FORMATION IA
Python pour Non-Ingénieurs : Données et IA en Pratique
Acquérez les bases Python pour interroger des APIs, manipuler des données et intégrer des LLMs sans background technique.
Ce qu'elle couvre
Ce programme pratique enseigne aux analystes, équipes opérationnelles et chefs de produit les bases de Python pour travailler efficacement avec les données et les outils d'IA. Les participants apprennent à utiliser les notebooks Jupyter, à manipuler des jeux de données avec pandas, à appeler des APIs REST et à créer des scripts simples exploitant les LLMs via les SDKs OpenAI ou Anthropic. Le cours est structuré autour de cas métier concrets — résumé de documents, extraction de données structurées et automatisation de tâches répétitives. Aucune expérience préalable en programmation n'est requise.
À l'issue, vous saurez
- Write Python scripts that load a CSV, filter rows with pandas, and export results to Excel
- Call the OpenAI or Anthropic API to summarise or classify a batch of text records from a spreadsheet
- Build a Jupyter notebook that combines data manipulation and LLM calls into a repeatable workflow
- Parse structured JSON responses from an LLM and insert them into a pandas DataFrame
- Identify when a task is better solved with Python than with a no-code tool, and scope it accordingly
Sujets abordés
- Python basics: variables, loops, functions, and error handling
- Jupyter notebooks for interactive, reproducible analysis
- pandas for loading, filtering, and transforming tabular data
- Calling REST APIs with the requests library
- OpenAI and Anthropic SDK usage: completions, chat, and embeddings
- Prompt construction and response parsing in Python
- Automating document summarisation and data extraction workflows
- Reading and writing CSV, JSON, and Excel files
Modalité
Delivered as four half-day sessions (online or in-person) spread over two weeks, allowing participants time to practise between sessions. Each session includes a short concept introduction (30%) followed by guided coding exercises on real datasets (70%). Participants work in pre-configured cloud Jupyter environments so no local setup is required. A Slack or Teams channel is opened for async Q&A between sessions. In-person delivery requires a laptop per participant and stable Wi-Fi.
Ce qui fait que ça marche
- Anchor every exercise to a real dataset or workflow the participant already owns
- Provide a cloud-based coding environment that removes local setup friction entirely
- Assign a small between-session mini-project that is reviewed at the start of the next session
- Follow up four weeks later with an optional office-hours session to unblock real projects
Erreurs fréquentes
- Jumping straight to LLM integrations before participants are comfortable with basic Python syntax and file I/O
- Using abstract programming exercises instead of datasets from participants' actual jobs, leading to low retention
- Skipping environment setup guidance, causing half the cohort to spend session one debugging installations
- Treating the programme as a one-off event without follow-up projects or peer accountability, so skills atrophy quickly
Quand NE PAS suivre cette formation
This training is not the right fit if the organisation already has a data engineering team that owns all Python tooling and business users are expected only to consume dashboards — in that case, a BI tool (e.g. Tableau, Power BI) literacy programme delivers more immediate value.
Fournisseurs à considérer
Sources
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