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 Intermediate NumPy Training - Optimize Your Vectorized Calculations training are: Intermediate proficiency in Python, basic NumPy knowledge, familiarity with data structures.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Intermediate NumPy Training - Optimize Your Vectorized Calculations training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Intermediate NumPy Training - Optimize Your Vect... | 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 Intermediate NumPy Training - Optimize Your Vectorized Calculations 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 Intermediate NumPy Training - Optimize Your Vectorized Calculations 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 Intermediate NumPy Training - Optimize Your Vectorized Calculations 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

Master the creation and advanced manipulation of multidimensional arrays

Optimize performance through vectorization and broadcasting

Apply ufunc operations and linear algebra

Handle boolean indexing and fancy indexing

Integrate NumPy with Pandas and Matplotlib

Develop high-performance scripts for massive data analysis

Diagnose and fix memory leaks

Training program

Module 1Advanced NumPy Arrays: Creation, Shapes, and Attributes (ndarray tools, reshape methods)

Dive into expert manipulation of NumPy arrays, create complex ndarrays from real data, explore shape, dtype, and strides attributes through interactive exercises, transform CSV datasets into optimized matrices, produce your first vectorized scripts to accelerate processing, and validate your skills on concrete data loading cases.

Module 2Vectorized Operations: Arithmetic and Universal Functions (ufunc, broadcasting)

Accelerate your calculations by mastering NumPy ufuncs, add, multiply, and apply trigonometric functions to millions of data points without loops, discover broadcasting to automatically align dimensions, complete exercises on physical simulations, generate performance comparison charts, and integrate these techniques into your daily workflows for a 10x speed gain.

Module 3Advanced Indexing: Slicing, Fancy, and Boolean (advanced ndarray methods)

Become a pro at selective access with fancy and boolean indexing, extract complex subsets from large arrays without copying, mask outliers in real scientific analysis datasets, combine advanced slicing and views to save memory, test on concrete cases like image filtering, and optimize your queries for peak performance.

Module 4Linear Algebra and Statistics: linalg and random (matrix tools, distributions)

Leverage the linalg module to solve linear systems, compute eigenvalues and SVD on real matrices, simulate datasets with random to test robustness, apply correlations and weighted averages through statistical exercises, integrate into basic machine learning projects, measure precision and speed gains, and prepare production-ready deliverables.

Module 5Optimization and Integration: Profiling, Memory, Final Projects (memoryview, Cython intro)

Reach expert level by profiling your code with line_profiler, eliminate memory leaks using views and strides, integrate NumPy with Pandas for hybrid dataframes and Matplotlib for visualizations, develop a complete massive data analysis project, receive personalized feedback, export your optimized scripts, and leave with a concrete portfolio to boost your CV.