Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training R Expert - Master Advanced Predictive Analytics training are: Mastery of intermediate R, functional programming, multivariate statistics, handling large datasets.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training R Expert - Master Advanced Predictive Analytics training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training R Expert - Master Advanced Predictive A... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training R Expert - Master Advanced Predictive Analytics training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training R Expert - Master Advanced Predictive Analytics training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training R Expert - Master Advanced Predictive Analytics training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Master tidymodels packages for automated ML pipelines

Optimize R code for big data processing with data.table

Develop advanced interactive visualizations with ggplot2 and plotly

Perform robust predictive analyses with caret and mlr3

Automate data science workflows with targets and renv

Deploy R models in production with plumber and Docker

Manage complex errors and profile performance

Training program

Module 1Advanced Data Manipulation: dplyr, tidyr, data.table (tools, pipelines, processed datasets)

Dive into expert refactoring of massive datasets with dplyr and tidyr to clean real enterprise data, chain complex mutations, pivot tables through practical Kaggle exercises, switch to data.table to accelerate joins on millions of rows, build modular pipelines, test live in RStudio, and generate optimized CSV deliverables ready for analysis.

Module 2Predictive Modeling: tidymodels, caret (models, tuning, cross-validation)

Build powerful ML models with tidymodels and caret on real cases like customer churn prediction, tune hyperparameters via automated grid search, validate with k-fold cross-validation on real datasets, integrate advanced feature engineering, compare ROC-AUC performance in collaborative exercises, debug overfitting in real-time with RStudio, and export serialized models for immediate reuse.

Module 3Visualization and Deployment: ggplot2, plumber (dashboards, APIs, optimization)

Create stunning interactive dashboards with ggplot2 and plotly on multidimensional data, automate via Shiny apps in group exercises, deploy REST APIs with plumber for production, optimize code with profvis on real benchmarks, integrate Docker for scalability, test end-to-end on simulated client projects, and finalize with a personal portfolio of deployable scripts.