Duration
5 days
Training structure
Continuing Education Department, Faculty of Science
Presentation
This course provides the fundamentals for collecting, processing, and analyzing health data, while addressing regulatory considerations (GDPR, anonymization) and practical tools (Python, data visualization). It introduces participants to the use of artificial intelligence methods (machine learning, deep learning) for analyzing medical data.
The advantages of the training program
A training session is planned for the current year:
- June 8–12, 2026
Objectives
- Train participants in the use of AI to analyze health data, equipping them with the technical (Python, machine learning, data visualization) and regulatory skills needed to analyze, model, and leverage this data, while addressing the practical challenges facing the medical and healthtech sectors.
Know-how and skills
- Understand the structure and types of health data: identify file formats (XLS, CSV, text, and numerical 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: creating 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 algorithms and drawing conclusions relevant to the healthcare field.
- Master advanced concepts: understand how neural networks, deep learning, and language models (LLMs) work.
- Processing textual data: applying NLP (Natural Language Processing) techniques to analyze unstructured data (medical reports, etc.).
- Evaluating models: selecting 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 presenting the results.
- Adopt a project-based approach: structure an analysis, prioritize tasks, and communicate results effectively.
Organization
Knowledge assessment
50% quizzes (4 quizzes: Days 1, 2, 3, 4)
50% practical work (hackathon, Day 5)
Program
This course consists of 30 hours of instruction spread over 5 days of in-person classes.
PROGRAM
Day 1 (6 hours): Data Management
- Health Data Management and Interpretation [3 hours]
- Introduction to Programming for Data Analysis with Python [3 hours]
Day 2 (6 hours): Collection/visualization of health data ( anonymization)
- Data manipulation: XLS files, CSV files, text data, numerical data, missing data, basic statistics
Day 3 (6 hours): Introduction to machine learning (unsupervised learning)
- kNN
- Decision trees
Day 4 (6 hours): Advanced Machine Learning (Supervised Learning)
- Deep Learning - Neurons
- LLM
- Text data
Day 5 (6 hours): Hands-on practice and collaboration – Hackathon
Admission
Registration procedures
To apply, please send your resume and cover letter to the following address: sfc-fds @ umontpellier.fr
Applications are accepted until the end of April.
Target audience
This training is designed for a diverse audience, including:
- Healthcare professionals (doctors, data managers, executives, etc.)
- Digital and data professionals (data analysts, healthtech project managers, consultants, etc.)
Tuition fees
Prices:
- Self-funding: €1,750
- Third-party funding: €2,450
Mandatory prerequisites
Bachelor's degree in Science or Health Sciences
And after
Professional integration
Career Opportunities
- Health Data Analyst
- Healthtech Project Coordinator
- Data Analyst specializing in healthcare
- Healthcare App Developer
- Healthcare Digital Transformation Consultant