Objectives of Programme's Learning Outcomes

• Understand, describe, analyse and prioritise biological phenomena
• Understand and use different graphical representations of numerical values and their relationships
• Control the molecular, morphological and functional approaches of normal and pathological conditions
• Understand experimental protocols in the biomedical domain
• Integrate concepts from different approaches/disciplines in a complex biomedical problem
• Develop reasoning skills
• Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, the rule of three, syllogism, analogy, Boolean logic, etc.)
• Understand the statistical and/or epidemiological methods
• Work with efficiency / accuracy / precision
• Present a hypothesis and hypothetical-deductive reasoning
• Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
• Manage doubt and uncertainty
• Demonstrate developed interpersonal skills
• Summarise, explain, and argue
• Work in a team
• Share knowledge and information
• Submit reviews, reports and give oral presentations
• Manage resources
• Manage time
• Use basic IT and bibliographic resources.
• Manage their studies
• Locate scientific information efficiently
• Be open to research and demonstrate scientific curiosity

Learning Outcomes of UE

- Master the fundamental computational and statistical tools of data science, including data import, preprocessing and transformation, data visualization, and statistical inference methods.
- Present the results of analyses in a clear, rigorous, and reproducible manner in a scientific report.
- Analyze routine biological data effectively in a practical application context.
- Use reproducible environments and tools (e.g., R Markdown, Quarto, Git) to ensure the traceability and transparency of data analyses.

UE Content: description and pedagogical relevance

- Introduction to Software Tools: Familiarization with programming environments, version control systems, and reproducible scientific writing.

- Data Visualization: Construction of graphs suited to different data types — scatter plots, histograms, density curves, violin plots, bar charts, pie charts, and box plots — with an emphasis on graphical design principles.

- Data Preparation and Transformation: Data importation, variable management, manipulation of data tables, contingency tables, handling of multi-table datasets, instruction chaining, and introduction to querying databases.

- Fundamentals of Probability and Distributions: Introduction to basic probability concepts and commonly used probability distributions.

- Sampling and Estimation: Distinction between populations and samples, principles of statistical inference.

- Statistical Tests for Categorical Variables: Chi-square independence test, goodness-of-fit test, comparison of proportions, and evaluation of relationships between categorical variables.

- Hypothesis Testing for Means: Confidence intervals, parametric and non-parametric tests for comparing two groups.

- Analysis of Variance (ANOVA): Introduction to one-way and two-way ANOVA models.

Prior Experience

Basic computer usage skills, including file management and proficiency with common software tools.
This course assumes prior knowledge of the concepts covered in: 
- Statistics I (UM-B1-BIOMED-031-M)
- Statistics II (UM-B3-BIOMED-020-M)
- Introduction to Data Analysis in Research (UM-B2-BIOMED-033-M)