• Description

This course is designed to provide the student with an integrated, in-depth but applied approach to multivariate data analysis. The course aims to provide students with a set of research tools that allow them to better analyze and understand the data from experiments where systems, networks or processes are analyzed and to satisfactorily explain the results obtained in scientific articles. Corresponding topics include, but are not limited to, the following: Multiple Regression, ANOVA, ANCOVA, Outlier Detection, Data Representation, Principal Component Analysis, Factor Analysis, Cluster Analysis.

• The subject within the syllabus as a whole

This is an optional course for those interested in the study of multivariate data.

• Professional fields to which it applies

The course provides students with the necessary foundations of a data analyst.

• Prior knowledge

It is advisable to have previous knowledge on linear algebra and programming. It is necessary to be able to read texts in English, although the book we follow is in Spanish.

• Information prior to enrolment

• Coordinating Professor: Dr. Agusti Solanas (http://smarttechresearch.com);
• Description: This course is designed to provide students with an integrated, in-depth but applied approach to multivariate data analysis. The course aims to provide students with a set of research tools that allow them to better analyze and understand the data from experiments where systems, networks or processes are analyzed and to satisfactorily explain the results obtained in scientific articles. Corresponding topics include, but are not limited to, the following: Multiple Regression, ANOVA, ANCOVA, Outlier Detection, Data Representation, Principal Component Analysis, Factor Analysis, Cluster Analysis.
• Requirements: Ability to read scientific texts in English. Basic programming knowledge.
• Material: Notes, scientific articles and books.

• Content

Refer to the weekly-based planning

• Support tools and learning resources

• Guidelines on assessment at the UOC

The assessment process is based on the student's personal work and presupposes authenticity of authorship and originality of the exercises completed.

Lack of authenticity of authorship or originality of assessment tests, copying or plagiarism, the fraudulent attempt to obtain a better academic result, collusion to copy or concealing or abetting copying, use of unauthorized material or devices during assessment, inter alia, are offences that may lead to serious academic or other sanctions.

Firstly, you will fail the course (D/0) if you commit any of these offences when completing activities defined as assessable in the course plan, including the final tests. Offences considered to be misconduct include, among others, the use of unauthorized material or devices during the tests, such as social media or internet search engines, or the copying of text from external sources (internet, class notes, books, articles, other students' essays or tests, etc.) without including the corresponding reference.

And secondly, the UOC's academic regulations state that any misconduct during assessment, in addition to leading to the student failing the course, may also lead to disciplinary procedures and sanctions.

• View the assessment model