The course aims to provide students with the necessary knowledge and skills of the necessary tools to enable them to get familiarized with Data Science, as well as the implementation of analyses and predictions by using computational applications in applied problems of Transport.

Upon successful completion of the course the student will:

- Have understood and become familiar with one of the essential tools for data study and analysis, the open-source software R and R-studio.

- Gain specialized knowledge to solve basic problems using programming.

- Can integrate and analyze data available in external sources and various types of formats.

- To design research objectives and questions.

- Be able to apply hypothesis testing and critically evaluate results.

- Can apply and explain methods of regression, correlation of variables, methods of data clustering and classification, and basic methods of predicting data values.

- Perform teamwork to coordinate activities and manage progress in the delivery of a research project.

- Can interpret the results of analyses to generate research insights.

- Has the ability to synthesize, visualize and evaluate data and results through simple and complex graphs.

The open-source software R (introductory concepts, basic programming skills, applications of the language in data analysis), Descriptive Statistics (distributions, basic descriptive measures, hypothesis testing), Types of variables - Correlation of variables (Pearson's correlation coefficient, chi-square test), Visualization of data and results - Graphs, Types of regression (Linear, Logistic, etc.), In-depth data analysis - Prediction.

The evaluation of students is carried out through:

-A semester group project (60%) including a written report and a group presentation. It describes the intended research project in terms of objectives, analytical framework, and also summarizes the research project in terms of contribution to existing literature, results of the research findings and conclusions.

-An end-of-semester written examination (40%) that includes a demonstration of proficiency in a set of techniques.

The written examination will include:

- Multiple choice questions.

- Open questions.

-Suggested bibliography:

-Field A., Miles J., Field Z. (2012). Discovering Statistics Using R. SAGE Publications

-Wickham H., Grolemund, G. (2017). R for Data Science. O’Reilly

- Related academic journals:

-Journal of the Royal Statistical Society Series A, B, C

-Accident Analysis and Prevention

-Transportation Research part A, B, C, D, E

-Journal of Safety Research

-Traffic Injury Prevention

-Analytic Methods in Accident Research