Program profile · official sources
Machine Learning Scientist in R
The Machine Learning Scientist in R track by DataCamp is a self-paced track with 65 listed hours and sixteen courses. Learners access the curriculum through a Premium subscription, priced at USD 35 per month at the regular price checked on 8 September 2026. The program focuses on applying R programming to supervised and unsupervised machine learning workflows. Participants receive a Statement of Accomplishment upon completion. The curriculum covers classification, regression, feature engineering, clustering, dimensionality reduction, and Bayesian data analysis using tidymodels and other R packages. The provider includes two bonus items: a skill assessment and a project on predicting movie rental durations. The stated subscription price represents one month of access and does not guarantee a total completion cost.
Facts checked 9 Sep 2026
- Price
- DataCamp Premium — USD 35/month for course content, billed monthly at the regular price
- 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.
- 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.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
- Price
- DataCamp Premium — USD 35/month for course content, billed monthly at the regular price. (see provider page)
Premium for Individuals. English public pages with USD explicitly selected; visitor country not stated. Monthly regular access is USD 35 with no monthly promotion shown. Annual regular USD 330 and promotional USD 165 are separate billed totals; no promotion expiry or additional account/geographic restriction is stated. Access price is not a guarantee of track completion. · Official pricing - Duration
- 65 hoursProvider-listed track duration; not guaranteed elapsed completion time.
- Format
- Track
- Credential
- Statement of Accomplishment
- Prerequisites
- 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
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See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- Supervised Learning in R: Classification
- Supervised Learning in R: Regression
- Feature Engineering in R
- Unsupervised Learning in R
- Machine Learning in the Tidyverse
- Intermediate Regression in R
- Cluster Analysis in R
- Machine Learning with caret in R
- Modeling with tidymodels in R
- Machine Learning with Tree-Based Models in R
- Dimensionality Reduction in R
- Support Vector Machines in R
- Fundamentals of Bayesian Data Analysis in R
- Hyperparameter Tuning in R
- Bayesian Regression Modeling with rstanarm
- Introduction to Spark with sparklyr in R
Prerequisites: 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.
What are the prerequisites for the Machine Learning Scientist in R track?
DataCamp's beginner-focused FAQ says this track is not suitable for absolute beginners. It describes learners familiar with R programming and basic machine learning, and recommends a basic understanding of statistics, linear algebra, and calculus. This is useful starting-point guidance for a curriculum covering classification, regression, feature engineering, clustering, and Bayesian analysis. The track also serves people seeking deeper knowledge of machine learning and R, rather than only those pursuing a particular job title.
How much does the Machine Learning Scientist in R track cost?
The Machine Learning Scientist in R track requires a DataCamp Premium subscription. As checked on 8 September 2026, the regular price for the Premium subscription is USD 35 per month, billed on a monthly basis. This subscription grants access to the sixteen courses within the track, along with the bonus skill assessment and bonus project. The stated price represents the cost for one month of platform access. Because the program is self-paced and the official duration is estimated at 65 hours, the final amount depends on the subscription route and billing periods selected, not a guaranteed completion schedule.
What credential do learners earn upon completing the program?
Upon successfully finishing the sixteen courses in the Machine Learning Scientist in R track, learners earn a Statement of Accomplishment from DataCamp. This credential acknowledges track completion and can be added to a resume, CV, or LinkedIn profile. It is important to note that this Statement of Accomplishment represents program completion rather than a formal, independent professional certification. DataCamp does not state any specific mandatory certification exam, expiration date, or renewal requirement associated with this Statement of Accomplishment. The bonus skill assessment is listed separately; its presence does not establish a mandatory credential exam.
What you actually do
- Supervised Learning in R: Classification · Source
- Supervised Learning in R: Regression · Source
- Feature Engineering in R · Source
- Unsupervised Learning in R · Source
- Machine Learning in the Tidyverse · Source
- Intermediate Regression in R · Source
- Cluster Analysis in R · Source
- Machine Learning with caret in R · Source
- Modeling with tidymodels in R · Source
- Machine Learning with Tree-Based Models in R · Source
- Dimensionality Reduction in R · Source
- Support Vector Machines in R · Source
- Fundamentals of Bayesian Data Analysis in R · Source
- Hyperparameter Tuning in R · Source
- Bayesian Regression Modeling with rstanarm · Source
- Introduction to Spark with sparklyr in R — 4 hours · Source
What the credential is
- Credential type
- Statement of Accomplishment · www.datacamp.com / machine-learning-scientist-with-r
- Credential
- Statement of Accomplishment · www.datacamp.com / machine-learning-scientist-with-r
- Issuer
- DataCamp · www.datacamp.com / machine-learning-scientist-with-r
- Sharing
- Add this credential to your LinkedIn profile, resume, or CV; share it on social media and in your performance review. · www.datacamp.com / machine-learning-scientist-with-r
Who should skip it
- Absolute beginners seeking an introduction to R from scratch: the beginner FAQ describes prior R and machine-learning familiarity and recommends foundational mathematics. · Source
- Learners seeking a formal, independent professional certification requiring a proctored exam, as this track provides a Statement of Accomplishment upon course completion without stating an exam requirement. · Source
- Individuals looking for a program focused exclusively on natural language processing, as NLP is mentioned in the description but no specific NLP-labeled module is provided in the curriculum. · Source
Alternatives in the registry
Related by topic or level.
- Machine Learning Fundamentals in R — DataCamp Premium — USD 35/month for course content, billed monthly at the regular price; 24 hours.
- Supervised Machine Learning in R — DataCamp Premium — USD 35/month for course content, billed monthly at the regular price; 25 hours.
- AWS Certified AI Practitioner (AIF-C01) — USD 100 (One initial exam attempt; check applicable regional pricing and taxes. Optional preparation and retakes are not included.); 90 minutes (exam only; not preparation time).
Change log
- : Registry admission from previously reviewed official evidence; individual fact dates are preserved. · Source