MICRO-CERTIFICATION | Designing AI Methods for Health Data

Next training session: June 7–11, 2027

• To train participants in the use of AI to analyze health data, equipping them with the technical (Python, machine learning, visualization) and regulatory skills needed to analyze, model, and leverage this data, while addressing the practical challenges facing the medical and healthtech sectors.

• Understand the structure and types of health data: identify file formats (XLS, CSV, text, and numeric data), and understand their organization and specific characteristics.
• Understanding regulations: applying the legal and ethical rules governing the use of health data (GDPR, anonymization, etc.).
• Using Python: Working with Core Libraries for Data Processing.
• Automate simple tasks: read, clean, and transform data using Python scripts. 
• Collect and prepare data: import files (xls, csv), handle missing data, and apply anonymization techniques.
• Visualizing Data: Create charts and tables to explore and present data.
• Apply descriptive statistics: calculate basic measures (mean, standard deviation, etc.) to summarize the data.
• Understanding basic algorithms: using methods such as kNN (k-nearest neighbors) and decision trees to segment or classify data.
• Interpreting the results: analyzing the outputs of the algorithms and drawing conclusions relevant to the healthcare field.
• Master advanced concepts: understand how neural networks, deep learning, and language models (LLMs) work.
• Processing text data: applying NLP (Natural Language Processing) techniques to analyze unstructured data (medical reports, etc.).
• Evaluating models: choosing appropriate metrics and optimizing algorithm performance.
• Solve a real-world problem: work as a team to develop an AI-based solution, from data collection to the presentation of results.
• Apply a project-based approach: structure an analysis, prioritize tasks, and communicate the results effectively.