Degree | Type | Year | Semester |
---|---|---|---|
2503740 Computational Mathematics and Data Analytics | OB | 2 | 2 |
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
It is recommended a good knowledge of the course Modelling and Inference and to have some fluency in the software R.
The main objective is to provide statistical tools for data analysis, mastering the most relevant techniques to cope with complex models.
1- Linear models: multiple regression and ANOVA.
2- Generalized linear Models: logistic and Poisson regression.
3- Resampling methods 1: permutation tests.
4- Resampling methods 2: bootstrap.
5- Resampling methods 3: jackknife.
If we have time, we will also include an introduction to Principal Component Analysis.
Accordingly with the aims of the subject, the development of the course will be based on the following activities:
Theoretical lectures: The student acquires the scientific and technic skills of the subject assisting to the theoretical lectures and complementing them with his/her personal work on the topics explained. The theoretical lectures are the activities demanding less interactiveness: they are conceived like a fundamentally unidirectional method of transmission of knowledge of the teacher to the student. The lectures will be given using a support of slides (PowerPoint) in English that will be uploaded also at the Virtual Campus.
Problems and practices: The workshop of exercises and practical sessions have a double mission. On the one hand the students will work with the scientifical and technical issues explained in the theoretical lectures to complete its understanding developing a variety of activities, since the typical resolution of problems until the discussion of practical cases. On the other hand, the workshop of exercise are the natural forum at which argue in common the development of the practical work.
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 | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Theoretical lectures | 24 | 0.96 | 14, 9, 3, 4, 5, 8, 6, 7, 16, 13, 12, 15 |
Workshop of exercises | 20 | 0.8 | 14, 9, 3, 4, 5, 8, 6, 7, 16, 13, 12 |
Type: Supervised | |||
Practical sessions | 20 | 0.8 | 1, 14, 2, 16, 10, 11, 15 |
Type: Autonomous | |||
Personal working | 61 | 2.44 | 14, 2, 3, 4, 5, 6, 7, 13, 12, 10, 11 |
The evaluation runs continuously along the course. The continued evaluation has several fundamental aims: To check the process of education and learning and to verify that the student has attained the corresponding skills of the course.
This is the method of evaluation: The practical exercises delivered by the students (30%), a partial examination of Theory in the middle of the course (35%), and another partial examination of Theory at the end of the course (35%). The second-chance examination only will be allowed to the students having a minimum score of 3 at the final mark, recovering only the part corresponding to the Theory.
The students who chose the single assessment modality must take a final test that will consist of an exam in which there may be questions of theory and problem-solving and a practice exam in front of the computer. This test will be carried out on the same day, time, and place in which the test of the second partial is carried out. Anyone who misses the test without a valid excuse will be classified as NOT EVALUABLE. If a grade of less than a 5 is received, it may be recovered on the same day, at the same time, and in the same location as the other students in the course.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exercises | 30 | 20 | 0.8 | 14, 2, 16, 10, 11 |
Partial exam 1 | 35 | 2.5 | 0.1 | 1, 9, 3, 4, 5, 8, 6, 7, 13, 12, 15 |
Partial exam 2 | 35 | 2.5 | 0.1 | 1, 9, 3, 4, 5, 8, 6, 7, 13, 12, 15 |
We'll utilize the R programming language.