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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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30 free minutes with a training advisor — no commitment.
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Professional Training training in New York in September 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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Don't let this gap widen
Sans maîtrise avancée de Pandas, vos analyses de données prennent 3 fois plus de temps, avec 40% d'erreurs manuelles sur datasets complexes coûtant jusqu'à 50 000€ par projet en retards.
Les entreprises négligeant ces compétences perdent 25% de ROI sur leurs initiatives data, selon Gartner 2025, exposant à des décisions business erronées et des pertes compétitives massives.
En 2026, 68% des data jobs exigent Pandas expert pour scaler, risquant stagnation carrière ou éviction équipe.
Maîtrisez-le maintenant pour éviter ces pièges coûteux.
The Formation Pandas - Analyser des datasets massifs en entreprise 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.
To ensure the quality of the Formation Pandas - Analyser des datasets massifs en entreprise training, Learni provides the following 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.
The assessment of skills acquired during the Formation Pandas - Analyser des datasets massifs en entreprise training is carried out through:
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.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Plongez dans les techniques expertes de Pandas pour traiter des datasets volumineux, en pratiquant des merges complexes sur cas réels d'entreprise, des groupby multicritères avec agrégations personnalisées, des pivots dynamiques pour visualiser tendances, et des apply vectorisés pour accélérer les calculs, le tout via exercices interactifs sur Jupyter, production d'un rapport d'analyse intermédiaire prêt à intégrer dans vos workflows professionnels, avec debug et optimisation en direct pour des gains de performance immédiats.
Construisez des pipelines data end-to-end avec Pandas avancé, en intégrant Dask pour scaler sur big data, réalisant du feature engineering pour modèles ML sur datasets réels, automatisant le cleaning et l'enrichissement via custom functions, testant la robustesse sur scénarios d'entreprise critiques, déployant des scripts schedulés pour reporting quotidien, et finalisant un projet fil rouge livrable avec documentation complète, boostant vos compétences pour des analyses prédictives ultra-efficaces en production.
Target audience
Data scientists, analystes de données, ingénieurs data pour une montée en compétences professionnelle
Prerequisites
Maîtrise de Python avancée, utilisation courante de Pandas (DataFrames, Series, bases d'analyse)
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