Degree | Type | Year |
---|---|---|
Applied Statistics | OP | 4 |
You can view this information at the end of this document.
Basic knowledge of the English language, as a large part of the articles, tutorials, and software packages are written in English.
It is recommended to have taken the Bioinformatics course or have equivalent knowledge of:
MODULE 1. Big Data in Drug Discovery
MODULE 2. Big Data in Omics Data Analysis
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Practical sessions | 21 | 0.84 | |
Presentation of Research Project | 3 | 0.12 | |
Theory classes | 21 | 0.84 | |
Type: Supervised | |||
Tutoring | 10 | 0.4 | |
Type: Autonomous | |||
Preparation of Research Project | 20 | 0.8 | |
Study | 70 | 2.8 |
The course is organized in sessions of 3 hours. Each session consists of a theoretical part (theory classroom) that will introduce the new concepts followed by a practical part (computer room) where the students will work on the implementation of concepts explained in the theoretical part. In each session the teacher will indicate the students some tasks to do autonomously, such as reading articles, resolution of class exercises or sending reports. The material used by the teachers will be available on the Virtual Campus of the course.
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 |
---|---|---|---|---|
Practicum Reports Preparation | 30 | 0.5 | 0.02 | CM14, KM17, KM18, SM16, SM18, SM19 |
Presentation class exercises | 30 | 0.5 | 0.02 | CM14, KM17, KM18, SM18, SM19 |
Presentation Research Project | 20 | 2 | 0.08 | KM18, SM16, SM17, SM18, SM19 |
Theoretical-Practical Exam | 20 | 2 | 0.08 | CM14, KM17, KM18, SM19 |
BLOCK 1. Big Data in Drug Design (50%):
BLOCK 2. Big Data in Omics Data Analysis (50%):
The minimum overall grade required to pass the course will be 5 points. To calculate the average, the minimum grade for each of the assessable activities must be equal to or greater than 3,5 points.
In order to be eligible for the resit, students must have previously been assessed in a set of activities whose weight is equivalent to at least two-thirds of the total grade for the course. Students who have failed or not submitted one or more of the assessments may take the resit exam corresponding to the failed block. If the established threshold is not reached in any of the blocks during the resit, the final course grade will be the minimum of the block grades.
This course does not allow for the single assessment system.
R: https://www.r-project.org/
Rstudio: https://www.rstudio.com/
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 |
---|---|---|---|---|
(PLAB) Practical laboratories | 1 | Catalan | second semester | afternoon |
(TE) Theory | 1 | Catalan | second semester | afternoon |