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Complex Data Analysis

Code: 104399
Credits: 6
2026/2027
Degree programme Type Course
Computational Mathematics and Data Analytics OB 2

Contact lecturer

Name :
Amanda Fernandez Fontelo
Email :
amanda.fernandez@uab.cat

Teaching staff

Alan Morte Piferrer

Group languages

You can consult this information at the end of the document.

Prerequisites

Students should have a basic understanding of probability and statistical inference, as well as familiarity with the R software.

Objectives

The course provides statistical tools for data analysis and ensures proficiency in the most relevant techniques for dealing with complex models.

Learning outcomes

  • CM14 (Implement strategies to confirm or refute hypotheses.) Implement strategies to confirm or refute hypotheses.
  • CM15 (Manage the information for validation through statistical processing.) Manage the information for validation through statistical processing.
  • CM16 (Assess, using the data obtained, inequalities on the grounds of sex/gender.) Assess, using the data obtained, inequalities on the grounds of sex/gender.
  • KM12 (Identify statistical inference as a tool for forecasting and prediction.) Identify statistical inference as a tool for forecasting and prediction.
  • KM14 (Identify the usefulness of Bayesian methods, applying them appropriately.) Identify the usefulness of Bayesian methods, applying them appropriately.
  • SM14 (Use the properties of density and distribution functions.) Use the properties of density and distribution functions.
  • SM15 (Use suitable statistical software to manage databases, to obtain summary indices of the study variables and to analyse data using inference techniques.) Use suitable statistical software to manage databases, to obtain summary indices of the study variables and to analyse data using inference techniques.

Contents

Topic 1- Linear models: multiple linear regression and ANOVA.
Topic 2- Generalized linear models: logistic and Poisson regressions. 
Topic 3- Big data in linear and generalized linear models.
Topic 4- Resampling methods: Bootstrap.
Topic 5 (subject to available time)- Regularization: Lasso and Ridge regressions.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Laboratory sessions (PLAB) 14 0.56 CM14, CM15, CM16, SM15
Monitoring sessions and office hours 4 0.16 CM14, CM15, CM16, KM12, SM14, SM15
Lecture sessions (TE) 27 1.08 CM14, CM15, CM16, KM12, SM14, SM15
Self-directed learning to deepen understanding of lecture topics 46 1.84 CM14, CM15, CM16, KM12, SM14
Resolution of certain problems during face-to-face sessions (SEM) 8 0.32 CM14, CM15, CM16, KM12, SM14, SM15
Resolution of theory-based problems 28 1.12 CM14, CM15, CM16, KM12, SM14
Complete all laboratory practice tasks independently 16 0.64 CM14, CM15, CM16, KM12, SM14, SM15

The course will be developed based on the following activities, in line with its objectives:

  • Theory-type sessions (lectures): Students will acquire scientific and technical knowledge of the course topics by attending these lectures and complementing them with personal study of the topics introduced. These lectures require less interactivity as they are designed to transmit knowledge unidirectionally from professor to student. The lectures are delivered with the aid of slides in English, which are also uploaded to the course Moodle page. It is essential to complement these slides with a book from the recommended bibliography of the course.
  • Theory-related problems and laboratory sessions: Theory-related exercises and laboratory sessions serve two purposes. Firstly, students deepen their understanding of the scientific and technical issues introduced in the lectures by completing various activities, ranging from solving problems to discussing practical cases. Secondly, laboratory sessions provide an ideal setting for discussing the development of practical work. Although both the problem sets and laboratory practical exercises will be addressed during the corresponding in-person sessions, they must also be completed independently at home. The time available during problem-solving sessions and laboratory classes is limited and does not allow for all the necessary exercises and practical cases to be covered in full to successfully achieve the course objectives. Therefore, independent work on both problems and practical exercises is essential to ensure that the sessions are conducted as efficiently and effectively as possible.


The use of Artificial Intelligence (AI) technologies is permitted in this course as part of the completion of assignments, provided that the final submission reflects a substantial contribution from the student in terms of analysis, reasoning, and personal reflection. Students must clearly identify any parts of their work that have been generated or assisted by AI technologies, specify the tools used, and include a brief critical reflection on how these tools have influenced both the development process and the final outcome of the assignment. Failure to disclose the use of AI tools will be considered a breach of academic integrity and may result in a penalty on the assignment grade or, in more serious cases, disciplinary measures in accordance with the regulations of the Universitat Autònoma de Barcelona.

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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Bootstrap exam (BE) 15 2 0.08 CM14, CM15, CM16, KM12, KM14, SM14, SM15
Exam 2 (E2) 20 2 0.08 CM14, CM15, KM12, SM14
Evaluable in-person theory-based and practical problems (PP) 30 0 0 CM14, CM15, KM12, SM14, SM15
Exam 1 (E1) 35 3 0.12 CM14, CM15, KM12, SM14

See version in catalan

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.

 

Software

We will use the R programming language.

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 1 Catalan/Spanish second semester morning-mixed
(PLAB) Practical laboratories 1 Catalan/Spanish second semester morning-mixed
(SEM) Seminars 1 Catalan/Spanish second semester morning-mixed