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1. Analyze univariate and bivariate data 2. Solve probability problems 3. Use random variables 4. Show and understand basic concepts in Estimation techniques 5. Be able to solve problems in Estimation 6. Be able to solve problems using the statistical software R. 1. Capacity for analysis and synthesis. 2. Knowledge of statistical software. 3. Resolution of problems. 4. Team working. 5. Critical reasoning. 6. Oral and written communication skills.

K1: Know the principles and values of democracy and sustainable development, in particular, respect for human rights and fundamental rights, gender equality and non-discrimination, the principles of universal accessibility and climate change. K3: Identify and analyze research methodologies and sources to develop academic work in the field of digital information management K7: Understand the fundamentals of statistics and quantitative analysis to interpret data, as well as the appropriate techniques for their collection and processing, understanding different structures, social contexts and user needs. S1: Plan and organize teamwork by making correct decisions based on available information and gathering data in digital environments. S6: Be able to collect, process, cleanse and aggregate data by understanding the needs of users and organizations and how they need them. S7: Experiment with data visualization tools to represent information intuitively, properly presenting the results to different types of audiences. S10: Apply statistical analysis techniques and metric studies to evaluate and measure the impact of data in digital environments. C1: Know and be able to manage interpersonal skills on initiative, responsibility, conflict resolution, negotiation, among others, which are required in the professional field. C2: Be able to apply knowledge in a professional way in solving specific digital information management problems using the tools and techniques learned in the academic field C3: Demonstrate ability in the development and execution of digital content projects autonomously working in multidisciplinary teams. C4: Capacity for continuous autonomous learning that facilitates adaptation to new situations and the updating of knowledge in the field of digital information.

1. Introduction. 1.1. Concepts and use of Statistics. 1.2. Statistical terms: populations, subpopulations, individuals and samples. 1.3. Types of variables. 2. Analysis of univariate data with R. 2.1. Representations and graphics of a qualitative variable: bar plots. 2.2. Representations and graphics of a quantitative variable: histograms, densities and box-plots. 2.3. Graphics for related observations in time and space: line graphics and map visualization. 2.4. Numerical summaries. 3. Analysis of bivariate data with R. 3.1. Association among quantitative variables: scatter plots and correlograms. 3.2. Association among qualitative variables: mosaic plots. 3.3 Association among qualitative and quantitative variables: box-plots. 3.4 Associations among statistical units and variables: heat maps. 3.4 Numerical summaries of associations: covariance and correlation. 4. Probability and probabilistic models. 4.1. Random experiments, sample space, elemental and composite events. 4.2. Properties of Probability. Conditional Probability and its properties. 4.3. Random variables and their characteristics. 4.4. Discrete probability models: Bernoulli variables and related distributions. 4.5. Continuous probability models: the normal distribution and related distributions. 4.6. Introduction to the bivariate normal distribution. 5. Introduction to Statistical Inference. 5.1. Parameter point estimation. 5.2. Goodness-of-fit to a probability distribution. Graphical methods. 5.3. The sample mean distribution. 5.4. Confidence interval for the mean. 5.5 Hypothesis testing on a mean: use and interpretation of a p-value.

14 Theoretical support materials available on the Web, and 14 sessions based on problem-solving sessions and practical computing tasks. No group tutorials except during the last week before the final exam.