Make sense of statistics and ML, for real.
From p-values to gradient boosting to causal inference: the methods elite data scientists and researchers actually rely on, unearthed from the ground up and run live in your browser.
It takes 2 minutes. No account, nothing to install.
Knowing which button to press is not enough
The only way to really understand a method is to run it, break it, and watch what it does to real data
Pick a learning track
Six roadmaps, 674 lessons, seven certificates. The first section of every track is free.
Data Scientist
From your first model to the methods senior data scientists are hired for. Gradient boosting, conformal prediction, causal inference, LLMs.
- 01Regression, done properly
- 02Trees and gradient boosting
- 03Feature engineering and selection
- 04Calibrated and conformal prediction
- 05Causal inference for decisions
- 06Bayesian and hierarchical modelling
- 07Deep learning and NLP
- 08LLMs and generative AI
Data Analyst
From a raw file to an analysis you can defend, with the 120-chapter Statistics Handbook alongside.
Researcher
The statistics reviewers actually check: mixed models, Bayesian analysis, meta-analysis.
Forecaster
Prediction intervals you can stand behind: ARIMA, the Kalman filter, GARCH.
R Developer
Scripts into software people depend on: Rcpp, packages, Shiny.
ML Engineer
A model in a notebook to monitored production: serving, drift, RAG and agents.
Meet the hint ladder, a mentor that asks instead of tells
Every graded exercise comes with hints that nudge you toward the idea, never the answer. Reply to any daily lesson and Akshay answers in person.
Help! My average of the survey scores comes back as NA. None of the numbers look wrong.
Hint 1 of 3Look again at the fourth score. What do you think mean() should do when one value is unknown: guess, or refuse? R refuses. Which argument tells it to drop the gaps before averaging?
Join learners from 40 universities and research institutes
600 learners have signed up since launch: people who need the statistics to be right, at work and in print
The best free R stats site I've found. Clear explanations, live examples, and zero clutter. Absolute gold for learners.
This site helped me pass my technical interview. Practical R examples gave me the confidence to use statistics at work.
Every tutorial includes reproducible code and real datasets. r-statistics taught me regression better than my grad course.
It takes 2 minutes. No account, nothing to install.
People who understand the statistics earn well over $100,000 a year in the US
Estimated total pay in the United States, base plus bonus and equity, across all experience levels. Glassdoor, 2026.
These figures include entry-level roles; the top quarter of data scientists clear $200,000 and credentialed actuaries routinely earn far more. The Bureau of Labor Statistics projects data scientist employment to grow 35 percent between 2025 and 2035, among the fastest of any occupation.
Some people look at the job data and assume analytics means spreadsheets and dashboards. On the contrary, the demand is for people who can say what a number means, how sure we are, and what would change their mind. Most of these roles do not require a specific degree, but you do need to know your stuff.
It takes 2 minutes. No account, nothing to install.
The library
Over 1,800 free tutorials, exercises, handbooks and tools, all runnable, all yours whether or not you ever pay
How do I learn statistics and ML?
Pick one tool and spend most of your time running methods on real data, not watching videos. Follow a structured path, build real projects along the way, and practice daily.
- 01Pick one tool and stick with it.R is a great first choice for this: the statistics are first-class, and every method you will ever read about ships with an implementation.
- 02Follow a structured path.Random tutorials leave gaps. A linear roadmap removes the "what should I learn next?" problem entirely.
- 03Run and learn something every day.Understanding is a skill that takes real time. Daily practice beats occasional binge-sessions.
- 04Work on real data.Real data has gaps, outliers and surprises. Projects force you to handle them, and they become the portfolio that gets you interviews.
- 05Get unstuck fast.Everyone gets stuck. Lean on a hint ladder that asks questions, and a human who answers when you reply.
- 06Go deep on one specialty.Once you have the foundations, specialise. Bayesian methods, causal inference, time series, machine learning. Pick one and go deep.
Want the full step-by-step roadmap? Read our free guide, What Statistics Is For.
It takes 2 minutes. No account, nothing to install.
Frequently asked questions
Got questions? We've got answers
Can I sign up and try r-statistics.co for free?
Yes. It is free to create an account and start learning. The first section of every track is free, with graded exercises, and so are all 1,800 tutorials, the three handbooks and the tools.
How do I teach myself statistics?
Run the methods on real data, in order, every day. The tracks here are that order. Each page teaches one idea, runs the code in front of you, and then checks that you can do it yourself.
Can I learn statistics and ML on my own?
Yes, and most people here do. What you need is a structured path so you never wonder what comes next, feedback when you are wrong, and someone to ask when you are stuck. All three are built in.
How long does it take?
About three months per track at three hours a week. The daily five-minute email lesson keeps the thread between sessions.
Can I learn for free?
Yes. The first section of every track, the whole tutorial library, the handbooks and the tools stay free. Pro unlocks the rest of all six tracks (about 630 lessons), every certificate, and the portfolio projects.
Hasn't AI made learning statistics obsolete?
AI can write the code. It cannot tell you whether the answer is right, whether the test fits the question, or whether the model is lying to you. That judgement is the job, and it is what these tracks teach.
Should I include the projects in my resume and portfolio?
Yes. Each track ends in a graded portfolio project on real data, and each certificate has a public verification page you can send to an employer.
What is r-statistics.co? A bootcamp? An online school?
An online learning platform for statistics and machine learning, taught through R. Interactive lessons, graded practice, and certificates, with a free library that has been around since 2015.
What makes r-statistics.co different?
Every method is explained from the ground up and run live in your browser, with the statistics underneath the model rather than just the buttons to press. Progress is graded, not watched.
See how it feels in two minutes.
Two real lessons from a learning track, graded, in your browser. No account, nothing to install.
It takes 2 minutes. No account, nothing to install.
Statistics and machine learning, taught from the ground up and run live in your browser. Free tutorials since 2015.