigorous and biology-flavoured; Duke's is broader and gentler. Pick Harvard if you work in life sciences; pick Duke for a general stats refresher.
For most learners no — the content is free. Pay only if you need the verified credential for grad school applications or employer reimbursement.
No, R is introduced as you go. Familiarity with any programming language helps. The labs are well-scaffolded.
Different goals: Andrew Ng teaches predictive modeling in Python; this course teaches classical inferential statistics in R. Researchers should take this; engineers building ML systems should take Andrew Ng.
OpenIntro Statistics (free book + videos), or DataCamp's free Introduction to Statistics in Python track. Both cover similar concepts without R.