Ateneo - UPO

The course introduces the basic concepts of data management in the biomedical field, focusing on the use of Excel and R for handling, cleaning, and transforming datasets from experimental or observational studies. Students will learn how to import, explore, modify, and document databases, perform data quality checks, and produce simple descriptive reports. Practical examples will be based on real epidemiological and biological datasets.

Lecture materials (slides, exercises, sample datasets).

Provide basic knowledge and skills for biomedical data management, including data organization, cleaning, transformation, and documentation. Foster the ability to use digital tools (Excel and R) and understand best practices in data management according to FAIR principles.

To successfully follow the course, students should have a basic understanding of computer science.

Lectures, Classroom exercises

The Course material is available in the Moodle platform. Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-students-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The exam will consist of an individual data analysis project based on a database provided by the instructor. Students will work independently and submit their report by the exam date. The goal is to assess the ability to manage, clean, and analyze data using Excel and R, and to clearly document the procedures adopted.

Introduction to data management, data formats, importing/exporting in Excel and R, data cleaning, variable transformation, filters and pivot tables, introduction to R and RStudio, dataset handling, derived variable creation, descriptive analysis and basic plots, practical exercises with biomedical datasets.

Knowledge and understanding: understand the basic principles of data management and variable transformation. Applying knowledge and understanding: perform data cleaning and data transformation on biomedical datasets. Making judgements: assess data quality and select appropriate handling procedures. Communication skills: clearly present results and document analytical steps reproducibly. Learning skills: gain autonomy in using digital tools for managing complex databases.