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 Maîtriser SciPy : Analyse et Calcul Scientifique Avancé en Python training are: Bonnes bases en Python et en mathématiques (algèbre linéaire, calcul différentiel et intégral). A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Maîtriser SciPy : Analyse et Calcul Scientifique Avancé en Python training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Maîtriser SciPy : Analyse et Calcul Scientifique... | 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.

Configure your training

Response within 24hNo commitment100% free

9 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Maîtriser SciPy : Analyse et Calcul Scientifique Avancé en Python 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 Maîtriser SciPy : Analyse et Calcul Scientifique Avancé en Python 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 Maîtriser SciPy : Analyse et Calcul Scientifique Avancé en Python 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

Comprendre l’architecture du module SciPy et ses principales sous-bibliothèques

Effectuer des calculs scientifiques avancés en Python (optimisation, intégration, interpolation, traitement du signal, statistiques)

Appliquer SciPy à des cas pratiques en ingénierie, data science et recherche scientifique

Automatiser et accélérer des tâches analytiques complexes

Interagir efficacement avec d'autres bibliothèques scientifiques de l’écosystème Python (numpy, matplotlib, pandas)

Training program

Module 1Introduction à SciPy et Fondamentaux du Calcul Numérique

Présentation de la bibliothèque SciPy et de son écosystème. Installation et environnement de travail. Structure du module SciPy. Manipulation de données avec Numpy pour une utilisation optimale de SciPy. Calcul numérique de base et algèbre linéaire élémentaire avec scipy.linalg et scipy.array.

Module 2Optimisation, Intégration et Interpolation

Introduction à l’optimisation numérique (minimisation de fonctions, recherche de racines) avec scipy.optimize. Intégration numérique (scipy.integrate) : méthodes, intégrales simples et doubles. Interpolations linéaire, splines et autres méthodes avec scipy.interpolate. Études de cas en data science et ingénierie.

Module 3Traitement du Signal, Statistiques et Applications Avancées

Traitement du signal (filtres, transformées de Fourier, filtrage numérique) avec scipy.signal. Statistiques et distributions de probabilité : scipy.stats, analyse de jeux de données, tests d’hypothèses, ajustement de lois. Résolution d’équations différentielles ordinaires (ODE) avec scipy.integrate. Cas pratiques métiers : analyse de données, recherche appliquée, automatisation de routines analytiques. Bonnes pratiques et optimisation des performances. Introduction à la visualisation avec matplotlib.