Course: 2022/2023

Data Analysis Methods

(19014)

Specific competences:
E05 - Analyse questions related to science, technology and society by the means of basic and fundamental mathematical and statistical reasoning.
Transversal competences:
T01 - Make a critical use of digital tools and be able to interpret specific documental sources.
Learning outcomes:
1. To analyse data rigorously to extract information from them.
2. Use statistical distribution, regression to the mean and basic notions of statistical inference to specific problems.
3. Define and use models and programming languages for the resolution of basic statistical and probability problems.
4. Conduct estimation of order of magnitude and avoid fallacies and common mistakes in the use of numerical information and interpretation of scientific results (diagnostic tests, clinical trials, etc)
5. Collect and interpret data and information to base conclusions on, including, if necessary, a discussion on social, scientific or ethic issues.
6. Summarise the basis of management technologies and data analysis, as well as information representation tools.
7. Sound use of software to analyse, summarise and present quantitative information, in particular, by the use of graphics and infographics.

Skills and learning outcomes

Description of contents: programme

This course deals with basic ideas on Probability and Statistics, with the objetive of providing the necessary tools and concepts that allow analysing and managing quantitative information.
Contents:
· Introduction: data, information, knowledge.
· Where to find information: resources, research techniques, reliability.
· Numeric alphabetisation: percentages, magnitude orders, linearity and non linearity.
· Graphical information representation techniques and scientific visualization.
· Spreadsheets as tools for basic data management and representation.
· Correlation and causality. From data to theory
· Discrete correlation: the classification problem. Sensibility and specificity. Bayes theorem.
· Signal and noise: random phenomena. Binomial, normal and Poisson distributions.
· Continuous correlation: regression to the mean.
· Introduction to inferential statistics: surveys and clinical trials.
· Fundamentals of programming for data analysis.

Learning activities and methodology

Directed activities:
Practical classes - 16h - 0.64 ECTS - Learning outcomes: 4, 7, 5, 1
Theory classes - 33h - 1.32 ECTS - Learning outcomes: 4, 7, 5, 2, 1, 3, 6
Supervised activities:
Tutoring and project supervision - 4.25h - 0.17 ECTS - Learning outcomes: 7, 2, 1
Autonomous activities:
Study and project elaboration - 94.75h - 3.79 ECTS - Learning outcomes: 4, 7, 5, 2, 1, 3, 6

Assessment System

- % end-of-term-examination 50
- % of continuous assessment (assigments, laboratory, practicals...) 50

Basic Bibliography

- C. Criado Pérez. Invisible women. Exposing data bias in a world dessigned for men. Abrams Press.
- D. Huff. How to lie with statistics. W.W. Norton & Company.
- D. Peña y J. Romo. Introducción a la Estadística para las Ciencias Sociales. Mc Graw Hill.
- I. Portilla. Estadística descriptiva para comunicadores. Editorial EUNSA.

Additional Bibliography

- D. Rowntree. Statistics without tears. Penguin Books.
- G. Klass. Just plain data analysis (2nd ed.). Rowman & Littlefield.

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