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 Formation Polars - Analyser des données massives rapidement training are: Maîtrise de Python intermédiaire, notions de Pandas et bases en manipulation de données. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Polars - Analyser des données massives rapidement training is available in présentiel. The format can be adapted to your needs (inter, intra, custom).

Formation Polars - Analyser des données massives... | 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 Formation Polars - Analyser des données massives rapidement 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 Formation Polars - Analyser des données massives rapidement 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 Formation Polars - Analyser des données massives rapidement 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 objectives

Maîtriser les DataFrames Polars pour des analyses professionnelles ultra-performantes

Développer des pipelines de données scalables avec expressions Polars

Optimiser les traitements sur datasets volumineux en entreprise

Implémenter joins avancés, groupby et window functions certifiants

Configurer l'évaluation lazy pour des workflows data efficaces

Intégrer Polars dans des environnements production data science

Training program

Module 1Fondamentaux Polars : installation et manipulation DataFrames (Python, pl.DataFrame, I/O Parquet)

Installation rapide de Polars dans un environnement Python professionnel via pip et conda, création de DataFrames à partir de fichiers CSV, JSON et Parquet massifs, exploration interactive avec head, describe et schema, application de filtres, sélections et mutations via expressions fluides, exercices pratiques sur datasets e-commerce réels pour nettoyer et transformer des millions de lignes, production d'un premier rapport analysé avec code review par le formateur.

Module 2Opérations avancées Polars : joins, agrégations et lazy evaluation (expressions, scan_parquet)

Plongée dans les joins multi-types sur grands volumes pour fusionner datasets clients et transactions, maîtrise des groupby dynamiques et agrégations personnalisées avec over et partition_by, activation de l'évaluation lazy pour optimiser la mémoire sur téraoctets de données, mise en œuvre de window functions pour rankings et running totals, cas concrets d'entreprise comme analyse cohortes utilisateurs, exercices collaboratifs en Jupyter pour benchmarker performances contre Pandas, génération de visualisations intégrées Matplotlib.

Module 3Pipelines Polars : streaming, intégration ML et déploiement (streaming, Polars avec scikit-learn, export)

Conception de pipelines ETL complets en mode streaming pour données infinies en temps réel, intégration Polars avec scikit-learn et PyTorch pour feature engineering en MLops, optimisation avancée avec partitioning et predicate pushdown, tests de scalabilité sur clusters via Ray ou Dask, déploiement d'un workflow production-ready avec Docker et API FastAPI, projet fil rouge sur dataset bancaire réel pour prédire churn, remise de notebooks certifiants et plan d'implémentation en entreprise.