Objectives of Programme's Learning Outcomes

• 1. Pharmaceutical expertise: Use a foundation of concepts and knowledge in health sciences and pharmaceutical sciences.
• Understand and apply mathematical translations of large models and chemical and/or biological phenomena through abstract reasoning
• Understand and use different graphical representations of numerical values and their relationships
• 2. Scientific method: Solve pharmaceutical problems using their knowledge and critical mind.
• Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, the rule of three, syllogism, analogy, Boolean logic, etc.)
• Understand and use statistical methods
• Develop a critical, practical and analytical mind
• Manage doubt and uncertainty
• 3. Communication: Communicate in an appropriate, efficient, rigorous and respectful way in a professional perspective.
• Use a rich vocabulary linking concepts and words accurately
• Share knowledge and information
• 5. Quality: Assess themself, expand their knowledge and adapt their attitude.
• Research, read, interpret, and critique a scientific paper with sufficient knowledge of scientific English

Learning Outcomes of UE

Learning outcomes include :
- Understanding the most frequent tools used in descriptive statistics and how to use them practically;
- Interpreting graphical outputs such as box plots;
- Understanding basic principles of correlation (Pearson, Spearman) and linear regression;
- Being able to compute some basic probabilities (normal distribution and discrete distribitions);
- Understanding the basis of inferential statistics, which is a fundamental prerequisite before taking more advanced statistical courses.

UE Content: description and pedagogical relevance

Definition and aims of biostatistics. Variability in medicine. Types of variables. Univariate descriptive statistics (localisation and dispersion parameters) and graphical output. Bivariate descriptive statistics (linear correlation and simple linear regression). Frequency and probability of event. Elements of combinatorics. Conditional probability and Bayes theorem. Random variable (discrete and continuous). Probability density function. Central limit theorem. Binomial and Poisson laws. Normal and Z law. Use of statistical tables.

Prior Experience

Not applicable