Program profile · official sources
Machine Learning Scientist in R
An extended R learning track spanning supervised and unsupervised ML with Bayesian analysis.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
Who it is for
The R scientist track addresses prospective machine-learning, data-science, research and AI-engineering learners, as well as people deepening their R and machine-learning knowledge.
Who should skip it
Prepare R and basic ML familiarity if needed: FAQ expectations differ from the generic no-prerequisites panel, so do not rely on that panel alone.
Facts checked 9 Sep 2026
- Price
- DataCamp Premium · USD 35/month for course content, billed monthly at the regular price
- Premium for Individuals. English public pages with USD explicitly selected; visitor country not stated. Monthly regular access observed 2026-09-08 was USD 35. Annual regular access observed 2026-09-05 was USD 330, billed annually; a time-limited promotion was displayed on 5 September 2026. Access price is not a guarantee of track completion. ·
- Provider link
See price and enroll at DataCamp (official site) ↗
Official site. No commission.
- Time
- 65 hours
Provider-listed track duration; not guaranteed elapsed completion time.
- Level
- Not listed
- Credential
- Statement of Accomplishment
Add this credential to your LinkedIn profile, resume, or CV; share it on social media and in your performance review.
- Format
- Track
- Prerequisites
- See program details
The panel states no prerequisites. The beginner FAQ expects R programming and basic machine-learning concepts and recommends mathematics. Another FAQ says prior machine-learning knowledge is not necessary but says the track is best suited to basic R and machine-learning familiarity.
Sources ↗
Course and activity access
Career and Skill tracks: Basic not included; Premium included.. Official source
What you actually do
Sixteen courses cover classification, regression, features, clustering, dimensionality reduction and Bayesian data analysis using tidymodels and other R packages. A skill assessment and movie-rental-duration project are bonus items. The FAQs variously describe basic R/ML familiarity, recommend mathematics and say prior ML knowledge is unnecessary; those observations remain distinct rather than rewritten into one uniform entry rule.
The breadth of the R Scientist route is relevant when you want to connect multiple modelling approaches rather than learn one package in isolation. Bayesian analysis, dimensionality reduction and predictive methods raise different questions, so a long topic inventory should not be interpreted as equal depth in every speciality.
The preparation statements are not fully aligned. The panel says none, while FAQ wording expects or favours R and basic ML familiarity and recommends mathematics. Keep that disagreement visible when deciding whether the sequence fits your background; it does not justify inventing a formal admission barrier or promising a completely unprepared start.
The alternatives provide another way to judge scope. A focused supervised-learning route may suit a narrower objective, while a broader scientist track represents a different commitment. The name of the completion credential does not replace that comparison of practical content and expected preparation. Your own modelling objective can therefore guide the choice more clearly than comparing track titles alone. The R learning route can be compared with your existing analytical workflow. Familiarity with a language and familiarity with a modelling approach are different considerations when deciding which part of the sequence matters most.
Before you start
The panel states no prerequisites. The beginner FAQ expects R programming and basic machine-learning concepts and recommends mathematics. Another FAQ says prior machine-learning knowledge is not necessary but says the track is best suited to basic R and machine-learning familiarity.
Curriculum
The sixty-five-hour track estimate describes provider-listed content. Sixteen core courses and bonus work do not define a necessary number of paid months or an examination duration.
| Entry | Subject | Listed time | Share | Role | Observed |
|---|---|---|---|---|---|
| 1 | Supervised Learning in R: Classification | Not stated | Not calculated | 2026-09-09 | |
| 2 | Supervised Learning in R: Regression | Not stated | Not calculated | 2026-09-09 | |
| 3 | Feature Engineering in R | Not stated | Not calculated | 2026-09-09 | |
| 4 | Unsupervised Learning in R | Not stated | Not calculated | 2026-09-09 | |
| 5 | Machine Learning in the Tidyverse | Not stated | Not calculated | 2026-09-09 | |
| 6 | Intermediate Regression in R | Not stated | Not calculated | 2026-09-09 | |
| 7 | Cluster Analysis in R | Not stated | Not calculated | 2026-09-09 | |
| 8 | Machine Learning with caret in R | Not stated | Not calculated | 2026-09-09 | |
| 9 | Modeling with tidymodels in R | Not stated | Not calculated | 2026-09-09 | |
| 10 | Machine Learning with Tree-Based Models in R | Not stated | Not calculated | 2026-09-09 | |
| 11 | Dimensionality Reduction in R | Not stated | Not calculated | 2026-09-09 | |
| 12 | Support Vector Machines in R | Not stated | Not calculated | 2026-09-09 | |
| 13 | Fundamentals of Bayesian Data Analysis in R | Not stated | Not calculated | 2026-09-09 | |
| 14 | Hyperparameter Tuning in R | Not stated | Not calculated | 2026-09-09 | |
| 15 | Bayesian Regression Modeling with rstanarm | Not stated | Not calculated | 2026-09-09 | |
| 16 | Introduction to Spark with sparklyr in R | 4 hours | Not calculated | 2026-09-09 |
US price
| Purchase information | Recorded terms |
|---|---|
| Access and billing | For the R Scientist track, USD35 is the regular one-month Premium access quote dated 2026-09-08. The 65-hour course estimate and sixteen-course scope do not establish a necessary number of payments or an all-in completion total. |
| Refund and retakes | Refund and retake terms are not established in these stored policy fields. |
Alternatives
Supervised Machine Learning in R
| Provider and format | Level | Workload | Award | US price observation |
|---|---|---|---|---|
| DataCamp · Track | Beginner | 25 hours | Statement of Accomplishment | USD 35. Regular one month of Premium access; not guaranteed total cost to finish. |
| Entry and assessment questions | Recorded answer |
|---|---|
| Who is it aimed at? | This track is particularly beneficial to data scientists, machine learning engineers, and researchers, though any job that requires knowledge of supervised machine learning will benefit from this track. |
| What preparation is required? | There are no prerequisites for this track |
Official source · 2026-09-09 | Official source · 2026-09-08
Machine learning scientist
| Provider and format | Level | Workload | Award | US price observation |
|---|---|---|---|---|
| 365 Data Science · 100% online career track | Advanced | 29 hours of content (provider-listed) | Provider-issued certification | As shown in the United States on 23 October 2026, the 365 Data Science pricing page displays a Free plan at $0/month (course previews, projects, exercises and practice exams) and a Self-study plan at $29/month billed annually, shown against a $36/month reference price; a billed-monthly option is shown without a separate amount in the captured page. The FAQ states $29/month. A Lifetime plan shows no amount (Contact us). Accredited certificates are listed under Self-study, not Free. These are platform subscription quotes, not a fixed career-track completion total. |
| Entry and assessment questions | Recorded answer |
|---|---|
| Who is it aimed at? | Learners moving into machine learning scientist work, whether transitioning from another role or extending a data background; the provider labels the track advanced. |
| What preparation is required? | No formal prerequisite is stated on the track page. The provider labels the track advanced. |
| How is the award earned? | Finish the career track (track page); 365 states career-track certificates involve course and career-track exams and that certifications require 60% or above on the respective exam. |
| What remains unconfirmed? | Expiry, renewal and retake conditions are not stated. The track page does not itself describe the final exam; the exam condition comes from the 365 certificates page. Provider-listed accreditations are not treated as equivalence. |
Questions before enrolling
What course completion earns the R Scientist statement, and are renewal rules known?
Completion observation 2026-09-09: finish the sixteen R Scientist courses to receive DataCamp's Statement of Accomplishment. The credential can be shared on LinkedIn, a resume or CV, social media and in a performance review. A separate final exam, passing threshold, expiry and renewal are unstated.
Change log
- : Registry admission from previously reviewed official evidence; individual fact dates are preserved. · Source