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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.
10 spots per session maximum — 9 already taken
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30 free minutes with a training advisor — no commitment.
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Don't let this gap widen
Without mastery of IPython, data analysts and engineers waste 25-40 hours weekly on inefficient interactive exploration and debugging, crippling workflow efficiency.
This drags down productivity by 35%, costing mid-sized teams $200,000+ annually in delayed projects and rework.
Over 65% of data science incidents stem from suboptimal REPL environments, exposing companies to flawed analyses, regulatory fines up to $1M, and stalled career growth amid rising AI demands.
Every month without these skills escalates risks, transforming insights into expensive failures.
The Training: Mastering IPython for Productivity and Data Science: From Interactive Exploration to Advanced Analyses 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 Training: Mastering IPython for Productivity and Data Science: From Interactive Exploration to Advanced Analyses 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 Training: Mastering IPython for Productivity and Data Science: From Interactive Exploration to Advanced Analyses 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.
Discovery of IPython: history and concepts. Installation and launch. Tour of the interface and main features. Comparison with the standard interpreter. First steps: magic commands, autocompletion, integrated quick documentation. Hands-on exercises: exploration of functions, imports, evolving workflow.
In-depth exploration of magic commands: script management, timings, profiling, file and system management. Advanced use of autocompletion, dynamic object exploration, display, and rich display. Step-by-step bug resolution with the interactive environment. Productivity tips. Exercises and applications on analysis or script cases.
IPython in Jupyter: differences, synergies, best practices. Interactive data manipulation with pandas/numpy. Generation of interactive graphs (matplotlib, seaborn) in IPython and notebooks. Automation of analytical workflows. Export, saving histories, sharing notebooks, and reproducing analyses. Practical cases on real datasets.
Target audience
Python users, data analysts, engineers, developers, researchers, and educators who wish to harness the power of IPython to improve their efficiency and workflow in data science or data analysis.
Prerequisites
Basic knowledge of Python (data structures, loops, functions).
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