This version of the course guide is provisional until the period for editing the new course guides ends.

2022/2023

Degree | Type | Year | Semester |
---|---|---|---|

2503740 Computational Mathematics and Data Analytics | OB | 2 | 2 |

- Name:
- Pere Puig Casado
- Email:
- pere.puig@uab.cat

- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No

- Dorota Mlynarczyk

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.

- Calculate and reproduce certain mathematical routines and processes with ease.
- Formulate hypotheses and think up strategies to confirm or refute them.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Use computer applications for statistical analysis, numerical and symbolic computation, graphic visualisation, optimisation and other to experiment and solve problems.
- Using criteria of quality, critically evaluate the work carried out.
- Work cooperatively in a multidisciplinary context assuming and respecting the role of the different members of the team.

- Analyse data using inference techniques for one or two samples.
- Choose the appropriate statistical software to analyse the data through inference techniques.
- Identify statistical inference as an instrument of prognosis and prediction.
- Identify the distinct sources of information available.
- Interpret obtained results and provide conclusions that refer to the experimental hypothesis.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Understabd the distinct methods of data collection.
- Use statistical software to manage databases.
- Use statistical software to obtain the summary indexes of study variables.
- Use the properties of distribution function.
- Use the properties of the density function.
- Using criteria of quality, critically evaluate the work carried out.
- Validate and manage information to carry out statistical processing on this.
- Work cooperatively in a multidisciplinary context, taking on and respecting the role of the distinct members in the team.

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 avaluation runs continuously along the course. The continued avaluation 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 avaluation: The practical exercises delivered by the students (30%), a partial examination of Theory in the middle of the course (35%), another partial examination of Theory at the end of the course (35%). The second-chance examination only will be alowed to the students having a minimum score of 3 at the final mark, recovering only the part corresponding to Theory.

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 |

- Introduction to Linear Regression Analysis. Montgomery, D. Peck, A. Vining, G., 2001.
- An R Companion to Linear Statistical Models. Christopher Hay-Jahans, 2012.
- Generalized Linear Models. McCullagh, P. and Nelder, J., 1992.
- Resampling methods: a practical guide to data Analysis. Phillip I. Good, 2006.
- The jackknife, the bootstrap and other resampling plans. Bradley Efron, 1982.
- Bootstrap methods and their application. A.C. Davison, D.V. Hinkley, 1997.

We'll utilize the R programming language.