Complex Data Analysis
Code: 104399 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| 2503740 Computational Mathematics and Data Analytics | OB | 2 |
Contact
- Name:
- Amanda Fernandez Fontelo
- Email:
- amanda.fernandez@uab.cat
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
It is recommended that students have some knowledge of probability and statistical inference, as well as some knowledge of the R software.
Objectives and Contextualisation
The main objective is to provide statistical tools for data analysis, mastering the most relevant techniques to cope with complex models.
Learning Outcomes
- CM14 (Competence) Implement strategies to confirm or refute hypotheses.
- CM15 (Competence) Manage the information for validation through statistical processing.
- CM15 (Competence) Manage the information for validation through statistical processing.
- CM16 (Competence) Assess, using the data obtained, inequalities on the grounds of sex/gender.
- KM12 (Knowledge) Identify statistical inference as a tool for forecasting and prediction.
- KM12 (Knowledge) Identify statistical inference as a tool for forecasting and prediction.
- KM12 (Knowledge) Identify statistical inference as a tool for forecasting and prediction.
- KM12 (Knowledge) Identify statistical inference as a tool for forecasting and prediction.
- KM14 (Knowledge) Identify the usefulness of Bayesian methods, applying them appropriately.
- KM14 (Knowledge) Identify the usefulness of Bayesian methods, applying them appropriately.
- SM14 (Skill) Use the properties of density and distribution functions.
- SM14 (Skill) Use the properties of density and distribution functions.
- SM15 (Skill) Use suitable statistical software to manage databases, to obtain summary indices of the study variables and to analyse data using inference techniques.
- SM15 (Skill) Use suitable statistical software to manage databases, to obtain summary indices of the study variables and to analyse data using inference techniques.
- SM15 (Skill) Use suitable statistical software to manage databases, to obtain summary indices of the study variables and to analyse data using inference techniques.
Content
Topic 1- Linear models: multiple regression and ANOVA.
Topic 2- Generalized linear models: logistic and Poisson regression.
Topic 3- Regularization: Lasso and Ridge regressions.
Topic 4- Big data in linear and generalized linear models.
Topic 5- Resampling methods: Permutation tests and bootstrap.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Theoretical lectures | 24 | 0.96 | |
| Workshop of exercises | 20 | 0.8 | |
| Type: Supervised | |||
| Practical sessions | 20 | 0.8 | |
| Type: Autonomous | |||
| Personal working | 61 | 2.44 |
Following the objectives of the course, its development will be based on the following activities
- Theoretical lectures: The student acquires scientific and technical knowledge of the subject by supporting the theoretical lectures and complementing them with personal work on the topics introduced. Theoretical lectures require less interactivity: they are conceived as a unidirectional method of transmitting knowledge from the teacher to the student. The lectures are given with the help of slides in English, which are also uploaded on the Virtual Campus.
- Problems and exercises: Sessions of exercises and practical sessions have a double mission. On the one hand, the students will work with the scientific and technical issues explained in the theoretical lectures to complete their understanding, developing a number of activities, from the typical solution of problems to the discussion of practical cases. On the other hand, the exercise sessions are a natural forum to 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.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Exercises | 30 | 20 | 0.8 | CM14, CM15, CM16, KM12, KM14, SM14, SM15 |
| Partial exam 1 | 35 | 2.5 | 0.1 | CM15, KM12, SM14 |
| Partial exam 2 | 35 | 2.5 | 0.1 | CM15, KM12, SM14 |
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.
Bibliography
- 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.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Hastie T., Tibshirani, R., Friedman, J. 2009.
- 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.
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Software
We'll utilize the R programming language.
Language list
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLAB) Practical laboratories | 1 | Catalan/Spanish | second semester | morning-mixed |
| (SEM) Seminars | 1 | Catalan/Spanish | second semester | morning-mixed |
| (TE) Theory | 1 | Catalan/Spanish | second semester | morning-mixed |