Degree | Type | Year |
---|---|---|
Genetics | FB | 2 |
You can view this information at the end of this document.
Those required for admission to the degree program.
A basic understanding of mathematics is highly recommended for successful progress in this course.
Statistical tools are fundamental for research and analysis in Genetics and Genomics. In this Biostatistics course, we will not only understand and analyze experimental data but also learn how to communicate statistical results rigorously and effectively. This course introduces the basic concepts of statistics, data handling with R, and basic visualization techniques to clearly and effectively represent data.
The course objectives are:
Competencies
Learning Outcomes
The topics covered will include:
Additionally, parallel work will be done with R:
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Computer practicals | 12 | 0.48 | 2, 1, 5, 10, 9, 8, 11, 3, 12 |
Lecture classes | 30 | 1.2 | 4, 5, 6, 7, 9 |
Problems seminars | 11 | 0.44 | 2, 1, 5, 10, 9, 8, 11, 3 |
Type: Supervised | |||
Group mentoring sessions | 4 | 0.16 | 2, 1, 5, 10, 9 |
Type: Autonomous | |||
Practical work | 20 | 0.8 | 2, 1, 10, 11, 3, 12 |
Studying hours | 60 | 2.4 | 2, 1, 4, 5, 10, 6, 7, 9, 8, 11, 3, 12 |
The contents of the Biostatistics course are designed to provide students with a general introduction to basic statistical concepts, aiming to develop an understanding of statistical reasoning and the appropriate use of these tools in the design and analysis of experiments.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Practical exams | 15% | 1 | 0.04 | 8, 11, 12 |
Problem-based seminars | 15% | 7 | 0.28 | 1, 10, 11, 3 |
Written exams. First midterm | 30% | 2 | 0.08 | 2, 4, 5, 6, 7, 9, 3 |
Written exams. Second midterm | 40% | 3 | 0.12 | 2, 4, 5, 6, 7, 9, 3 |
Assessment
The competencies of this course will be assessed through continuous assessment, which includes written exams, practical exercises, and individual assignments.
The assessment system is divided into three modules, each with a specific weight in the final grade:
Theory
Assessment will consist of two midterm exams. The first midterm will account for 30% and the second for 40% of the final grade. A final resit exam will be available for students who did not pass one of the midterms, and it will carry the same weight as the respective missed midterm.
Problem-solving
This part will be assessed through the completion of short problem exercises in class.
Students are expected to solve and discuss these problems in front of their classmates. This component represents 15% of the final grade.
Practical sessions
This component will be assessed through a practical exam conducted in the computer lab in one-hour sessions for each group. In this session, students must use the appropriate statistical software, enter data from a study, propose an analysis, and respond to specific questions. This exam will account for 15% of the final grade.
The problem-solving and practical components will only be considered if the student has passed the theory exams.
The final grade is calculated by weighting each component (theory, problem-solving, and practical work). However, a minimum grade of 4.5 in the second midterm or in the resit exam is required in order to calculate the final average. Students who have passed the course via midterms and wish to improve their grade may take the resit exam for the theory section. In doing so, they forfeit the previous grade obtained in that section.
Students who have not participated in the continuous assessment or submitted the problem-solving exercises may be assessed through a final exam. In such cases, the final grade cannot exceed 70% of the maximum possible score.
To pass the course, students must achieve a final grade of 5 or higher, whether through the midterms or the resit exam, always considering the weighted contribution of the problem-solving and practical components.
Repeat students must complete all evaluation activities, including the submission of exercises and the practical exam.
Attendance at the practical sessions is mandatory.
To be eligible for the resit exam, students must have been previously assessed in activities that amount to at least two-thirds (67%) of the total course grade. Otherwise, the student will be marked as “Not Assessable.”
Single assessment
Single assessment will consist of one comprehensive exam covering theory (70%), computer-based practice (15%), and classroom-based practice (15%). This exam will count for 100% of the final grade.
The single assessment exam will take place on the same date as the second midterm of the continuous assessment, and its resit exam will coincide with the resit exam of the continuous assessment.
Books
The course will use the R software and the RStudio development environment, employing specific packages for statistical analysis and data visualization (such as ggplot2, dplyr, tidyr, among others). All the necessary software will be installed and available on the faculty's computers.
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PAUL) Classroom practices | 621 | Spanish | first semester | morning-mixed |
(PAUL) Classroom practices | 622 | Spanish | first semester | morning-mixed |
(PLAB) Practical laboratories | 621 | Spanish | first semester | morning-mixed |
(PLAB) Practical laboratories | 622 | Spanish | first semester | morning-mixed |
(PLAB) Practical laboratories | 623 | Spanish | first semester | morning-mixed |
(TE) Theory | 62 | Spanish | first semester | morning-mixed |