A professional credential issued by r-statistics.co and earned the hard way: you solve real exercises, then pass a code-based assessment that runs your R and grades it on whether it actually works. A line on a resume a hiring manager can click, check, and trust.
A real credential, with a live verification ID, an Open Badge, and a one-tap import to LinkedIn.
Every r-statistics.co credential is earned, not awarded. To hold one you work a track's exercise hubs to mastery, then pass a timed, code-based assessment: you write real R that an automated grader runs against a battery of correctness checks. It either works, or it does not.
That is what gives the credential its weight. It attests to demonstrated skill, performed under conditions that cannot be faked or guessed, and every credential carries a public verification page, so a recruiter or hiring manager can confirm it in a single click. It is the receipt for work already done.
One path, three gates, and the numbers are the real ones. The example below is the Tidyverse Practitioner track: 12 exercise hubs, 367 graded exercises, an 80% bar before the assessment unlocks.
Each track is a cluster of the exercise hubs already on this site. Solve at least 80% of a track's exercises across its hubs at your own pace. Anything you cracked earlier, even before you decided to chase the track, counts toward the bar.
Reaching the bar unlocks a timed assessment. You write real R that an automated grader runs against a battery of correctness checks, then scores on what the code actually does. There is no option to pick from a list, so the result reflects skill, not recognition.
Pass, and your credential mints with a unique ID like RST-2026-A7K3F9, a permanent public verification page, and an Open Badges 3.0 record signed by the r-statistics.co issuer that imports straight into LinkedIn.
The credentials build on one another, from the base every R user needs up to a data-science capstone. Each maps to a public, verifiable credential and states plainly what holding it proves you can do. The foundation credentials are free to earn; the specialist credentials and the capstone are part of Pro.
You can read and write idiomatic base R without reaching for a search bar: vectors and coercion, control flow, your own functions and their scope, and errors you can actually debug. The floor everything else here stands on.
Vectors, lists, data frames, subsetting, control flow, functions, the apply family, dates, strings, regex, functional programming, debugging.
You can take a messy table and make it answer a question: reshape it, join it, group and window over it, parse its dates and strings, and map a function down a list without a loop. The daily-driver skill most analyst jobs are actually asking for.
dplyr, tidyr, stringr, lubridate, purrr, readr, forcats, broom, joins, window functions, group-by.
You have internalised the grammar of graphics, not memorised chart recipes: you compose a plot from data, mapping and layers, then bend it with themes, scales and facets, and ship it interactive or on a map when the story needs it.
ggplot2 grammar, themes, facets, colour scales, bar charts, heatmaps, plotly, leaflet, gt tables.
You can choose the right test, fit a model, and check whether you were allowed to fit it: hypothesis testing, linear and logistic regression, ANOVA, categorical analysis, and the diagnostics that separate a defensible result from a lucky one.
Hypothesis testing, t-tests, linear & logistic regression, multiple regression, ANOVA, regression diagnostics, chi-square, nonparametric tests.
You can carry a problem from raw data to an evaluated model: build with tidymodels or caret, reach for trees and ensembles when they fit, cluster and reduce dimensions when they do not, and resample honestly so you never fool yourself about accuracy.
tidymodels, caret, decision trees, random forest, XGBoost, clustering, cluster analysis, PCA, cross-validation.
You can read the source of the packages you depend on, and write code worthy of being one: functions that compose, objects modelled in the right system, and an honest mental model of how R evaluates what you wrote. The track that turns an R user into an R developer.
Functional programming, S3 / S4 / R6 classes, environments & scoping, closures, the conditions system, debugging, parallelism, performance.
You can take a problem end to end in R: wrangle the raw data, see what matters in it, choose and defend the right model, and report a result that holds up. The one line that says you can do all of it, not just one slice.
Awarded automatically the moment you hold every foundation and specialist credential above.
A credential is only useful if other people can act on it. Yours lands in two places at once: on your LinkedIn profile as a real certification, and at a public URL anyone can open to confirm it, no account, no email to you, no waiting.
Every credential is issued as Open Badges 3.0, the 1EdTech standard for verifiable credentials. It is signed by the r-statistics.co issuer, carries the holder's score, and lives at a permanent public page. That is the difference between something a recruiter can trust and a JPEG anyone could fake.
A credential is only worth holding if it could have told you no.on why every assessment is code-graded, and can fail you
The certificate at the top of this page carries credential ID
RST-2026-A7K3F9.
It is loaded below, already confirmed: this is exactly what a recruiter sees. Verify it, or paste any RST ID,
and check the holder, track and score for yourself.
Anyone can check a credential without contacting us. The verification page is live and reflects the assessment score and issue date, so it says what the holder demonstrated, and when.
Every tutorial and exercise hub on this site is free. So are the two foundation credentials, forever. The three career-track credentials, the ones hiring managers weight most, are part of Pro. That is where the credential becomes a paid, professional signal, and where Pro earns its keep.
It depends on the track. The two foundation credentials, R Fundamentals and Tidyverse Practitioner, are free to earn, with the same public verification page and Open Badge as any other. The three career-track credentials, Data Visualization, Statistics and Machine Learning, are part of Pro. There is never a separate proctoring fee or exam voucher.
The foundations are free so anyone can see the credential is real and earn one at no cost. The career-track credentials, the ones hiring managers care most about, sit inside Pro. That keeps the highest-value signal behind real commitment, which is part of what makes it worth holding.
Pro is where the three career-track credentials live: Data Visualization, Statistics, and Machine Learning. It also carries the things around the work, progress that follows you across devices, your full attempt history, and a dashboard across every track. The tutorials and the two foundation credentials stay free.
It is code, not multiple choice. You write real R that an automated grader runs against a battery of correctness checks, then scores on what the code actually does. There is no option to pick from a list, so the result reflects skill rather than recognition.
You need at least 80% of a track's exercises solved across its hubs before the assessment unlocks. Anything you cracked earlier, even before you decided to chase the track, counts toward that 80%.
Each credential gets a unique ID like RST-2026-A7K3F9 and a public page anyone can load to confirm the holder, track, score and issue date. It is also emitted as Open Badges 3.0 JSON signed by the r-statistics.co issuer, so it imports into badge wallets and LinkedIn.
Yes. In rare cases of an integrity violation, a credential can be revoked, and its public page then shows that status. That possibility is part of why the credential is worth holding in the first place.
Pick a track, solve the work, and put your name on something that means it.
See the credentials