Specific competences: 1. Understand the basic concepts of population, sample, variable and statistic. 2. Know how to summarize a sample using measures of centre and variability. 3. Learn how to use statistical graphs to illustrate the main features of a sample. 4. Understand and implement the basic ideas of a regression analysis. 5. Learn how to estimate a population parameter based on sample data and how to formalize a hypothesis test. 6. Use of statistical software. Transversal competences: 1. Capacity of analysis and synthesis. 2. Understanding of how to use computer packages. 3. Problem solving. 4. Teamwork. 5. Critical reasoning. 6. Verbal and written communication.

K1: Acquire the knowledge, techniques and terminology specific to the field of economics, politics and international relations. K3: To know basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile. S3: Plan and organize team work making the right decisions based on available information and gathering data in digital environments. S4: Use information interpreting relevant data avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to assess the reliability and quality of such information. S7: Be able to identify, access and manage sources of information relevant to comparative analysis in the field of politics, economics and international relations. S10: Ability to gather and interpret relevant data and knowledge for the elaboration and defense of arguments on topics in their area of study. S11: Ability to discern which quantitative or qualitative research technique is the appropriate one to apply depending on the phenomenon being analyzed. C2: Know and be able to handle interpersonal skills on initiative, responsibility, conflict resolution, negotiation, etc., required in the professional environment. C3: Ability to establish good interpersonal communication and to work in multidisciplinary and international teams. C4: Be able to engage in lifelong autonomous learning, enabling them to adapt to new situations.

1. Introduction. 1.1. Concept and uses of statistics. 1.2. Statistical terminology. 1.3. Typos of variables. 2. Analysis of univariate data. 2.1. Representations and plots of qualitative data. 2.2. Representations and plots of quantitative data. 2.3. Numerical summary of a sample of data. 3. Analysis of bivariate data. 3.1. Representations and plots of qualitative and discrete data. 3.2. Representations and numerical summaries of quantitative data: correlation and regression. 3.3 Introduction to time series analysis. 4. Probability and probabilistic models. 4.1. Random experiments, sample space, elementary and composite events. 4.2. Properties of probability. 4.3. Conditional probability and its properties. 4.4. Random variables and their characteristics. 4.5. Bernoulli trials and related distributions. 4.6. The normal distribution. 4.7 Other distributions 5. Introduction to statistical inference. 5.1. Outline and objectives. 5.2. Point estimators. 5.3. Interval estimators. 5.4. Fundamentals of hypothesis testing. 5.5. Tests for normal means. 5.6. Tests for proportions. 5.7. Testing for independence.

Theory: Theory classes with materials available on the web. Prácticas: Problem classes. Computing classes using statistical software. Group tutorials for resolution of problems, doubts etc.