Course Overview

This course will provide the students with the necessary basic tools
and skills for all the other courses of the Master in Public Health
Data Science program. It will ensure all students reach a similar
understanding across the three core domains (epidemiology, statistics
and medical informatics) and will be able to follow the other teaching
unit of the program.

Learning Achievement

Competence

Course prerequisites

- General statistical theory concepts
- Analysis / Getting Cleaning Data Model Statistical Inference
- Fundamentals of epidemiology

Grading Philosophy

Continuous assessment

- Group presentations (40%)
- Written exam (3h – 60%)
- Rules and protocol for failures/re-sits is clearly described

Course schedule

- Biostatistics: core principles such as experimental design,
probabilistic modelling and maximum likelihood estimation will be
covered.
- Medical Informatics: database design & management, notion of object
programming as well as Parallel programming, Hadoop & cloud computing,
reproducible science will be covered.
- Epidemiology: general tools and ideas, biais, causality principles,
Data Legislation as well as communicable & reproducible data science
will be covered. 

Course type

Lectures, practicals, readings, inversed classes and presentations

Online Course Requirement

Instructor

Other information

Part of the Public Health Data Science Master programDuration: 4 weeks (76H)

Language of instruction: English
Mode of delivery: Blended-learning (76 contact hours)

Site for Inquiry