ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Expository Class: 10 Interactive Classroom: 10 Total: 20
Use languages Spanish, Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Faculty of Nursing
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
-Understand the fundamentals of descriptive statistics and be able to select and apply the appropriate techniques for data analysis and presentation in healthcare contexts.
-Comprehend the principles of statistical inference, with applications in clinical and epidemiological studies.
-Develop the ability to interpret and critically evaluate statistical results from health sciences research, with particular attention to methodological validity and the relevance of conclusions.
-Acquire practical skills in the use of statistical software, applying it to the analysis of real data from biomedical and healthcare fields.
1. Review and extension of basic concepts and techniques in descriptive statistics and statistical inference.
2. Inference for comparing means and/or populations.
3. Inference for categorical variables.
4. Correlation and regression in quantitative variables.
5. Data analysis using statistical software.
BASIC
Armitage, P.; Berry, G.; & Sanz, F. (1997): Estadística para la investigación biomédica. Harcourt Brace.
Crujeiras, R.M. & Faraldo, P. (2010): Manual de estadística básica para ciencias de la salud, Unidixital.
Rius Díaz, F. & Wärnerberg Wärnerberg, J. (2014): Bioestadística. Paraninfo.
SUPPLEMENTARY
Altman, D.G. (1999): Practical Statistics for Medical Research. Chapman & Hall.
Glover, T. & Mitchell, K. (2015): An Introduction to Biostatistics using R, Waveland Press. [https://waveland.com/Glover-Mitchell/r-guide.pdf]
Martínez González, M.A. et al. (2020): Bioestadística amigable. Elsevier.
Milton, J.S. (2007): Estadística para Biología y Ciencias de la Salud. McGraw-Hill (3rd ed.).
Rosner, B. (2011): Fundamentals of Biostatistics. Duxbury Press.
C12 – Understand the basic elements of descriptive statistics and be able to apply suitable techniques for data analysis.
C13 – Understand the principles of statistical inference and regression.
C14 – Be able to use software tools to perform statistical analysis.
H12 – Apply basic statistical techniques, both descriptive and inferential, to the field of Health Sciences and Nursing.
H13 – Evaluate the importance of statistics as a tool for accessing scientific knowledge and critically assess research reports based on the rigor of the statistical methods used and the conclusions drawn.
Teaching will combine lecture-based sessions and practical classes aimed at developing statistical competencies applicable to the health field.
In theoretical sessions, core concepts will be explained using presentations and examples drawn from health sciences research. Active participation will be encouraged through guided questions and case discussions.
Practical sessions will take place in a computer lab, where students will be introduced to statistical software for real data analysis. Work will focus on clinical and epidemiological datasets, allowing students to contextualize statistical techniques in real-world scenarios.
Tutorials will provide individualized support, problem resolution, content reinforcement, and guidance for individual assignments or data analyses.
The final grade for the course will be based on a theoretical-practical final exam, accounting for 40% of the final mark, and continuous assessment, accounting for the remaining 60%.
The final exam will be conducted in person and will assess the understanding of statistical concepts covered in class, as well as the ability to apply them to real healthcare scenarios. It will include both theoretical questions and problem-solving exercises.
Continuous assessment will be based on the student’s work during practical and interactive sessions, including use of statistical software, participation in case analyses, and submission of relevant assignments.
It should be noted that, for cases of fraudulent performance of exercises or tests, the provisions of the "Rules for the evaluation of student academic performance and review of grades" will apply.
The time required to successfully complete this course depends on the student’s prior knowledge. For every hour of lecture-based teaching, about 1.5 hours of personal study is recommended. For practical sessions, an additional hour of personal work per class hour is estimated.
Attendance at lectures and practical sessions is essential for successful completion of the course. Students are encouraged to complete all activities recommended by the teaching staff, including problem-solving, literature review, and interpretation-focused exercises.
Course materials will be made available via the USC Virtual Campus, which will serve as the primary channel of communication with students.
Jose Ameijeiras Alonso
Coordinador/a- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813165
- jose.ameijeiras [at] usc.es
- Category
- PROFESOR/A PERMANENTE LABORAL
| Wednesday | |||
|---|---|---|---|
| 18:00-20:00 | Grupo /CLE_01 | Spanish | Classroom 2.03 |
| 01.12.2026 16:00-18:00 | Grupo /CLE_01 | Classroom 2.03 |
| 06.22.2026 16:00-18:00 | Grupo /CLE_01 | Classroom 2.02 |