Checking date: 15/07/2023

Course: 2023/2024

Statistics and Data Science I
Master in Computational Social Science (Plan: 472 - Estudio: 375)

Coordinating teacher: KAISER REMIRO, REGINA

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Introduction to Programming with R (19151) Basic Statistics (19152)
- Ability to test hypotheses using data and the most appropriate tools. - Ability to estimate linear regression models with cross-sectional data, as well as to understand and explain the statistical principles underlying the estimations. - Ability to interpret the parameters of a linear regression, obtain predictions and evaluate the goodness of fit.
Skills and learning outcomes
Description of contents: programme
1. Parametric and non parametric estimation 2. Advanced Inference 3. Introduction to advanced modelization 4. Empirical examples
Learning activities and methodology
Training Activities: - Theoretical-practical classes - Tutorials - Group work - Individual student work - Partial and final examinations Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Basic Bibliography
  • Agresti, Alan. . Statistical Methods for the Social Sciences, Global Edition.. Pearson International Content.. 2018
  • Fogarty, Brian J. . Quantitative Social Science Data with R.. SAGE publications. 2018
  • Privitera, Gregory J.. Essential Statistics for the Behavioral Sciences.. SAGE Publications. 2017

The course syllabus may change due academic events or other reasons.